Beeminder Blog

Beeminder ♥ BoardGameGeek

bsoule — Tue, 31 Mar 2026 00:00:00 GMT

BoardGameGeek is an online community for board and card game geeks. They have reviews of every board game imaginable, from Your Favorite Niche Game, to Candy Land, to Cribbage, as well as community forums, the rules, etc. It’s kind of a forum meets a wiki meets blog.

It’s a great resource for finding new games to try, or recalling that one game you used to play in Joe’s basement every Friday before the X-Files came on.

Once you create an account you can track your board game collection, write session reports, and log your plays. Which is where the Beeminder integration comes in. We’ll automatically track your plays of either a specific game, or all games.

To create a goal, head to beeminder.com/new, pick the BoardGameGeek icon from the list of integrations, and answer the questions about how much you want to commit to game playing. Or if that sounds too hard, here’s a button:

Board Game Geeks Click Meeeee

Um, I just wandered in from BGG; what is Beeminder?

Welcome aboard(game)! You might want to check out our getting started guide in our Help Docs, and then get yourself signed up. Assuming you’re on board with the commitment device and game for paying money if you go off track, the beauty of an autodata integration is that you don’t normally need to interact with Beeminder once you’ve gone through the process above of setting up your commitment. Just log things in BGG like normal and Beeminder alerts you if you need to log more game playing.

(See also the full documentation of the BoardGameGeek integration in our help docs.)

The Sheeple Strategy

dreev — Tue, 17 Mar 2026 00:00:00 GMT

This is pretty obvious and, by definition, is what most people already do, but we’ve found the concept handle useful. When we notice one of our concept handles has stuck, it’s handy to have a blog post to link to that defines it.

In this case, it’s a strategy for minimizing software headaches. Namely, to delay software upgrades (not counting security updates — always do those) long enough that you’re always on whatever version the majority of people are on. Safety in numbers! I use the heuristic of always being one version behind whatever the very latest is. Like for macOS, my laptop wants me to upgrade to Tahoe but I’m staying put on Sequoia. Until I hear about whatever Tahoe’s successor is. Then I’ll finally upgrade to Tahoe.

Maybe I’m being a bit extreme on that one. My friend Serine Molecule suggests a tempered sheeple strategy of merely waiting to upgrade to the next major macOS version until it gets its first point release. And of course a fully strict sheeple strategy would be to always upgrade to whatever has the most market share:

Turns out I’m some months overdue for upgrading to macOS Tahoe, if we believe this golem-generated market share chart.

Of course if everyone followed the sheeple strategy we’d have zero progress ever. So the more pro-social version is to diligently upgrade at least to the majority version and bonus points for taking one for the team sometimes and braving the bleeding edge. And realistically there are enough people doing that because they’re compelled to (as I sometimes am myself). So you can consider yourself to have done your part by following the most sheeply version of the sheeple strategy. At least you’re not a straggler.

Generalizations

If you’re a web developer you probably know about “Can I Use”. A prudent sheeple strategy is to use a fancy JavaScript feature when something like 90% of browsers in the wild support it. (The other half of the sheeple strategy here, as an end user, is to expect developers to do that and make sure you’re in that 90%.)

And to go weirdly far afield, there’s a classic Scott Alexander post I’m enamored with that advocates a sheeple strategy for language usage: “Give Up Seventy Percent Of The Way Through The Hyperstitious Slur Cascade”.

One more example, back in the realm of tech: The last time we had multiple hours of downtime (see status.beeminder.com for gory details) was due to a huge data center outside of New York City having some kind of meltdown. It’s plenty embarrassing that we’re not robust to such things but being located in a popular data center of a popular cloud provider is another sheeple strategy. In this case it had the advantage that enough of the rest of the internet was down that our users treated it as an act of God and, I guess, blamed us less. Again, not something to be proud of, exactly, but what did you expect with a name like “sheeple strategy”? Still, it is sometimes the unironically savvy strategy. And now you know what to call it, you’re welcome.

Related Concepts

Clive Freeman used to call this a fast follower strategy, which is a business term for the counterpart of the first mover. See “Second-mover advantage”.
Theo Spears points out the idea of innovation tokens, from the classic Dan McKinley essay “Choose Boring Technology”.
Also via Theo Spears, the technology adoption life cycle as popularized in Crossing the Chasm.

Gresham's Law of Beeminder Deadlines: Or Death to Deadline Snoozing

dreev — Tue, 03 Mar 2026 00:00:00 GMT

Deadline snoozing in Beeminder refers to scooching your deadline later as it’s bearing down. Traditionally we’ve allowed any deadline changes until six hours before the deadline. But we’ve always hated that. It’s arbitrary and hard to remember. We have a whole essay railing against such things — the Anti-Magic Principle, we call it.

Ok, now for a quick economics digression. Some of you may know Gresham’s Law. It’s generally stated as “bad money drives out good” but the idea generalizes. Another famous bit of economics theory is the “Market for Lemons”. Suppose people sell their cars on eBay or wherever, and some cars are so-called lemons that are constantly breaking down and other cars are the opposite, peaches. Key is that you can’t tell a lemon from a peach till after you’ve bought it and driven it for a while. So if you’re car shopping, you’d gladly pay more for a peach than a lemon but, since you can’t be sure you’re not buying a lemon, you won’t pay peach prices. That means if you’re selling a car, and you know your car is a peach, you can’t get a fair price for it in that market. You can tell buyers your car is a peach, but that’s cheap talk [1] and they won’t believe you. So, as economists love to say, solve for the equilibrium. Everyone selling a peach flees that market and everyone selling lemons flocks to it. The bad drives out the good.

By rough analogy to all that, we’re coining Gresham’s Law of Beeminder Deadlines: Akratic deadlines drive out intentional ones. Presuming you’re like us, when something comes up and you snooze your deadline, it’s like a retrogressive ratchet. You never put the deadline back where it should be because it’s too onerous to do that within your akrasia horizon. You’d have to muster the discipline to make the deadline earlier again when that deadline is already bearing down on you.

So, in light of Gresham’s Law of Beeminder Deadlines, we’d like to simply kill deadline snoozing. Let us save you from yourselves — backing yourselves into corners where all your deadlines end up at 6am. Paternalism! We see this as an instance of a more general rule of thumb that we’ve called “the #1 rule of Beeminder”: things that make staying on the good side of your bright red line easier make reaching your underlying goal harder.

So that’s what’s currently deployed, as of today. If a goal is in the red, the deadline is just uneditable.

And since this is a violation of the Pareto Dominance Principle — meaning there exist users for whom this may make Beeminder slightly worse — we’re following our self-imposed rule that we have to write a blog post justifying ourselves. So, um, are you convinced?

“Noooooo, I am unmoved, you can take deadline snoozing from my cold, dead hands”

Geez, ok, hypothetical user — if you exist, which we’re not totally sure of. We have ideas for potentially getting the best of all worlds if there’s demand for it. (If this is you, please jump into the discussion in the forum.)

In fact, for a few days until today we tried out an honor system version if you tried to deadline-snooze:

Warning! You’re about to make your deadline later on a beemergency day. This can dampen Beeminder’s effectiveness. Do you promise that either (a) you really endorse this later deadline as a permanent change or (b) you have a good reason for changing it temporarily and have set yourself a calendar reminder to put this deadline back?

(If you just really need a break today, we humbly suggest treating yourself to the derailment)

And then you could click Cancel or “I Promise”.

Early feedback was that it was just too easy to click “I Promise” on autopilot. So we started layering on the duct tape. We floated the idea of a popup like the Uncle button has, where you’d have to type out a phrase to prove you understand the corner you may be backing yourself into.

Or we could make you prove you really mean it by charging a fee to snooze your deadline, if that wouldn’t seem to money-grubbing.

The real best of all worlds

But speaking of money-grubbing, the thing we’d most like to convince you of is that, as we’ve been saying for a while, Derailing It Is Nailing It. (See also the whole “derailing is good-actually” series in the sidebar of Maximal Motivation At Minimal Cost.)

Consider what it means to be willing to pay the tax of snoozing your deadline. Today you’re busy or stressed and it’s hard to meet Beeminder’s demands. Snoozing it has a short-term payoff. But, as we argued above, it’s hard to unsnooze a snoozed deadline. And you presumably chose that deadline for a reason. So the tax you’re paying is mustering the discipline to unsnooze the deadline in the future or else have a permanently less effective Beeminder.

The point is, snoozing a Beeminder deadline is paying a real, if non-monetary, tax. We humbly suggest that you could pay the pledge as that tax. In other words, accept the derailment and enjoy the reprieve on today’s deadline, with no further tax imposed on your future self.

We know it’s a little suspect coming from us, but we’re sincere in our belief that it’s the right attitude for optimal beeminding. The “must do whatever it takes to not derail” attitude is not necessarily all bad but it should have its limits. Deadline snoozing should mostly be viewed as outside those limits since, in practice, it seems to amount to digging oneself in a hole.

Again, if you have strong opinions, especially if you’re sad to be losing the ability to change a goal’s deadline when it’s in the red, do head to the forum thread and chime in.

Footnotes

[1] Cheap talk is the opposite of a costly signal. Also, “cheap talk” might be another case of the holy grail of jargon: a technical concept that’s fully (or close enough to fully) captured in a sufficiently short plain English phrase. See Against Pseudovernacular Jargon where we argue, among other things, that Beeminder’s “bright red line” shares this honor.

Neology

dreev — Tue, 17 Feb 2026 00:00:00 GMT

Previously: Call Them “Quals” and Against Pseudovernacular Jargon.

One time I went to a doctor’s appointment with our son for some knee pain he was having. The doctor asked where the pain was, if it was “below the kneecap”. My son and I both tried to clarify the question: “do you mean below as in under or… um, beneath, or… no, none of these words actually clarify” and finally I was like, “can you just speak Latin??”. (It turns out “subpatellar” means below the kneecap as in further into the body, as opposed to “inferior to the kneecap” / “infrapatellar” meaning closer to the foot.) Doctors may sometimes do this gratuitously but on the whole, Latin makes for surprisingly good jargon in medicine.

I’m pretty enamored with Latin myself but mostly resist subjecting Beeminder users to it. (Workerbees are another story.) I think only one bit of Greek has slipped through, with the akrasia horizon.

I know how pretentious these terms can sound. As Richard Dawkins once put it after using the zoology term Bauplan in one of his books:

Bauplan is just the German word for blueprint. Typically one switches languages to indicate profundity.

As I argued in my screed against pseudovernacular jargon, this kind of thing isn’t (just) about indicating profundity but indicating that it’s a technical term.

Speaking of German jargon, do you know what Freud’s terms for the ego, the id, and the superego are in the original German? “Ich”, “Es”, and “Über-ich” — literally just “I”, “it” and “over-I”. Which is also exactly what “ego”, “id”, and “superego” mean in Latin. I learned this from a bitter Twitter thread, castigating Freud’s English translator for deciding that “such an important concept needed to be made more obscure and technical sounding”. The translator’s choice seems to be widely reviled, but I’m a fan. Switching to Latin was an elegant way to avoid a particularly egregious case of (let me link you to it one more time) pseudovernacular jargon. Even more egregious in English where the word “I” is a single character and it’s hard to even tell when you’re italicizing it. Not to mention zero searchability.

If you don’t want to totally switch languages, may I suggest a foreign prefix? Consider “epicenter”. “Epi” is Greek for “on” so it just means “on the center”. Is that a pointlessly jargony version of “center”? No, when talking about earthquakes you don’t want to always have to disambiguate uses of “center” that refer specifically to being directly above the point at which the strain energy in the rock is first released. Similarly, “hypertext” elegantly jargonifies a certain kind of text. Same for “isoline” or “exoplanet”.

But switching languages isn’t the only solution to the jargoneering [1] problem. There’re always portmanteaus. Like “qubit” for “quantum bit”. (And “bit” itself is a portmanteau of “binary digit” — a nested portmanteau! [2]) Or something taken from literature, like “quark”, or astronomy’s “Goldilocks zone”.

The terminological holy grail is a well-understood word that can be given a technical definition without creating ambiguity. My favorite might be the term “overfitting” in machine learning. It feels similar to Beeminder’s “bright red line” — a technical concept that’s fully captured in a sufficiently short plain English word or phrase. [3] But these are rare. Usually you need to make tradeoffs.

Many years ago I wrote a popular post on how to make such tradeoffs for the case of naming startups and other projects (you can see how the name “Beeminder” fares, if you’re curious). So I’ll conclude by adapting that to the case of coinages in general. Here, I claim, are the core neological desiderata:

Evocativity — conveys as much of the technical concept as possible
Univocity — unambiguous (in particular, not pseudovernacular)
Brevity — shorter = better
Greppability — not a substring of common words
Mellifluidity — alliterative, rhymey, or otherwise fun to say

It’s rare to be able to max out all five but you can aim for that and trade the desiderata off against each other. For example:

“Akrasia” nails univocity, greppability, and brevity, sacrificing evocativity unless you know Greek
“Bright red line” maxes out evocativity, is plenty high on univocity and greppability, but sacrifices some brevity
“Onto” (the math term from the previous post on pseudovernacular jargon) fails so hard on greppability and univocity that its decent evocativity and brevity can’t save it
“Pixel” (picture element) comes pretty close to “qubit” for maxing out all five
“Phishing” is a clever misspelling that yields univocity and greppability without too much loss of evocativity
“Bug” also scores quite high across the board, though perhaps the evocativity is a little tenuous
“Quals” I’ve made the case for already
“Commitment dial” is another case of sacrificing some brevity for some evocativity.

Recall Scott Alexander’s “concept handle” concept handle. Packing up a suitcase full of knowledge or insight and being able to invoke it with a single word or phrase is powerful. It’s why jargon is good, on net. The above desiderata make a concept handle easier to remember, easier to find, and easier / more fun / less confusing to actually use.

Footnotes

[1] Jargonautics? Jargonology? Jargonatomy? Just kidding. As fun as it is to make these up, the word Neology (literally: study of neologisms) is actually perfectly adequate. Funny story: my old blog post on how to name startups and projects is titled “Nominology” and it was only after publishing it that I learned (HT Rob Felty) I’d reinvented the word onomastics.

[2] The original/alternate name of Curlminder, Curlex, has portmanteaus/acronyms nested FOUR levels deep:

Curlex
├─ cURL
│  ├─ command-line
│  └─ URL fetching
│     └─ uniform resource locator
└─ regex
   └─ regular expressions

As far as I know, the only way to top this (HT Brent Yorgey) is with infinitely recursive acronyms, like GNU (GNU’s Not Unix) or PHP (PHP: Hypertext Processor).

[3] Benjamin Fox (AKA zzq) rightly points out that this won’t be quiiiiite right until we eventually make the switch to the Bright Red Staircase. In the status quo, the bright red line isn’t exactly a bright red line, in that you can technically/literally cross it as long as you end the day on the right side of it. I think of “you can’t cross red line” as spiritually true even today, but I agree that it’s bothersome that it’s not fully literally always quite true. It’s true if you think of the red line Beeminder draws as a crude approximation of the true underlying red line with stair steps at each day boundary.

Feature Announcement: Dashboard Hotkeys

bsoule — Tue, 03 Feb 2026 00:00:00 GMT

I’ve been telling people in the forum about my work/life dashboard. I keep my Beeminder dashboard open in a dedicated browser throughout the day, and use it like a to-do list. At the start of the day I uncollapse all the goals I intend to work on that day. That will be all of the red goals, and often many of the orange ones too, though not necessarily all of them. Occasionally there are some blue or green goals in the list as well for whatever rare instances of ambition or pre-planning may arise. Then after I finish doing the things and enter the data, I can collapse the goal to make it smaller, and re-sort by deadline, to keep the done things shuffling down and the urgent things bubbling up.

Only problem is, all this collapsing and uncollapsing and data entry etc involves a lot of clicking around, and I’m vaguely anti-mouse. [1] I’ve used Vim for almost the entirety of my coding career, because I like the dichotomy of insert mode vs command mode. And as a Vim fan, I like any other interface that uses h-j-k-l for navigation (in place of arrow keys), like hitting j and k to scooch up and down past emails I don’t want to answer in Gmail, or bugs I don’t want to fix in our GitHub repos.

So after writing that forum post the other day, detailing all the clicking and scrolling and blah blah blah, it got me thinking about keyboard shortcuts again. As you do. I do? And so today, just to please myself, I implemented them. It turns out it was super easy, not just because robot minions, but because there were no UI tradeoffs. If you’re not into keyboard shortcuts, you never need know this exists.

As of a couple hours ago, you can type “?” to bring up a modal with this cheatsheet:

In text summary (because I can’t stand to give an image of text as the sole description of it): use the ↓/↑ keys, or j/k to enter keyboard mode, and move up and down the list. The Enter key or x will toggle the expanded-ness of the current goal row. e to add new data or refetch from the autodata source, if it’s an autodata goal. r refetches from Beeminder’s servers. g goes to the goal page. s toggles super-collapse. Esc or clicking anywhere exits keyboard mode.

Refetch

The first thing I want to highlight here is being able to refetch the latest data from the Beeminder server by selecting the goal and typing “r”. (Mouse people can also do this by double-clicking a goal’s hexy chevron, i.e., expanding and collapsing in rapid succession.) I have a lot of goals and some of them get data automatically from autodata sources, some of them I tend to update when I’m at my computer (often work-related ones), and some of them I tend to update when I’m away from the keyboard — like my decluttering goal, or my flossing goal. But after I put a few things away and floss my teeth, I’m usually back at my computer doing more work and checking on my Beeminder dashboard. So it’s nice to refresh just the one goal that I know I already updated on my phone while I was AFK, rather than refreshing the entire page and reloading the whole dashboard.

Super-collapse

Another thing in my workflow, which I didn’t mention above, is that I’ve been using a browser extension, BeeBrowse written by Benjamin Fox (aka zzq) for more than a year now, that allows you to super-collapse a goal row in the dashboard. So I had to implement that in Beeminder while I was at it with the keyboard shortcuts. This is honestly kind of the secret sauce in my dashboard-as-to-do-list. At the beginning of the day I actually super-collapse anything I’m not going to work on today and as I finish off goals throughout the day I super-collapse them and they’re basically out-of-mind. It makes my workload visibly shrink as the day goes by and that’s as pleasing as crossing out an item in a to-do list. Your mileage may vary.

Anything else?

The dashboard header is also a navigable row, and has its own section in the help sheet for the hotkeys. Hitting a is like clicking the header row hexy chevron — it toggles expansion/collapse of all of the goals at once. And you can use keys 1-4 to select the different sort orders, so if you need to put them in alpha order to find that one goal or something, you can nav up to select the header row and then type 1. Or much more common for me: type 2 to re-sort by deadline order. As in, I’ve just dispatched several goals and now I want to re-sort them into proper order and get them out of the way of the other goals that are still red for the day.

That’s all for now. Power users, go forth, tell us what’s still missing. We haven’t quite sherlocked Benjamin’s brilliant BeeBrowse browser extension but possibly we should?

Footnotes

[1] I swear I’m not being a hipster about it, it’s more like my fingers are suffering agoraphobia and don’t want to leave the home row. So I use Spotlight [2] to launch every application, and command-tab to switch applications. But I don’t use a chording keyboard to build elaborate symphonies of custom hotkeys.

[2] *tilts head and puts finger to earpiece* I’m being told that only Philistines use Spotlight and that I need to get on Raycast immediately. It even has a Beeminder extension, thanks to the amazing Vivian Guillen.

Beeminder ♥ Steam: Announcing the Steam Autodata Integration

bsoule — Tue, 20 Jan 2026 00:00:00 GMT

We’re excited to officially announce our newest autodata integration: Steam!

Yes, the gaming platform. Do you buy too many games and then never get around to playing them? Do you spend too much time on that one game and have regrets? We have a solution for you. Spoiler alert: it’s Beeminder. It’s always Beeminder.

If you’re new to Beeminder, we help you do the things you want to do by bringing long-term consequences right up and into your personal space. We do that by charging you money today if you’re straying from the track to reach that goal by some distant tomorrow.

So what does goal setting have to do with Steam?

Well, what if you have a backlog of 14 games you bought last year that you haven’t touched at all yet? You can set up a Beeminder goal to spend more time playing games on your Steam account. Or if those unplayed games are sitting there staring at you accusatorily while you keep playing Some Other Game obsessively, you can also set up a Beeminder goal to limit your time with that SOGgy bottom.

Then if your game time playing SOG racks up too far and you go over the bright red line, you get charged money. Contrariwise if you have a “spend more time playing”-type goal for the backlogged games, you get credit for playing your games. If you don’t play enough, you’ll also get charged. So if you’re the type of person who manages to feel guilty about spending time doing things you enjoy, or even just if you sometimes forget because you were too busy flicking through you-tok-gram, now you’ve set up an ironclad excuse for doing what you wanted to do anyway. “Sorry, I have to go play video games for 5 minutes or I’m going to get charged $10.” How’s that for self-care?

Detail-ey-doodles

To set up a goal you’ll look for the Steam logo in the new goal page and enter your Steam Id:

We check that we can actually see your profile — your profile has to be public for us to read your info. If the settings are copacetic then we fetch your owned games and show you the next goal creation screen. There are three main things to fill out here.

Select a game to beemind (or All Games)
Choose between do-more and do-less. If this choice is not self-explanatory, you’d better go back to square one.
Pick the daily time you’re commiting to (for now — you can always change it for next week if the rate you pick here is not quite right)

Then all you’ve gotta do is play your games. Beeminder automatically fetches the time you’re spending and logs it on your goals. While you are playing your game, Steam only updates the information available to Beeminder in half-hour increments. It updates immediately once you exit your game.

So if you’ve set up a Do More goal, and you are down to the wire on getting your last few minutes in before the goal deadline, you may need to exit the game for the values to update. And same for a Do Less goal. If you have less than 30 minutes of buffer on the goal, you may need to exit the game to get an up-to-the-minute count of your time in Beeminder. But either way, Beeminder won’t necessarily be accurate to the minute if you’re actively playing your game.

If you’re sold, head to beeminder.com/new, pick the Steam icon from the list of integrations, and follow the prompts to connect Beeminder to your Steam account. Or, here, have a button:

Moderate Steam Ahead

(See also the full documenation of the Steam integration in our help docs.)

Against Pseudovernacular Jargon

dreev — Tue, 06 Jan 2026 00:00:00 GMT

Beeminder is kind of notorious for its jargon. We’ve got beemergencies, an akrasia horizon, maxflux, and, most recently, tare tags. Some of these seem to us perfectly self-explanatory — derail, pledge, ratchet, commitment dial, post-derail respite — but of course they’re not. From a newbee perspective, the site is full of chicken clucking nonsense. [1] We have a whole Beeminder glossary, for the love of Beejeezus.

The downsides of that are obvious enough. Jargon can be pretentious and obfuscating. The upsides are pretty obvious too. Jargon is efficient and precise. At its best, a piece of jargon puts a concept handle on a whole suitcase-ful of hard-won knowledge and insights.

The Worst of All Worlds

If you can say something in fewer words with more precision using plain English, the Loving Beejeezus commands you to do so. That’s obviously the best of all worlds. We might’ve achieved something close to that when we replaced the cute but opaque “yellow brick road” with “bright red line”. (Apologies to those still wistful about the yellow brick road. I guess we can’t call that one the best of all worlds.)

What I claim is the worst of all worlds is pseudovernacular jargon: using plain English as jargon, rather than instead of jargon. There’s a huge irony here. Plain English can be too understandable. It’s like the quote tenuously attributed to George Bernard Shaw: “The single biggest problem in communication is the illusion that it has taken place.”

If you say, for example, as we used to do, “max safety buffer” then users, understanding each of those words, will jump to the conclusion that they understand the phrase. But “autoratchet” (as we now say instead) means something very specific in Beeminderland and it’s less confusing for users to hear “autoratchet” and know that they don’t know what it means than to hear “max safety buffer” and think they know but be wrong. As Mark Twain supposedly said, it ain’t what you don’t know that gets you into trouble. It’s what you know that ain’t so.

Mathematicians are the worst about this: real, complex, imaginary, rational, natural, normal, field, ring, group, smooth, set, function, … They even do things like make technical definitions for “almost everywhere” and “almost surely”. Some of those work surprisingly well, some work once you’re sufficiently inculcated, and others are kind of a disaster.

If you’re a mathematician and you’ve invented a nifty new complete ordered set (don’t get me started on “complete” and “set” — but “ordered” I like) with binary operators that follow the distributive law, as a way to axiomatize the rational (🤨) and real (🤦) numbers, then, before deciding to call that set-plus-operators thing a “field”, at least consider whether it could cause confusion with the thing gravity and magnets do in physics. Not to mention the dozen other definitions of “field” [2].

I don’t deny that natural language is — as the “field” example illustrates — impressively robust to such ambiguity, but first of all, there are limits. I think the worst example I’ve encountered is the technical term “common knowledge”. [3] And the illusion of transparency means it’s dangerously easy to fail to notice these ambiguities.

But my real gripe is how people imagine they’re being less jargon-y and more layperson-friendly by sticking to simple English. That only works if you’re actually translating the technical stuff to simple English. (Which of course is tedious and inefficient among experts — the whole reason we have jargon.) If you’re just co-opting simple English words as your jargon, that’s what I’m calling the worst of all worlds.

If you overhear a pair of doctors talking about gastric emesis, it might be super pretentious that they’re not just saying “vomiting”. They’ve essentially encrypted their communication. But at least it’s obvious it’s encrypted. What’s worse is steganographic encryption — when you encrypt communication and embed it in other communication to conceal the very existence of the encrypted communication. That’s what mathematicians talking about how “in the field of the reals, almost all numbers are irrational” are like [4]. I mean, I exaggerate. It doesn’t take long to realize that two chatting mathematicians are on a different planet from you, but terms like “real number”, “normal distribution”, and “almost surely” [5], if used in an even slightly normie-adjacent context, are ambiguity atom bombs.

Check out the example of Grant Sanderson (of 3Blue1Brown fame) talking to Neil deGrasse Tyson about how in statistics “people will often assume something’s normal when it’s not,” and Neil has to interrupt to clarify for listeners that Grant means a normal distribution, making a bell shape with his hands. Or if a math newbie hears you talking about “real numbers” and presumes you just mean “actual numbers” as opposed to hypothetical ones. I’ve had this happen myself, not for lack of knowing about the reals, but by accidentally saying “real number” to mean “not a variable”, before noticing the confusion I’d unleashed.

It would, ironically, be so much clearer if the math term for the reals were some fancy Latin thing like “continuata” or “fluxorum”. You see that, immediately know you don’t know what it means, look it up, et voilà; now you know. With “real” you have to see it enough times to catch on that it’s being used in a technical sense and then construct a disambiguating search query like “real number math”. (Of course LLMs have made this easier.)

Again, I get that when fields (not that kind of “field”) do this it does mostly work and you can almost (not that kind of “almost”) always distinguish, say, “real” and “real” in context. But there really are so many examples of this causing problems and I want to hammer on them because so many people I argue about this with are determined to defend pseudovernacular jargon, as I’m calling it.

But I don’t want to pick on mathematicians too much. Using common words as technical terms is better than, as John D Cook points out, how business does the complete opposite and coins technical terms to refer to common ideas. I’m not sure how fair that is but it feels true. I think fear of gratuitous jargon is the biggest reason people do things like use the word “group” to refer to one of the most deep and powerful concepts in math. John D Cook cites the term “group” as the candidate technical term likely to be most confusing for his readers. If you don’t know better it sounds like you mean “collection” which, Cook says, “is just close enough to correct to be misleading.” [6] But at least it’s a noun (and a verb, I guess). I think the worst of all (close behind “almost”) might be “onto” to refer to a function that covers its whole range. [7]

Suitcases of knowledge

Turning back to software engineering, and Beeminder specifically, the pseudovernacular “tests” bothered me enough that we now call them quals. That was the topic of the previous Beeminder blog post:

Your software’s meta-software for quality assurance and regression-prevention infrastructure is not “just some tests” […] More pragmatically, the word “test” is just too common and overloaded.

There are many other examples of Beeminder jargon that’s a result of us attempting to thread this needle. Before we shipped the tare tags feature we debated whether the magic string should be “#TARE” or “#RESET”. Ultimately I vetoed “#RESET” because of how overloaded that word is — did the graph reset somehow, like derailing? a fresh start? resetting the commitment in some way? So many conclusions a user might jump to, thinking they understand the word “reset” perfectly well. Which they do, which is the problem.

We’ve given ourselves a lot of experience trying to find the sweet spot between all these competing constraints — precision, concision, evocativity, approachability, greppability, … Hopefully I’ve convinced you that you’ve got to be careful around plain English, and you’ll want to come back around for part 2 where I’ll give my advice on what good alternatives to pseudovernacular jargon look like. It doesn’t (just) mean translating to Latin.

Footnotes

[1] Fun fact: the word “jargon” comes to us from Old English via French “jargoun”, or “gargoun”. It’s an onomatopoeic word to describe the chattering of birds, thence applied to incomprehensible language. Also related: gargling!

[2] Field a question, field of view, battlefield, field of biology, in the field as opposed to the lab/office/classroom, field of candidates, field in a database, field a team, …

[3] “Common knowledge”, ironically, is such a thorn in my side in trying to communicate unambiguously. Unless you have common knowledge [technical sense] that everyone in the conversation is a game theorist, you’re constantly having to disambiguate it. Because it’s common knowledge [colloquial sense] that “common knowledge” is merely something commonly known.

[4] The math fact that “almost no reals are rational” is super cool. It means that, even though there are an infinite number of rational numbers (rational is “ratio”-nal — meaning fractions; this term I’m kind of a fan of) they’re embedded in a literally bigger infinity of irrational numbers (like pi and the square root of two, numbers that can’t be written as the ratio of integers) such that the percentage of real numbers that are rational is exactly zero! But, again, there are still lots of rationals, hence “almost” none are rational. It’s 0% of them and yet not none of them. Infinity makes things weird.

[5] What if instead of “almost certainly” and “almost always” we used “pen certum” and “pen universum” or whatever the proper Latin would be? It needs some more workshopping but the point is that terms like “almost always” are especially hostile to newbies who can be deceived into thinking they’ve understood something when they haven’t. Would it have occurred to you to seek out the “almost surely” Wikipedia page if I hadn’t already linked to it? It almost surely would’ve if we used something in Greek or Latin. Also you’d be able to grep for it in a paper or textbook.

If you’re still skeptical that this is actually a problem in practice, have a Hacker News thread full of people confused by “almost always” because of mathematicians ruining it.

Or consider an example from @Sean in the Beeminder Discord who was very confused about large deviations theory specifically because he didn’t know that he didn’t know what “large deviation” meant.

[6] Here’s a silly idea to get the best of both worlds: Take the ostensibly normie-friendly naming to an even further extreme and use cute phonetic spellings. Reel numbers, groop theory, etc. Now readers can tell again that you don’t mean the words it sounds like you’re saying. Which is good, because you don’t.

[7] Here are my arguments that the jargon-y sounding terms “injection” and “surjection” are superior terms to the pseudovernacular “one-to-one” and “onto”:

You can grep (aka control-f) for “surjection” but you can’t grep for “onto”.
When a newbie sees one of these -jection words they know that they don’t know what it means and can look it up. That’s less confusing than “since f is onto, blah blah blah” which looks like a typo or the author had some kind of stroke midsentence.
After a small upfront cost, the Latin-based terms are more evocative and memorable. For one, they’re themed, by all ending in -jection. (There’s also “bijection” which means both an injection and a surjection.)
“Injection” you can visualize as the elements of X mapping to a subset of Y — the function injects X into Y. “One-to-one” always makes me think it means a bijection — that everything in X has a thing it corresponds to in Y and vice versa. That’s the usual nontechnical meaning of “one-to-one”.
(Which is my more general point: turning everyday words into technical terms is a recipe for confusion.)
“Surjection” is easy to remember once you’re used to it. The prefix sur- means “over/above/onto” — like surname is the name everyone in the family has; surface is the outer face; surmount means overcome; survey means look over. So a surjection maps over the whole codomain, covering all of it.
We definitely need a word for “bijection” and the plain English equivalents of these terms have got nothing here other than “one-to-one and onto”.

Call Them "Quals"

dreev — Tue, 16 Dec 2025 00:00:00 GMT

Attention conservation notice: This is of no actual relevance to Beeminder users. Except the ones who are software developers, which in fact seems to be most of you. (Also AI seems to be drastically lowering the barrier to entry for creating software, so maybe this is about to be relevant for everyone.) Or maybe you just like these random peeks behind the curtain.

Let me get to the point. I dislike the term “tests” (as in unit tests and test-driven development — code that verifies the correctness of other code). What’s my beef with this perfectly innocuous and perfectly descriptive word? First, it has the wrong connotations. You say something is “just a test” to mean it’s a one-off trial. Your software’s meta-sofware for quality assurance and regression-prevention infrastructure is not “just some tests” in that sense.

More pragmatically, the word “test” is just too common and overloaded. You can’t search GitHub for Issues that mention “tests”. The results are overwhelmed by mentions of tests-as-in-experiments, offers to test something out, the Javascript regex checking function, pen-testing, developers with chips on their shoulders saying “don’t test me, motherforker”, … Am I exaggerating? On that last one, yes, but I did test this in Beeminder’s bug database, searching the word “test”, and got back 275 gissues (as we call them, for GitHub Issues).

It’s similar for grepping the codebase for the string “test”: 316 lines. That’s more than twice the number of lines mentioning the thing we actually care about.

How do I know? Because we call them quals. It’s an infinitely better term! “Qual” already means “test” in some contexts, as an abbreviation for qualifier. Like how you have to pass your quals before you’re a Ph.D. candidate. It also evokes quality assurance, which makes sense for software.

Then there’s verbability. With “test” you’ve got “I tested that” but it’s impossibly ambiguous. Did you just try it or did you actually qualsify it? [1]

This is part of my broader jargon rant for a future blog post, about how confusing it can be when technical terms also have everyday meanings. (UPDATE: The sequel has arrived! Against Pseudovernacular Jargon.)

What about “Specs”?

We had a whole debate about this in the forum. I think it’s a fine choice. I’ve come to like “quals” better, mainly because specs are both more specific and overlap with another concept I’m enamored with:

“Spec” definition 1: The document that states the goals or the problem being solved and how all the code will work from a user’s perspective.
“Spec” definition 2: Code that verifies that functions given known input produce expected output, or that some sequence of UI interactions yields the expected program behavior.

But it’s not at all crazy to overload the term to just mean both of those. They’re pretty related. One is a document in English specifying how future code will function and the other is code that verifies the behavior of existing code. Robert Perce argues that thinking of those two senses of “spec” as the same thing leads to design and usage insights that improve both kinds of specs.

Uphill battles

“Spec” in this sense even has some traction in the wider software world and so might’ve been less of an uphill battle than “qual”. We’ve gotten used to saying “quals” now and I like it and hope it catches on. But realistically, it’s hard to argue it was worth the effort, especially if it doesn’t catch on anywhere else. Mostly I was just annoyed by ambiguities we’d run into talking about “tests” and was nerd-sniped by the onomastic challenge of finding something better.

Footnotes

[1] In some engineering contexts, “qualify” as a transitive verb already means running a set of checks to demonstrate that something meets its requirements. So, yes, you could be less weird and verbify “qual” as “qualify”, but we are not in the business of being less weird. (Also I think this existing sense of “qualify” is another point in favor of the term “quals” to refer to a software test suite.)

Writing About Writing: Highlights from Inkhaven

dreev — Tue, 02 Dec 2025 00:00:00 GMT

This post is adapted from an earlier and less Beeminder-specific version on LessWrong.

I returned from Inkhaven (see my previous post about heading there) a week and a half ago. It was intense and wonderful and I highly recommend it to fans of the Beeminder blog, if it happens next year, which it seems like it will. I should clarify that I wasn’t officially a participant myself. The organizers recruited me as a Contributing Writer, to share my decades of blogging wisdom. And to help with writing-related commitment devices, of course. It was quite an honor, given the list of other contributing writers: Scott Alexander, Scott Aaronson, Dynomight, Gwern Branwen, Andy Matuschak, and many others. The participants were no slouches either and included many friends and people I’m a huge fan of (such as Rob Miles) and others who I’ve become friends with and/or become a fan of.

With 40 posts published per day by participants for 30 straight days (1200 total! not counting those by organizers and contributing writers) it feels impossible to highlight samples of them. Fortunately that’s been done for me: the Inkhaven spotlight. And just to semi-randomly pick a single post to highlight, for making me laugh out loud, I’ll link to one by Rob Miles. And to non-randomly pick one because it’s somewhat about Beeminder, I’ll link to Lucie Philippon’s post about Cyborgs vs Mentats. It describes two schools of thought on incentive alignment. There are also myriad poignant, insightful, informative, and otherwise delightful posts. And, sure, plenty that don’t quite work yet. But that’s the point: force yourself to keep publishing, ready or not, and trust that quality will follow.

Which brings me to the two Inkhaven lessons for me personally as a writer.

First I need to follow the advice (#5 in my collection of Inkhaven tips) to dedicate blocks of time to dumping words onto the page. Editing is separate. See the version of this post on LessWrong for an embarrassing illustration, where I started with “here I am on the plane” on my way home from Inkhaven. It’s a terrible opening but the alternative was sitting there agonizing about a better opening, getting distracted, and having nothing written.

The second lesson for me in particular is that sometimes it’s ok to hit publish before something is perfect. In addition to helping the participants, and some amount of work on Beeminder, I committed to the same thing the participants did while I was there: publishing a 500-word blog post every day by midnight. It was hard! And sometimes — as above — something mildly embarrassing wound up in the published post because I ran out of time for the editing. But I also managed to actually publish fourteen posts in fourteen days. I link to the more Beeminder relevant ones below.

Write, publish, repeat.

When I got home I agonized about committing to keep churning out a post every day for the rest of the month. Obviously I needed to have created a Beeminder goal if I were serious about that. So far I’ve decided that my current blogging commitment is enough — biweekly Beeminder posts, plus my new AGI Friday Substack. I do have a pretty much unlimited number of ideas to write about, even sticking to the theme of writing about writing. Here are half a dozen of them:

Complaining about a list of words I’ve been curating for which Google’s built-in dictionary definitions are garbage.
What a work of art the 1913 edition of Webster’s dictionary still is and how to configure a Mac laptop so it pops up those definitions when you hard-press on a word with the touchpad.
Why Overleaf is the bees’ knees for technical writing or collaborative writing (and definitely what you want for technical collaborative writing).
More of my favorite word games (does that count as writing about writing? maybe I can find a way to make it count!).
My ever-growing pile of notes about and examples of redefining everyday words as technical jargon (“common knowledge”, “real number”, “normal distribution”) and how bad this is.
Tips for dealing with trolls (beyond not feeding them, which is rules 1 through 17).

The problem is how daunting it feels to do justice to some of those. But that’s where the writing tip to First Just Write comes in.

(Fun fact: I spent some hours today thinking today’s Beeminder blog post would be the one about my thoughts on jargon before I threw up my hands and realized my Inkhaven retrospective deserved a translation to the Beeminder blog.)

So let me end this with a recap of my at-least-vaguely-Beeminder-relevant posts from my Inkhaven stay:

Against Powerful Text Editors (mindless, repetitive edits waste less time than it seems and avoiding them is more costly than it seems — this is sort of a commitment device for not getting distracted by your own tools)
See Your Word Count While You Write (I made a tool, Tallyglot, to see your word count in various online editors)
Why to Commit to a Writing and Publishing Schedule (it matters for you and for your readers; also covers “how”)
Strategically Procrastinate as an Anti-Rabbit-Hole Strategy (aka Just-in-timeboxing)
The Eightfold Path To Enlightened Disagreement (characterize, crux, ITT, steelman, scout-mindset, etc)
Smarmbots, Secret Cyborgs, and Evolving Writing Norms (new rule: no plagiarizing LLMs)
Mnemonic Exposition (how to name and gender hypothetical characters)
Eat The Richtext (another tool I made for preserving formatting when pasting text)
The Principle of Delayed Commitment (more pro-procrastination propaganda — this one’s already on the Beeminder blog)
Ten Wrong and Dumb Grammar Rules (infinitive splitting, less-vs-fewer, syntactic vs elocutionary punctuation, etc)
Writing Tips from Inkhaven (listicles, curse of knowledge, Hemingway Mode, the out-loud-to-your-friend constraint, etc — two Beeminder users got $15 honey money bounties for two of these tips)

The Principle of Delayed Commitment

dreev — Tue, 18 Nov 2025 00:00:00 GMT

The irony is not lost on us that this post has been languishing as a draft + pile of notes for so long that it has a title image from the era when a human illustrated Beeminder’s blog.

The Principle of Delayed Commitment says to consider whether a decision or task will be worse or harder if delayed. Often it won’t be, so delaying is a no-brainer. Procrastination isn’t inherently bad. It’s bad when it causes actual badness. Consequentialism! People commonly overgeneralize the virtue of prudence/preparedness to cases where it’s blatantly irrational.

Yes, we know, this is Decision Theory 101 and intuitively obvious for some people. Optionality has value, the end. But writing it out for a lay audience is the best way we know of to establish it as a concept handle, and we’ve found it to be a useful one. In particular, principlifying it helps catch cases you might otherwise miss. “By the Principle of Delayed Commitment, let’s not bother to do XYZ yet.” Saying it that way clarifies that you’re not (just) procrastinating. That we might do XYZ differently (or not at all) depending how other things play out.

“People commonly overgeneralize the virtue of prudence and preparedness to cases where it’s blatantly irrational”

This can absolutely be taken too far. Some of us (especially me, dreev) may use it to rationalize procrastination beyond what’s actually rational. (“What if a meteor destroys the city before April 15 and I did my taxes early for nothing?”) As Scott Alexander astutely asks, “Should You Reverse Any Advice You Hear?”. Some people need to be more prudent and pre-planny and others do that too much. But there’s a theoretical sense — and for plenty of people a practical sense — in which it’s true and useful.

Toilet seat toggling

Here’s a simple example to start with: Don’t toggle the state of the toilet seat — up vs down — after you use it. You don’t know who will be next to use the toilet, and therefore don’t know what state they’re gonna want it in, so don’t waste effort lowering it when you’re done. What if you’re the next person to use the bathroom? You’d have lowered it for nothing!

Though huge caveat here: actually toilets are gross and you should put a lid on that germ bowl to separate it from the rest of your bathroom.

But in principle, toggling the seat is wasted effort.

The same applies to other shared toggles as well. Should you switch the TV back to cable input after you finish playing games? Nope. Should you switch the seat position and mirrors back after driving if you share the car with someone who is differently sized than you? Nope.

The kids’ carseats

Real world example from Bee, whose parents drive her niece and nephew around town often enough to have their own child carseats in their car. They leave them installed in the backseat most of the time.

They don’t take the carseats out every time they drop the kids off in case they need to give an adult a ride before they see the grandkids again. And when Bee gets a ride from them to go out antiquing or to a choir concert, and needs to use the back seat, she doesn’t reinstall the carseat when she gets out of the car.

On both ends of the transaction they wait until the next time the backseat needs used, and toggle the state — carseat or no carseat — appropriately. Unless it’s certain who is going to be the next passenger in that back seat, they delay commitment and deal with it in the future.

Bridge crossing

Sometimes when you’re riding your double-decker bike to the neighboring town across the river, you think to yourself, “I need to bike over that bridge. In order to get across the river.” And you may be tempted to get that part of your journey out of the way early.

But we propose that it’s more prudent to cross the bridge when you come to it.

(The related aphorism, about enumeration of pre-hatched chickens, says to not depend on things that haven’t happened yet, as opposed to not doing things it may not make sense to do yet.)

You may be wondering at this point if we actually need the “principle of delayed commitment” concept handle when there’s already the perfectly serviceable “cross that bridge when we come to it”. Well, “need” is a strong word but we use both of them. We’d say the bridge version feels more casual — “let’s not worry about that yet” — while the principle version prompts us to think about what’s decision-theoretically optimal.

Premature optimization

Software devs will be familiar with this. Indexing your database before you actually have any data in it. Setting up geographic redundancy before you even have users outside your timezone. Caching too soon. Adding features or building out too much functionality before you’ve had actual users. See especially the Shirk & Turk Principle.

Pre- or over-planning

As a real-world counterpart to the software version above, it can be tempting, especially if you are an anxious person, to expend a lot of effort planning for events that may never come to pass. Applying the principle of delayed commitment can be useful if overplanning is your tendency.

Don’t borrow chairs from your neighbor for your dinner party until your RSVP count actually exceeds your chair capacity. Don’t buy a full kosher set of dishes and build a second kitchen until the Rabbi agrees to come to dinner. Don’t buy plane tickets to attend your grandmother’s funeral until she’s actually dead.

Some of these are more obvious than others. But, again, some people in some situations are prone to blatantly violate the principle and calling it a principle is sometimes helpful.

Lazy evaluation

Here’s another one from computer science. Suppose I ask you if it’s true that either 248 is an even number or there are infinitely many twin primes. Lazy evaluation means noticing that the or-statement is true as soon as you figure out that 248 is even, so don’t bother solving the twin prime conjecture. That example is contrived and ridiculous but this comes up constantly in programming. Statments like if(x > 0 and long_computation() == x) can be short-circuited and the long_computation() avoided whenever x is not greater than zero.

Delaying those ticket purchases

There are lots of reasons to buy tickets early — for concerts or flights or whatnot. The price might go up, for one thing. And you get more choice in seating or departure time.

But the principle of delayed commitment tells us to trade that off against the advantages to delaying: Something might come up to keep you from going to that wedding. Is there only a 50% chance your cousin will actually go through with it? Are your mother and her brother constantly fighting and your whole family might get uninvited? Are you getting a new boss next month and you’re not sure if you’ll be able to get time off work?

If there’s additional information that might decrease your excitement about attending (maybe the concert set list, or opener), or that might increase your excitement or otherwise change your decisions (you find a friend who wants to go too, but only if you can get seats together), then there’s value in delay.

Ok, the principle of delayed commitment says it can be rational to delay in order to preserve optionality. Ironically, and especially relevant to Beeminder, there’s also a meta-rational reason to delay, in order to remove optionality. I (dreev) blogged about this the other day on LessWrong as part of my stay at Inkhaven: “Strategically Procrastinate as an Anti-Rabbit-Hole Strategy”. It points out that delaying starting a task can serve as a commitment device to cap the amount of time you spend on it. Just-in-timeboxing, we’re tempted to call it.

Final question: Does Beeminder not blatantly violate the principle of delayed commitment, locking you in to a course of action that might turn out to be suboptimal? It sure does! We’ve thought about this forever and our best answer is still Flexible Self-Control. It’s worth “irrationally” pre-committing to thwart your impetuous future self. But retain as much flexibility and optionality as possible. Beeminder does that with an akrasia horizon: You’re only ever on the hook for the upcoming week. So you can change what you’re committed to at any time and that change takes effect with a one-week delay.

Coauthored with Bethany Soule. Thanks to Mary Renaud for helpful discussion. Image credit: Faire Soule-Reeves.

Inkhaven

dreev — Tue, 04 Nov 2025 00:00:00 GMT

Probably some of you will be pretty enamored with the concept of a writing retreat in which you have to publish a 500-word blog post every day by midnight or you get kicked out. Well, the concept is real! The inaugural such thing, dubbed Inkhaven [1], kicked off three days ago in Berkeley with 41 participants. On day 4 we’re down to 40. [UPDATE: Turns out that was incorrect. I’m not sure why it looked like that at the time on the website, but all 41 participants officially made it to the end!] The prediction markets currently expect, as of me writing this, about 34 people to make it till the end on November 30. Here’s a [static] graph:

Or follow along with the firehose of blog posts.

The idea for this thing was born when Scott Alexander made the observation that whenever he sees someone blogging every day, they almost invariably end up successful in some way. Scott called it his best leading indicator of a good blogger.

The Inkhaven organizers are perfectly aware that correlation doesn’t imply causation, but, as the saying goes, sometimes correlation does “waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.” They refer to Inkhaven’s conceit as Goodharting on that correlation and explicitly warn prospective participants that committing to writing every day might merely bring the correlation between daily writers and eventual good bloggers to zero.

Goodharting?

Yes, as in Goodhart’s law, that any metric you try to optimize quickly becomes meaningless because people game the crap out of it. We’ve talked about it a lot on the Beeminder blog over the years. Like in “Ice Cream Truck Loopholes”, where we argue that Goodhart’s law doesn’t tend to sting you too badly when you’re the one making up the metrics. Trying to game them is like pulling yourself up by your own petard? Since you care about the underlying goal, you can just change the metrics if Goodharting is ruining things.

DIY Inkhavens?

I’m proud to report that Beeminder is a minor sponsor of the event. And I’m especially opposite-of-humbled to report that I myself have been recruited as a mentor/”Contributing Writer” for the middle two weeks of Inkhaven. My objective is to help participants set up their own Inkhavens after the retreat is over.

The foundation is, naturally, setting up a goal to graph your wordcount over time, automatically, and commit to some number of average words per day. (Unlike at Inkhaven, with Beeminder you can write more on some days and build up safety buffer. Unless you don’t want that, in which case there’s the autoratchet feature. Beeminder is very flexible.) See our old Newbee Corner blog post, Beemind Your Writing (By Word Count, Automatically), for the basics.

Finally, I should clarify that I’m not technically a participant myself. But I would crush it if I were. Inhumanly astute readers will notice it’s 11:59pm and this blog post (including this sentence, including this parenthetical) is exactly 500 words.

Footnotes

[1] Yes, “Writehaven” would’ve been a better pun on the name of the retreat venue, Lighthaven, but the name was taken by a different tool they built.

The Protein Dial

dreev — Tue, 21 Oct 2025 00:00:00 GMT

Background: Mindful Munching with Metrics and especially Gauges and Dials.

The short version of the background here is that there are a bunch of smart things you can do to manage your weight (more to the point: your size) and you should focus on those things more than the number on the scale. Also don’t get stuck in diet mindset. But also, Beeminder is all about bright lines and obsessing about numbers and we think there’s a way to get the best of both worlds. Mindfulness and healthy habits and moderation and all that as the foundation, along with numbers-based guardrails.

Commonly we focus on scale weight as a proxy metric for a fitness goal (but there are other options). We call such a measure of your progress a gauge. You also need a primary metric under your direct control that affects your gauge. We call this a dial. Net calories is a classic dial. Reduce your net calories, see your scale weight trend vaguely down.

One big problem with net calories as a dial (besides how much some people hate tracking them) is that you can create a vicious cycle. The less you eat, the more your body clings to every calorie you do give it. Maybe you find yourself sleeping more, or feeling generally low-energy so you move less.

A related problem with using scale weight as a gauge is that you might lose weight by losing muscle instead of fat. We strongly recommend doing resistance training [1] while trying to lose bodyfat, to avoid that.

But also: protein!

Getting more protein makes everything easier. It helps ensure you don’t lose muscle, of course. And it fills you up, making it easier to run a calorie deficit or avoid a calorie surplus.

So I thought, what if we ignored calories and focused just on protein? Maybe the right things tend to naturally happen when you do that. Instead of reducing net calories when your gauge is in arrears, just up your protein. Is that not abusable, you ask? Would you just eat even more calories in order to hit the protein minimum? Our solution to that is to make your dial metric be, not the absolute amount of protein you consume, but the fraction of your day’s calories that are from protein. (More on this in the “couldn’t you game this” section below.)

Nitty gritty

Alright, the first thing to decide is the bounds (extrema) of the dial. I’ve picked 25-50%. That is, maximally lax is 25% and hyperstrict is 50%. The latter is what hardcore gym types sometimes recommend for what they call a mini cut. (See the related reading below.)

“When your [weight] is not near the bright red line then you just ignore all of this”

Of course, per usual with the Gauge/Dial system, if your gauge is below Beeminder’s maxflux line (think: not near the bright red line) then you just ignore all of this. So even though the “maximally lax” setting of 25% protein is not exactly trivially easy, you don’t even have that restriction when your gauge is far enough in the green.

What we do recommend as general guideline regardless is to aim for at least twice as many grams of protein as your lean mass in kilograms. Like if you weigh 155 pounds and have 30% bodyfat, that’s 49kg of lean body mass, so your target is >98g of protein. This is hotly debated, maybe somewhat less is fine, but all our research sugggests that at worst excess protein is kind of wasted (or can make it hard to get a calorie surplus if your goal is to gain weight) so might as well err high.

At the hyperstrict extreme of 50% it’s actually hard to eat a balanced diet. There aren’t a ton of foods that have more than half their calories from protein. We’ll see an example with chicken breast (77%) in a moment.

Being bound by the protein dial — when you’re in the red on whatever your gauge metric is — means following one rule:

Say your protein dial is set to D, something between 0.25 and 0.5. And say P is the grams of protein you’ve eaten so far today. Then your total calories for the day, C, must always satisfy C ≤ 4P/D.

That’s the bright-line feedback control system part. And of course you let the spreadsheet worry about the math for you, but for the nerds, here’s where that limit comes from:

Your fraction of calories from protein is 4P/C (each gram of protein has 4 calories) and the dial constraint is that this must be at least D. I.e., 4P/C ≥ D. So solve for C and Bob’s your uncle.

“Your total calories for the day, C, must always satisfy C ≤ 4P/D”

For example, if D=1/3 and you have 100g of plain chicken breast for breakfast, that’s 32g of protein and 166 calories. So your calorie cap is 4×32×3 = 384, of which you’ve had 166. So 218 more non-protein calories until you have to get more protein.

Again, this gets hard as the dial approaches D=0.5. But the idea is that your gauge metric will improve with these high-protein days and, with a bit of mindfulness, you’ll keep your gauge in the green and not need these abnormally high protein days often. Of course it’s still great to follow the general guideline every day, to hit your protein minimum. Just that strictness isn’t strictly required when your dial is all green.

Couldn’t you game this by…

Yes, suppose you’re in the red (so the dial rule is in effect) but you get your heart set on a 730-calorie Crumbl cookie, which, sadly, is only 2.7% protein. No problem, even at the hyperstrict D=0.5, if you gulp down (math math math) 896 calories of pure protein powder first, you’re all clear for the Crumbl cookie!

But any system can be gamed and thwarted if you try hard enough. Hopefully that’s an ice cream truck loophole. Also, would you even still want the Crumbl cookie after 896 calories of protein powder?

The Snackwell effect and the so-called Big Fat Lie

Speaking of gaming the crap out of things… Wavy flashback lines to the 1990s, when conventional wisdom was that dietary fat was public health villain number one. Eating fat would clog your arteries and give you a heart attack. But if you take, say, yogurt and add a bunch of sugar then the percentage of calories from fat goes down and you can call it LOW FAT and that sounds healthier. Oy.

One company (or brand of Nabisco I guess) that cynically exploited this confusion was SnackWell’s, which made sugary treats marketed as health food because they were FAT FREE.

The Snackwell effect refers to people eating more of something because it has less of something they think is bad.

I’m particularly amused by the irony here. I grew up not questioning what Gary Taubes dubbed the Big Fat Lie. [2] Then I spent a couple decades shaking my head at how dumb I was to think skim milk was healthier than whole milk. And now here I am, treating skim milk as almost twice as good as whole based on the percentage of calories from protein.

Not that “fat is the devil” has been vindicated. Just that fat is very calorie dense and crowds out protein.

Trying this in real life

I’ve been logging what I eat (and burn) in Google Sheets like so:

It’s pretty slick, I think. I can enter the number of calories directly and ignore the multiplier column (it defaults to 1) as in the 150-calorie protein bar above. If I have a 2nd bar I can put a 2 in the “mult” column. Or I can enter a formula like “=100/113” (shown as 0.8849 above) if a serving has 100 calories and 113 grams, as happens to be the case for 2%-milkfat cottage cheese. Then I can use a kitchen scale to see how many grams I’ve eaten and put that in the multiplier column. Either way, the actual number of calories consumed is shown in yellow.

What about home-cooked food or restaurant food with no nutrition labels? For this I use the Macrofactor app. It lets you take a picture of any food and magicAIally guesses all the nutrition facts. (Or if you’re eating something with a bar code, it knows all the bar codes. You just scan it.)

(ADDED LATER: You people are relentless. Yes, you can drop a stick of butter in your spaghetti sauce and the AI calorie guesser will be none the wiser. The solution to that is, don’t do that. The AI is making a guess based on what it can see and it will sometimes be over and sometimes be under and on average it will be… pretty good, I think? Unless you introduce a systematic bias by making deceptive-looking meals. Again, maybe don’t. And let us link again to our post about ice cream truck loopholes.)

Of course Macrofactor is its own whole system for nutrition, fitness, and weight control. I think it actually has a similar control system deep inside its black box. It shows you your trend weight (if you tell it your weight) and adjusts your calorie and protein recommendations to nudge you towards whatever your goal is. It’s all too opaque for me though, as I suppose it has to be to be usable by normal humans. Still, a Beeminder goal for adhering to its recommendations most days might well work as effectively as this protein dial system.

But back to the protein dial, I also have this gauge/dial dashboard on the same spreadsheet:

As you can see, today my scale weight is slightly above the maxflux line on my Beeminder graph so the target for my dial is 29%, slightly above the minimum (maximum laxity) of 25%. Of the 483 calories I’ve eaten so far, 285 are from protein, so my actual dial is at 59%, far above what it needs to be. I can then enter a hypothetical protein percentage for the rest of the day to see how many more calories I can eat such that my dial doesn’t dip below the 29% target.

If I wanted to reward myself with Crumbl cookies at this point, I’d change that 27.5% to 2.7% and I’d see that final line change to 550 calories. So I could eat like three quarters of a Crumbl cookie and I’d be done. If I, in gross violation of this system, ate the whole 730-calorie cookie, I’d see the final two lines as follows:

2.7% protein for remaining calories
−180 remaining calories eatable

In other words, I exceeded my allowed calories by 180. But the situation is salveagable if from here on I exceed the dial target’s 29% for my remaining calories. Suppose I decide to start eating non-fat Greek yogurt, which has 69% of its calories from protein. I put that 69% in the protein percent cell and now the final two lines show this:

69% protein for remaining calories
119 remaining calories needed

So 119 calories of yogurt and my dial will hit 29% on the nose. Perfect. For the rest of the day I can eat what I like as long as it’s not below today’s dial target of 29% protein.

The Five Theses of Dials & Gauges

Melanie and I have been debating this endlessly in the weightloss channel of the Beeminder Discord. I gravitate strongly to quantified systems and bright lines; Melanie’s all about flexible rules and addressing one’s underlying psychology. She worries that my approach can engender an unhealthy relationship with food. (Bethany has very gamely experimented with both of our approaches and may have more to say about her own happy medium in a future post.) But Melanie and I managed to converge on the following five principles for getting the best of both worlds — mindfulness and healthy habits along with a feedback control system.

(One more review: you monitor a proxy metric of your health called a gauge and have another metric you control (like protein percentage) called a dial. Adjust the dial, per a formula, to control the gauge. And critically, if you’re far enough in the green on your gauge, you ignore the dial altogether.)

Adding dial&gauge guardrails rarely hurts. In theory they can be a crutch, like in that Batman movie where he couldn’t manage to climb out of the pit until he ditched the safety rope and was nice and motivated by a fear of death. Just learn to not let the graph psyche you out rather than ditch the graph.
If you get past Thesis 1, adding guardrails is just a backup system. You can and should use mindfulness to avoid ever hitting the guardrails. (And if in doubt, and especially if using scale weight as a gauge, set your red line much higher than you may think you want it. That way you have plenty of breathing room to practice mindfulness without pressure.)
If you do hit the guardrails, they help. They do not backfire by putting you in a what-can-I-get-away-with mindset or make you feel guilty for eating “bad” foods or any of that. If you’re prone to those psychological traps, address them separately. The guardrails are a more systematized and distilled version of mindfulness for people who can benefit from bright-line rules.
If the rules that kick in when you hit the guardrails are counterproductive in any way, well, we’re all adults. You should be trusted to perceive that counterproductiveness and construct better rules.
Or even simpler: dial up (in the informal sense) the strategies Melanie teaches to mindfully minimize bumping into the guardrails in the first place.

Protein in various foods and food-adjacent products

As a handy reference, here are some options for upping your fraction of daily calories that are from protein. To compute these protein percentages, take grams of protein times 4 (usually) and divide by the number of calories. (Sometimes this can get fussy, either due to rounding on the label or because technically some sources of protein have a bit less than 4 digestible calories per gram. This is called Atwater specific factors.)

Thanks to Melanie and Bethany who helped compile these.

83-96% for whey protein isolate protein powder. I get pretty sick of this pretty fast.
80-91% for liquid egg whites. I haven’t tried this.
80% for tilapia.
78-90% for canned white tuna packed in water.
77% for skinless chicken breast. (50% for skinless chicken thighs.)
75% for David protein bars which I think taste better (no worse anyway) than Cliff bars. They’re very processed, of course, but the ingredients seem fine enough. I doubt it’s much less healthy than protein powder.
72% for turkey lunch meat.
69% for non-fat Greek yogurt.
60% for non-fat cottage cheese.
47-59% for tofu.
59% for salmon.
58% for Musashi bars which have grown on me and I think taste at least as good as the David bars. The ingredients also seem fine enough. Both Musashi and the David bars use fake sugar, which I think is fine.
53-54% for, what — spinach! Who’d a thunk it?
49% for Built Puff bars (brownie batter flavor) which taste, bizarrely, about as good as normal candy bars. Suspicious.
43-47% for dry roasted edamame (too dry for me). And also for non-dry-roasted aka soy beans.
44% for Quest protein bars (chocolate chip cookie dough flavor).
40% for skim milk.
34% for a hard-boiled egg.
33% for Snickers Hi Protein bars (not palatable IMO).
28% for brussel sprouts. These brassicas and leafy greens keep surprising me. Kale, kohlrabi, cauliflower, collard greens — they’re all in here from ~25-32%.
27% for lentils.
26% for black beans.
24-27% for RXBARs.
24% for Gatorade protein bars (chocolate caramel flavor).
23% for broccoli!
23% for cheddar cheese.
22% for chickpeas.
22% for whole milk (note skim milk all the way up at 40%).
20-23% for cabbage.
20% for Mealsquares, which are minimally processed.
14-22% for whole wheat bread. (We looked up a handful of our local supermarket brands to get a range.)
14-17% for Cliff bars (and they don’t even taste good, what the fork).
15% for quinoa.
14% for oatmeal.

Footnotes

[1] Melanie would like us to emphasize here that you need progressive overload, meaning that you keep increasing weight and reps. In the wise words of Greg LeMond, it never gets easier, you just go faster.

[2] My robominion is pushing up its glasses and saying that technically it was the New York Times Magazine that came up with that title — “What If It’s All Been a Big Fat Lie?” — for Gary Taubes’s article.

Feature Announcement: Tare Tags

dreev — Tue, 07 Oct 2025 00:00:00 GMT

As promised, tare tags are live! And the old “odometer reset feature” is dead, thoroughly subsumed by the new tare tags.

Background

Besides knowing that taring (aka zeroing) is what you do to a scale when you want to weigh stuff not already on the scale, you need to know that Beeminder really likes cumulative graphs. See the classic post from Grayson Bray Morris explaining why, for Beeminder goals, cumulative graphs are Good and Correct. (Very briefly: it’s how we allow you to accumulate safety buffer, getting ahead of the bare minimum you committed to maintain as an overall average.) But the important thing for now is that Beeminder graphs are cumulative whether or not you enter cumulative data. Let us explain.

Most Beeminder graphs, like standard do-more or do-less graphs, don’t expect cumulative data. These graphs are auto-summing. That means you enter the additional amount (“the delta”) that you did each day and the cumulative total is plotted on the y-axis for you. [1] Odometer-style graphs do not auto-sum. Whatever value you enter is directly plotted on the y-axis.

(Ignore weight-loss and weight-gain goals for now. They’re neither auto-summing nor cumulative, exactly, but also they’re not relevant to taring. Not yet anyway. We’re intrigued by a use case where you tare your weight after getting dressed so you can log your true weight throughout the day without actually getting naked for each weigh-in. But it’s weird and messy — with the numbers and the taring algorithm, not the nakedness — so we’re not officially supporting that yet. If you really want to experiment, you’ll need to change your weight-loss goal to use aggday “last” instead of aggday “min”. Beeminder experts only!)

So usually you want auto-summing. Like if you ate 3 vegetables today, you enter a +3 on your veggies goal. Easy peas-y. But sometimes the convenient number to report is the new total. Maybe you’ve read 7 pages today but to figure that out you’d need to subtract the page number you started at from the current page number. Easier to just tell Beeminder your current page number! Especially since — if you started on page 1 (more on this shortly) — the current page number is your total number of pages read, exactly what’s plotted on the y-axis.

Quick version if you don’t want to learn any (more) new things

It used to be that a zero datapoint on an odometer goal would be treated as a reset of the odometer. Maybe you were beeminding numbers from a literal odometer and it literally got reset. Like you tracked your miles driven and your car died at 123,456 miles. If you want to keep tracking your cumulative miles (let’s pretend you got a brand new car with 0 miles on the odometer) then it would be pretty annoying to have to add 123,456 to the reading on the new car every time you reported the number to Beeminder.

Instead, Beeminder would presume that any zero datapoint was an accidental odometer reset and magically add the previous total to all new datapoints from then on. It was very handy for a lot of things, like beeminding total pages read by reporting the page number. Starting a new book is like getting a new car with zero on the odometer.

But it was also a gross violation of the Anti-Magic Principle, as we talk about in the second half of our recent post about magic strings. I may have mentioned punching my past self in the face.

So now everything is exactly the same except that if you want that to happen, you have to explicitly tag the datapoint with “#TARE” in the comment. See our previous blog post about magic strings in datapoint comments for background on how that works in general.

Ok, this concludes the quick version. You used to implicitly get an odometer reset with a zero datapoint. Now you explicitly get it by tagging the zero datapoint with “#TARE”.

Also we took the liberty of converting all your existing data. Zeros on odometer graphs now have the tare tag appended. (We used the @TARE version instead of the #TARE version, for reasons explained in the previous blog post.) Expect an email about this shortly if it applied to you. [Now sent.]

The wonderful generality (and explicitness)

What’s really new is that you can tare any datapoint. Maybe the shiny new car you bought was test-driven by some grubby grubkins and you only start driving it when the odometer already says “7”. Gasp. Here’s how you’d enter the datapoints (the carets are Beeminder’s shorthand for “today”):

^ 123456 “RIP my old car” [123456]
^ 7 “New car acquired with 7 on the odometer #TARE” [123456]
^ 107 “Inaugural 100-mile road trip” [123556]
^ 999 “About to hit 4 digits on the odometer but 8 more miles till I’ve driven this car 1000 miles” [124448]

In brackets we’ve put the actual values plotted on the y-axis of the graph. Notice how the 7 miles that were already on the new car are never added to the running total — which is just miles you’ve driven — thanks to taring the graph. But all that you, as the user, ever have to worry about is reporting the actual odometer reading.

As a simpler example, maybe you want to beemind your way through a book but skip the boring introduction. Say chapter 1 starts on page 30. When you enter a “30”, that should mean you’ve actually read just the first page of the meat of the book. You can now enter data like this:

^ 29 “Skipping the first 29 pages before chapter 1. #TARE” [0]
^ 30 “Finished page 30, ie, the first page of chapter 1” [1]

Here’s a more convoluted example if you want to really make sure you’ve got your head around it:

data entered		value plotted
0	#TARE	0	It doesn’t actually matter if there’s an explicit TARE tag on an initial 0 datapoint
20		20	First real datapoint
50		50	If this were auto-summing we’d plot “70” but it’s not
0	#TARE	50	A traditional odometer reset
10		60	Odometer shows 10 + previous 50 = 60 total
15		65	Again, add previous 50 to current odometer
100	#TARE	65	Ignore current reading of “100” but remember it
130		95	30 more on the odometer means 30 more than the 65 previously tared at

“We’re relieved that you now have to be less of a supergenius to make Beeminder behave as you want it to”

The general algorithm (feel free to skip this if you’ve got the idea already): Until there’s a taring, the y-values plotted on the graph match the entered datapoints. Now say a datapoint with value v is marked as a tare and let u be the previous y-value (or 0 if this is the first datapoint) before v was entered. Then the actual value of v is disregarded (but remembered). The plotted value corresponding to v is still u and subsequent datapoints are measured as the delta from v and added to u. When that happens more than once, those deltas just accumulate.

(Fun fact: All of this was technically possible with the old odometer reset feature but it involved outrageously clever / excruciating abuse of the feature. Credit to Scarabaea for the ingenuity there. We’re relieved that you now have to be less of a supergenius to make Beeminder behave as you want it to.)

Whittle-Down use case

We don’t know if this one’s a good idea yet, but… As an alternative to the schemes we’ve blogged about for backlog-minding, you could beemind something like your inbox with a whittle-down goal, but every so often, maybe even at the end of every weekend, you just tare the graph at whatever value it ballooned up to.

In this scenario you are still beeminding the size of your inbox, entering the current size and always trying to make that number go down. But you’re kind of giving yourself a free reprieve periodically. Your inbox size can jump up but the plotted y-values will keep going down. Possibly into negative numbers.

Like we said, it could be a bad idea. It seems to violate the Quantified Self First principle. And unless you have a principled rule for when it’s ok tare your inbox at a higher value, that could be too much of a loophole. (Or maybe it’s an ice cream truck loophole and it’s fine?)

Probably you should mostly think of tare tags as applying to graphs that go up. Book reading, bike riding, word writing, Anki reviewing. Those sorts of things. We assume someone will do something wildly creative with this though.

Oh my goodness the sheer number of things this fixes

Here’s a dumb one: Sometimes users would create an odometer goal, which would naturally have an initial datapoint of zero. That would technically yield an odometer reset — what we now call a taring — but when you do that to the first datapoint, it’s a no-op. Fine. Then maybe the user would add a datapoint of 10 for today, in addition to the initial zero. Still fine. But then maybe they’d backfill a datapoint for yesterday of 5. Do you see the problem? The person doing this probably didn’t. The 5 yesterday, followed by a tared zero today, would mean that today’s 10 is now added to yesterday’s 5. So the graph would plot 15 today instead of the expected 10.

That one at least is fairly corner-casey.

How about this disaster that happened to my own URLminder goal, for automatically fetching wordcounts of documents and beeminding my total words written:

What is going on there? Well, the documents I’m pointing to sometimes fail to load when Beeminder requests them, which means Beeminder sometimes fetches a zero datapoint. Now you see the problem. All those zeros got processed as odometer resets, so each subsequent non-zero fetch got added cumulatively to the previous total. (Btw, tarings are indicated visually on the graph with faint vertical dotted lines, same as odometer resets were.) This is especially tragic because it just silently inflated my total wordcount higher and higher until I had infinite safety buffer and the graph was buried at the bottom of my dashboard, out of sight, out of mind.

All these problems are now mercifully a thing of the past.

You could say we’re zeroing them out and making a fresh start. #TARE

Footnotes

[1] We sometimes, like in the API, call auto-summing graphs kyoom graphs, or kyoom=true. It’s phonetic for “cumulative”.

Magic Strings In Datapoint Comments

dreev — Tue, 23 Sep 2025 00:00:00 GMT

This is a followup to our previous post on magic strings.

There are three (soon to be four) magic hashtags that Beeminder can add to a datapoint comment. They’re reasonably self-documenting and if you’re a newbee you probably don’t care about any of this nitty-gritty. But we just finished cleaning this all up in the codebase and we want to document how everything works now. Also we have a heads-up for veterans who may have been counting on some crufty backwards-compatibility for wild use cases that even we can’t fathom. You’ll need to get with the program, as laid out here. (But of course if this is ruining your day or murdering children, definitely let us know and we’ll help get you sorted.)

#SELFDESTRUCT (or #THISWILLSELFDESTRUCT)

We’ve talked about this magic string extensively in the forum and in our previous blog post, but we’ll repeat everything here for completeness.

First, a quick review of Pessimistic presumptive reports (PPRs). Think of them as the do-less analog of flatlining on a do-more goal. For a do-more if you don’t enter any data, Beeminder assumes you did nothing, and soon enough that’ll derail you. If you don’t enter anything on a do-less goal, it’s pretty critical that Beeminder not presume you did nothing, since that would have you safe forever and you could just stick your head in the sand and pretend the goal didn’t exist. Instead Beeminder presumes you did twice the daily amount you committed to stay below, on average, if you don’t report anything. Meaning that, as with a do-more goal, not reporting any progress means you’ll soon derail.

The other thing to know about PPRs is that they automagically self-destruct when you enter an actual datapoint. And the mechanism by which they do that is to include the string #SELFDESTRUCT in the datapoint comment. (Or #THISWILLSELFDESTRUCT — we use that variant to make it as self-documenting as possible.)

That means you can make your own datapoints self-destruct if you want! Why would you want that? Well, superuser Alys has a very clever use case. Suppose Alys has a lot of safety buffer on a goal, but wants to get herself to do at least some work on it today. She enters a #SELFDESTRUCT datapoint with a large negative value (for a do-more goal), putting the goal into the red, which sorts it to the top of her dashboard. Once she’s done some work and enters the data — voila! The #SELFDESTRUCT datapoint is automatically removed and her safety buffer is safely back where it belongs.

Now the fine print and a heads-up.

If for some reason you try to add multiple #SELFDESTRUCT datapoints for the same day, separately or as a batch, each will annihilate its predecessor and only the last of them will remain. Probably that’s the logical, natural thing to happen, but, again, wild use cases we can’t fathom, so we thought we’d spell it out.

And the heads-up is that in the past, datapoint comments that started with “PESSIMISTIC PRESUMPT…” were yet another way to make a datapoint self-destructing. That’s officially gone now and we appended ” #SELFDESTRUCT” to all such datapoints so nothing changes on you retroactively (we don’t think). (And actually we used @SELFDESTRUCT with an “@” instead of a “#” in this case, for a reason we’ll talk about at the end.)

#DERAIL

This one essentially marks a dummy datapoint indicating when a derailment happened. Such datapoints are displayed on your graph as a red triangle, pointing up or down depending on which side of the bright red line was the bad side to cross into.

We want this recorded as if it’s a datapoint in order to record exactly where you were at the point when you derailed. There’s not necessarily a real datapoint at that point due to flatlining. That is, there’s a good chance that if you derailed today, it’s due to not entering any data. For a do-more goal, we show a faint triangle today with the same y-value as yesterday, indicating a presumed zero for today. So the #DERAIL datapoint reifies that into an angry bright red triangle.

(This one also had some backward-compatibility that we ruthlessly excised. Derail datapoints used to be marked by a datapoint comment that started “RECOMMITTED ON”. No more!)

#RESTART

Restarting a goal that’s been archived is very similar to derailing a goal in terms of the dummy “#RESTART” datapoint, and what we do with your bright red line. First we lock the red line in place up until now, so we aren’t changing the past. Then we make the line jump to whatever your current y-value is and add an initial flat spot and the new rate. We also add a #RESTART datapoint, analogous to the #DERAIL datapoint, and as extra insurance that we don’t accidentally trigger the derail condition.

In the case of an ordinary do-more goal that was properly archived it’s probably superfluous, though still nice to have it demarcated.

In other cases it can be totally fluous. Take the case of restarting an odometer-type goal. Your current datapoint might have no correlation to your last datapoint at all. You might have gone back to start your book over again because it’s been so long, or you stopped beeminding your email inbox, and it ballooned back up from the optimistic 10 messages you’d committed to the last time you beeminded it. Or maybe the goal wasn’t archived in the usual way — say you lost your credit card and had to cancel it, and then an ordinary derail caused the graph to freeze because your payment didn’t go through. In any of those cases, the usual flat-lined datapoint from whatever your last entered value was might not be safely on yesterday’s red line. That’s when a #RESTART datapoint is required to keep it from insta-derailing when you restart. Because “red yesterday” is the core criterion Beeminder uses to decide if you derailed. A Beeminder graph matrix has to be continuous, so there’s no way to indicate that yesterday didn’t count because the goal was inactive so we have to retroactively make it have-been active and safe yesterday.

Ok, phew, that was really more than you wanted to know. Probably as a user you only care about this one as a marker of, as it says in the hashtag, when you restarted a goal.

Coming soon: #TARE

As in taring a scale. Tune in next time for the whole story on this one.

More Fine Print, The Hashtag Feature, and Alternate Syntax

All of these are case-sensitive — they have to be in all caps. And if there’s anything else in the comment, it has to be separated from the hashtag by whitespace. So if you make a datapoint comment like “I’m gonna #SELFDESTRUCT!” it won’t work unless you stick a space in front of the “!”.

Ok, do you know about the hashtag feature? It’s in the privacy section of graph settings: “Show hashtags from comments on the graph”. It lets you annotate your graph with any hashtags you like by including them in your datapoint comments. This includes all magic hashtags above. Mostly we think that’s right and proper, explicitly labeling derailments, PPRs, restarts, and tarings. But if don’t want those annotations, only your own custom-crafted annotations, we finally have a solution. Namely, all the magic strings work equally well with “#” or “@”. If you use the “@” versions — @SELFDESTRUCT, @DERAIL, @RESTART, and (soon) @TARE — then everything we’ve said about these magic strings still applies, but they won’t be hashtags and so won’t be shown explicitly on your graph, whether or not you have the hashtag feature turned on.

For now, those who want only their own hashtags annotating their graphs will have to manually change the “#” to “@” on the instances that Beeminder generates. We’re reluctant to add an explicit setting for this unless there’s enough demand for it. So let us know!

Magic Strings are Anti-Magical

dreev — Tue, 09 Sep 2025 00:00:00 GMT

A magic spell is an incantation that, just by being incanted, makes something happen in the physical world. You say “avada kedavra” and someone dies — that sort of thing. Did you know that’s a literal real thing in programming and it’s called a magic string? [1] We use them in Beeminderland occasionally. The source document from which this blog post was generated uses them. That means that I absolutely cannot tell you what those magic strings are in this blog post. If I did it would literally break the blog and you would not be reading these words. Sounds dangerous, you say? Kind of! But it’s pretty ok with a couple ground rules:

Only pick a magic string that can never possibly be uttered accidentally.
Treat magic strings with some reverence and don’t casually type them out if you don’t mean to invoke them.

Like how in the Harry Potter universe people refer to “the killing curse” rather than actually utter “avada kedavra” in casual conversation.

Magic strings done right are, ironically, kind of the opposite of magic in the sense of the Anti-Magic Principle. [2] Namely, they’re a way to have something happen transparently, consistently, and predictably. Never accidentally, opaquely, or silently.

An example in Beeminder (besides the blog post ones we can’t tell you about on the blog) is certain hashtags in datapoint comments. You may know about pessimistic presumptive reports (PPRs) and how they automagically self-destruct when you enter an actual datapoint. Did you know that the mechanism by which they do that is to include the string #SELFDESTRUCT in the datapoint comment? And that you can include that string yourself if, for reasons (HT superuser Alys), you want any particular datapoint to self-destruct when a new datapoint for that day is added?

The worst anti-magic violation in all of Beeminder

Let us pause to tell you about the odometer reset feature. It’s so bad that I (dreev) want to punch my past self in the face for having been the one to come up with it. It’s the feature where, if your datapoint is ever zero we just silently, magically start adding your cumulative total so far to every future datapoint.

(There are mitigating factors: (1) It only applies to goals specifically created as odometer goals and so is not super likely to confuse normal users. (2) It really is exactly what you want to happen most of the time and is super handy for things like beeminding your way through multiple books and being able to always just tell Beeminder the page number you’re on. (3) We did this before we figured out the Anti-Magic Principle. We’d never implement something like that today.)

The way to fix that (coming soon! PS: ok, now) is with, drum roll, another magic string in the datapoint comment. In this case the plan is to use #TARE. [3]

The way it would work in the book reading example above is you complete your first book and enter the final page count as your datapoint value on Beeminder:

^ 349 “finished Adventures of Tom Sawyer”

And then you pick out the next book and add a #TARE point like so:

^ 0 “starting Huckleberry Finn #TARE”

This can also work for other more literal odometers. Say a goal tracking your total cycled miles across multiple bikes and bike computers over the course of your life. And things like taring your weight graph so you can weigh in multiple times in one day without getting naked each time. Also nebuminding, where you want to beemind words added to a document but not let words deleted count against you.

We’re not saying a magic string in the datapoint comment is the best implementation of this tare feature, but it’s expedient and a huge step up from the anti-magic violating abomination that is the current odometer reset feature.

Footnotes

[1] See also in-band signaling which is mostly what we mean here. The Wikipedia article on magic strings is mostly about ways magic strings are abused. Possibly this means we should find a different term!

[2] Not the opposite of magic in the sense of “science”.

(From the Moste Excellent Webbe Comik, Girl Genius, by Phil and Kaja Foglio, et al.)

[3] Like when you zero a scale. There was some debate in the Beeminder Discord a while back about how common-knowledge the term “tare” is.

Time Bridges: Beeminder on The Future of Life Institute Podcast

dreev — Tue, 26 Aug 2025 00:00:00 GMT

This is embarrassingly out of date, but since it’s literally about Time Bridges, that makes it all the more apropos, right? (If you’re wondering how we even dug up something this old, you might like our recent series on backlogs and freshenable collections.)

The Future of Life Podcast is mostly about AI these days, reasonably enough. Actually it’s been particularly prescient in having that as a focus for 9 years or so. But back on the eve of 2019, they did one called “Existential Hope in 2019 and Beyond”. They interviewed several experts, including Beeminder fan Gaia Dempsey who talked about Beeminder and related strategies for what we call incentive alignment.

Since it’s a beautiful bit of Beeminder evangelism (and entirely organic and unpaid, we hasten to add) and because it coins a handy term, we wanted to preserve the relevant bit of the transcript for posterity. We’ve edited it for clarity and concision but, to keep us honest, the original recording is hosted on SoundCloud (from 20m38s to 23m59s).

Gaia Dempsey: My favorite type of tool I think is very rare: the kind that support us developing the capacity for longterm thinking, and for being true to the longterm intentions and goals that I set for myself.

Ariel Conn: Can you give some examples of those?

Gaia Dempsey: One example comes from the book Ready Player One. The main character is interacting with his VR system that he lives and breathes every single day. At a certain point the system asks him, “Do you want to activate your health module?” [It’s called the “Fitness Lockout protocol” in the book.] And without giving it too much thought, he says, “Sure, yeah, I’d like to be healthier.” And it instantiates a process whereby he’s not allowed to log in to the OASIS without going through his exercise routine every morning.

This is an interesting system design choice, because he didn’t actually do that much deep thinking about “oh yeah, this is a choice I really want to commit to”. But the system’s saying, we’re thinking through how your decision-making works, and we think that this is something that you really do want. And we think you’re going to need about three months before you make a final decision as to whether this is something you want to continue with.

So that three month period (or whatever it was in the book) is what’s known as an akrasia horizon.

Which is a term that I learned through a different tool that is sort of a real-life version of that, which is called Beeminder. The akrasia horizon is a time period that’s long enough that it will circumvent a cognitive bias that we have to really prioritize the near-term, at the expense of the future.

In the case of the Ready Player One example, the near-term desire that would circumvent his longterm health is, “I don’t feel like working out today, I just want to get into my email, or I just want to play a video game right now.” And a very similar setup is created in this tool, Beeminder, which I love to use to support some goals that I’m very motivated to meet. So it’s a tool where you can put in your goals and you can track them, either yourself by entering the data manually, or you can connect to a number of other tracking capabilities like RescueTime and others. And if you don’t stay on track with your goals, they charge your credit card. It’s a very effective motivating force.

I have a nickname I call these systems: “time bridges”. Which are really choices made by your long-term thinking self that in some way supercede the gravitational pull toward mediocrity inherent in your short-term impulses.

It’s about experimenting, too. This is one particular system that creates consequences and accountability, and I love systems. For me, if I don’t have systems in my life that help me organize the work that I want to do, I’m hopeless. So that’s why I like to collect and am an avid taster of different systems. And I’ll try anything to see what works. I think that’s important. It’s a process of experimentation to see what works for you.

Amen to all of that.

Downtime and DEFCON Levels

dreev — Tue, 12 Aug 2025 00:00:00 GMT

A couple weeks ago we had close to 9 hours of downtime. Some kind of meltdown in the datacenter in Newark where our servers live. A lot of websites/apps were affected, which I guess lessens the shame of it, though it’s embarrassing we’re not more robust to this kind of thing. Anyway, check out status.beeminder.com for a link to the gory details. And keep that URL in mind to check in case Beeminder is down in the future.

But let’s not dwell on that. It reminded us that we have a system for conveying the priority of bugs and other brokenness that’s sometimes handy. It goes like this:

DEFCON:pS — seconds matter, the servers are literally on fire
DEFCON:pM — minutes matter, the servers are figuratively on fire
DEFCON:pH — hours matter, brokenness we have to fix before we can sleep
DEFCON:pd — days matter, it’s something that preempts all other normal work
DEFCON:pw — weeks matter, lower priority
DEFCON:pm — months matter, no hurry
DEFCON:py — years matter, i.e., maybe/someday, i.e., wishlist

In practice, we never use anything beyond “pd” (days matter). See our old post on our system of bug labels and the ill-fated “OMG” label for why we mostly don’t assign priorities to bugs/tickets. (Except for regressions aka zombies, which we talk about in Control Systems For Backlogs.)

We do combine the seconds/minutes/hours/days levels to get finer-grained priorities. Like DEFCON:pMH means “minutes or hours matter”. So definitely don’t go to sleep before it’s fixed but it’s not quite at the point where you should hold your pee. Or DEFCON:pHd means “hours or days matter”. So don’t literally lose sleep but get back at it as soon as you roll out of bed.

I don’t think we’ve ever used DEFCON:pS. No, wait, there was that time we ran a bad query that started deleting the database row by row. But that might’ve predated this DEFCON nomenclature.

For a burgle bug we’d probably say DEFCON:pM. For a merely embarrassing bug, either DEFCON:pMH if it affected all users, or DEFCON:pH or DEFCON:pHd if it was more niche. Or I guess :pd or even :pdw if it was niche enough.

We’re telling you this because having this shorthand has been handy for us, so maybe you’ll like it too? I think the advantage of it is that it straightforwardly combines priority and severity in a way that’s intuitive to set a value for.

Why Danny Hates the Beemium Plan

dreev — Tue, 22 Jul 2025 00:00:00 GMT

We keep raising the price of Beemium (currently $64/month as I write this). When we do, we grandfather existing users for a year. Most businesses warn everyone when they raise prices. It’s a classic Act Now inducement: lock in the old price before it’s too late. We don’t do that for the Beemium price hikes. Why not? Because we don’t want more people getting Beemium. Do we hate money, you ask? Not exactly. We actually have a list of reasons we want to wean people off Beemium and discourage new users from opting for it. Setting a high price is saying “don’t go for this one unless you really, really want it”.

So here are the reasons.

1. It’s unbeeminder-y

I, as an avid Beeminder user (as opposed to ex officio as CEO), personally dislike Beemium and have never paid for it as a user. Bee (my cofounder and CTO) is the same. We do both pay for Bee Plus, and would pay much, much more for it (were it to cost much, much more). Beemium’s zero-dollar pledge caps feel especially wrong. A Beeminder graph with nothing at stake is hardly a Beeminder graph. And the 50% charity option: see our old post on why we hate anti-charities, which includes a bit about why we don’t think commitment devices with (good) charities as the beneficiary are ideal either.

2. We might kill it eventually

See our old Strategy Memo: Beeminder Is Pledge-Focused. Though more likely Beemium is the principled exception, the thing we would keep charging for even if we made everything else free. Beemium is where the revenue-eating features go. Which brings us to…

3. It’s expensive for us

In particular, Beemium includes quasi-real-time support and the charity option which is partially shirk-n-turked so we’re a bit averse to creating more Beemium users especially if they don’t care much about those expensive features.

4. Pledge short-circuiting is a honey trap

Not that we intended it to be. It just, in our pretty vast experience, is something people think they want but tend to be wrong about. When someone jumps to a huge dollar amount at stake because they’re feeling all gung ho and hard-core, they’re more likely to weasel when they derail. Or freak out and quit when it gets hard. Climbing the pledge schedule like God intended is, empirically, more awesomeness-inducing. But this is admittedly highly individual; if you’re sure that you want pledge short-circuiting, you could be right.

In conclusion

Mostly we could call this a boring business decision. Beemium is expensive for us and expensive for you. Also the cool kids don’t need Beemium. Bee Plus is the coolest plan, in our humble opinion. And look how cheap it looks, next to Beemium! 🤑

Thanks to Alys for encouraging us to blog about this.

Deprecation Warning: Using “Deprecated” To Mean “No Longer Supported”

dreev — Tue, 08 Jul 2025 00:00:00 GMT

Welcome to the Dreevpeeve of the day. I’ve actually seen “deprecate” misused so often that I was worried that, as usual, the prescriptivists would soon have to concede defeat. But so far all the dictionaries are holding firm. This is merely in the category of Common Misconception and so I’m doing my part with this blog post to hold the line. We don’t want another “literally” on our hands here.

First, what it actually means to deprecate something:

To express disapproval of it
To play it down or belittle or disparage it
To withdraw official support for it or discourage the use of it in favor of a newer or better alternative (chiefly of software)
To seek to avert it, or (archaic) to pray against it

Definition 3 is the one people are confused about. But as you can see, they’re all variations on the same theme. Google’s dictionary (which I hate, but that’s a story for another blog) groups the software definition of “deprecated” with their main “disapproved of” definition:

(Chiefly of a software feature) [to] be usable but regarded as obsolete and best avoided, typically due to having been superseded. “This feature is deprecated and will be removed in later versions.”

“Deprecated” just means “disapproved of”

The Common Misconception is betrayed, for example, in this request (edited to protect the innocent) from one of our integration partners:

We’d appreciate it if you can update Beeminder to not use [thingamabob] so that we can hopefully deprecate it.

You’re not hopefully deprecating it, you’ve deprecated it, right there in the first half of the sentence. What you hope is to actually remove the old thingamabob! [1]

Here’s another example I found — someone talking about a thing from OpenAI called the Assistants API:

They promise more to come, and that chat completions will be supported going forward but they plan to deprecate the Assistants API mid-2026.

What OpenAI actually says:

[After the Responses API subsumes the Assistants API] we plan to formally announce the deprecation of the Assistants API with a target sunset date in the first half of 2026.

It’s admittedly confusing, talking about a plan to formally deprecate something. I even found an example of us falling for this ourselves, culled from Twitter back in 2013:

@fitbit you deprecated activeScore a month early! The api is returning -1 as of Sept 28th, not Oct 29 per [rotted link to Fitbit support forum]

I’m almost surely the one who wrote that, and am duly embarrassed. [2] Presumably I was following Fitbit’s misuse of the term.

I polled beemail subscribers on this a few months back. They’re super savvy and mostly didn’t actually recall hearing this kind of misuse. [3] But I predict that now that I’ve pointed it out you’re going to hear it all the time. Please glare disapprovingly when you do.

Footnotes

[1] In the case of deprecating old URLs, we recommend just adding them to an eternal list of legacy redirects and not worrying about how big that list gets. Even over decades it’ll stay perfectly wieldy. We’re old enough to know! The failure to create permanent legacy redirects for old URLs is definitely another Dreevpeeve.

[2] Conceivably it was Bee. She’s the one who was (still is) dealing with Fitbit’s API. But I’ve almost always manned the Twitters and whatnot.

[3] One of those savvy beemail subscribers came up with a steelmanning of at least one of these supposed misuses. You could read “we’ll hopefully deprecate it” as shorthand for going through a formal deprecation process, with the usual warnings in the documentation, culminating in eventual removal of support for the thing. Perhaps this conflation of “deprecation with scheduled removal” and mere deprecation is the source of the Common Misconception.

Freshenable Collections

dreev — Wed, 18 Jun 2025 00:00:00 GMT

Welcome to the next post in our Freshening Series. Before we get to that, let’s review our related posts on clearing and preventing backlogs:

Redqueening, Inbox Zero, Backlogs, and Fluid Dynamics describes Mark Forster’s Backlog Method — “isolate and redqueen” — using lots of analogies.
Control Systems For Backlogs lays out a particular beemindable system for whittling down a backlog.

Freshening is for the case that you have something like a backlog except you have no illusions of ever clearing it. So a collection more than a backlog. Here’s the Freshening Series so far:

Backlog Freshening introduces the core idea: a beemindable system for ensuring nothing in your collection ever gets too stale, focused on a GitHub use case.
Backlog Freshening For Humans by Beeminder Support Czar Nicky broadens the idea beyond tech nerdery, focusing on the example of keeping Beeminder’s help docs fresh. (This continues to be a smashing success.)
Rolodex Goals gets specific about how Bethany and I use a freshening system on our database of contacts, i.e., how we beemind being less neglectful of our friends and family.
This post, Freshenable Collections, in which…

…we start with a partial list of other collections we have ongoing freshening goals for on Beeminder, in case anyone’s inspired. Some of these may be worth their own future posts; just ask if you want details.

Beeminder’s Operations Manual
Beeminder blog post ideas and drafts
My and Bethany’s relationship repo aka conversation stack
Post ideas for my other blogs/newsletters
Beeminder’s main bugtracker
BeemiOS’s bugtracker
Beebrain’s bugtracker
Omnitask aka my personal tasks/notes (yes, I use a collection of gissues the way other people use Notion or Obsidian)
Math puzzles (but actually this is currently just part of Omnitask — gissues tagged PUZ)
Beeminder’s Tips of the Day that we auto-include in bot emails
Bibliophilia aka books I want to read
Household projects

I’ve been on a tear lately with freshening goals, and my protocol to bootstrap them has been like so (this parallels the “Phase 1: Migration” section of the Rolodex post):

I notice I have something like math puzzles or AGI Friday post ideas that I want a freshenable collection of.
Create the empty collection, which for me usually means creating a repo in GitHub.
Create the freshening goal on Beeminder.
To get the +1, look for an existing puzzle or post idea, wherever it currently lives, and migrate it to an item in the new collection.
If all the pre-existing stuff is already migrated, then make an epsilon improvement to the oldest item, i.e., freshen it. That’s where all +1’s come from in the steady state.
Creating new items doesn’t give you +1’s — freshening is all about the old stuff.

The short version of all those steps is that migrating pre-existing items to your collection counts as freshening.

Sidebar: Blog Dogfood

I’ve been doing this for Beeminder blog post drafts/notes/ideas for over three years. At the moment there are 135 future potential blog posts in the works! Most are extremely low-fractionally-baked ideas and probably most will never and should never see the light of day, but it’s really nice to have a way to curate the collection sanely and not just have it be a black hole.)

Finally, thanks to brainstorming help from many of you, here’s a meta-collection of ways to maintain a collection that all support the critical feature for freshening: a way to sort or query for the least-recently-edited item.

(Optional additional feature: a way to mark items as snoozed, meaning they’re immune from freshening. Which means your query/sort-by-stalest needs to be able to exclude snoozed items. For example, if you’re using a kanban tool like Trello, you can manually drag items to the bottom of their column when you freshen them, and move them to another column to snooze them.)

Without further ado, the meta-collection:

GitHub Issues (I use this for evvvverything (and maybe this is not crazy?))
The Things 3 app on iOS, with hackery from Philip Hellyer
Kanban apps like Trello or GitHub Projects (see parenthetical above)
A spreadsheet with a “last freshened” column
Plain text files in a directory sorted by last-edited
Emacs Org mode, probably
Airtable, almost surely
Obsidian with the Dataview or Tasks plugin
Taskwarrior
Mark Forster’s Autofocus on paper
Any collection of paper, like sheet music for songs in one’s piano repertoire

Thanks to Alys, Marcin Borkowski, lanthala, Brent Yorgey, and Robert Perce for contributing ideas for this post.

Time Stream ♥ Beeminder: A New App by a Long-Time Beeminder User

Mary Renaud — Thu, 05 Jun 2025 00:00:00 GMT

We’re thrilled and honored to welcome back to the blog the esteemed Mary Renaud, PhD. Though for now, only iPhone people can reap what Mary has sown…

I’m excited to officially announce my first iOS app: Time Stream!

What is Time Stream?

Time Stream is a routine management app that adapts when your day doesn’t go as planned. Routines will wrap around things that get scheduled into your day or adjust when something takes longer than expected.

I’ve had a lot of trouble with rigid scheduling apps and calendar templates that broke in the face of life’s little interruptions or when my schedule changed a little… So I built something that lets my routines kind of ride the current of the day.

You can create personalized morning, evening, and custom routines, each with activities with estimated durations. The app estimates when each routine will be finished and those completion time estimates update as you go. When the inevitable happens and things change (something runs late or gets interrupted or procrastinated), it automatically recalculates when the other activities and routine can fit in, so you can see exactly what you’ll still have time for, and you can adjust without losing track or spending a bunch of time fiddling with changing today’s plan. It also shows you exactly how your routines will wrap around your calendar events, and will give you visual cues if your routines are going to run past important critical times in your day, like when you need to leave for work. And it keeps me honest about whether “I’ll just do that later” is actually feasible, because I always know how much buffer I have in my day between my routines and my events or important times thresholds.

What’s The Benefit of Adding Beeminder Goals to Time Stream?

As a Beeminder user of over 12 years, it was critical to me that it work with Beeminder. Beeminder has had a huge effect on the shape of my life. But, given I tend to beemind a lot, I’ve also had a hard time being able to visualize when my day was getting off the rails and my infinitely many Beeminder goals wouldn’t all be accomplishable if I didn’t get moving RIGHT NOW. (I used to use the fantastic waterfall method, but things changed a little in my day’s structure, making it harder to make that work.)

Each activity can be linked to a Beeminder goal so that, when you check it off in Time Stream, it adds a +1 datapoint to that Beeminder goal immediately (or, as of a recent update, if you check it off while offline, it will add it as soon as you’re back online).

And since I’ve never seen an over-engineered system I didn’t love, I also added “critical times” which are visual representations of thresholds in the day that I want to be aware of. We can use this for things like making sure morning routines are done before the “critical time” to leave the house or that evening routines don’t extend past bedtime. Beeminder users can also, if we want, set the deadlines for activities in a given routine to a specific deadline, and then set that deadline as a critical time in the app. The app will then flag if a routine connected to that critical time is going to go past that deadline, so you know if you’re at risk of derailing any of the goals in that set.

Setting Up Your Time Stream + Beeminder Goals

In the Time Stream app, go to Settings > Integrations > Beeminder. You’ll need to authenticate with Beeminder. This does not get sent to or saved to any Time Stream servers or anything (nothing does). Then, select which Beeminder goal should receive +1 data from Time Stream activities by selecting the goalname and the activity.

That’s it. Done.

You can connect more than one activity to a goal. (I use this, for example, because my Weekend routine and Weekday routines are different, but some of the activities overlap, and I don’t want two different Beeminder goals for the Weekday & Weekend versions of that commitment.)

Caveats

A New App from a New Developer: I’m brand new to developing for Apple devices, so there are still a few bugs being worked out. I’m fairly confident there are no bugs left in the Beeminder integration, though. Since those are potentially costly to users, they’re top priority. The price will go up between 50% and 100% down the line but, for now, I’m keeping it at the current price ($8 USD), in case there is the occasional bug that might be frustrating to users as I learn.

Upgrades won’t be free, but there’s no subscription cost: There are no server costs, so I’m going with a no-subscription model. You buy the current version and don’t have to pay for it again. When major updates happen, you can choose between sticking with what you’ve got, or upgrading. Bug fixes will be included for two years for each version (as long as we’re still in business!) but new major versions or fixing older version when new a iOS break things won’t be.

In the End…

Building Time Stream has been my own answer to years of life’s unpredictability clashing with my “perfectly” planned routines and Beeminder goals.

If you, too, know the pain of frantically calculating whether you can still hit all your goals when your day goes sideways, maybe this will be helpful for you too.

Want to get started with Time Stream? Download it at time-stream.app.

Rolodex Goals

dreev — Wed, 21 May 2025 00:00:00 GMT

We have a pair of blog posts from 2022 called “Backlog Freshening” and “Backlog Freshening For Humans” that we’re still very pleased with. To recap, say you have a backlog of something — tasks, emails, blog post drafts, bugs, you name it — that it’s not realistic to clear. (If you do need to clear it, see “Control Systems For Backlogs.”) Which is to say, you have a collection of items. And you don’t want the collection to be the place where items go to die. Enter freshening, in which you sort the collection by least-recently-touched, and then you beemind regularly revisiting the collection, stalest item first. Every +1 that Beeminder demands is satisfied by making progress on or improving in some way that stalest item. You can also think of it as doing spaced repetition on your collection.

It turns out we have a lot more to say about this, most of which we’re saving for a future post about Freshenable Collections generally. Today we want to share a particular example we think this works great for: your rolodex.

Oh, sorry non-ancient people, that means your database of contacts. As in friends and family and colleagues. Fellow humans with whom you have relationships.

Being weirdos, Bee and I keep ours in a GitHub repository as a set of gissues (er, GitHub Issues — we call them “gissues”; did we mention we’re weirdos?). You need something that lets you sort or query by least-recently-updated, and gissues are such a thing. So here’s the protocol we use.

Phase 1: Migration

Assuming you don’t already have your address book in a collection that’s sortable by least-recently-touched, you can make the migration part of the Beeminder freshening goal. Here’s how that looked for us:

Export your Google Contacts to a big text file
Create an empty private GitHub repository called “rolodex”
Create a freshening goal on Beeminder (also “rolodex”)
When Beeminder demands its +1, satisfy it by moving a contact from the text file into a newly created gissue for that person

In other words, migrating pre-existing items to gissues counts as freshening.

Note: In the event you someday make a new friend you should certainly make a gissue for them, but that doesn’t count as a +1 on the freshening goal. Freshening is all about the old stuff. Eventually the new friend will become the stalest gissue and you’ll freshen them then.

Phase 2: Steady State

Ok, suppose there’s no one left to migrate. You keep right on going with the same Beeminder goal, just that how you get those +1’s changes. Now you get your +1 by pulling up the stalest gissue and freshening it, i.e., updating it so it’s no longer the least recently updated one.

Things that change the updated-at date on GitHub include editing the description or title, changing tags, adding or editing a comment, and assigning the gissue to someone.

(That last one matters for Bee and me, sharing a rolodex. She can assign me people on my side of the family, for example. Then when sorting by least-recently-touched, we can also filter out gissues assigned to the other person. Also we’re currently doing this with individual goals but this is a great use case for group goals.)

Emoji reacts and external references don’t seem to update the timestamp.

That said, and possibly especially because there are two of us beeminding the same list (consistency! accountability!), we like to have some guidelines for what counts.

Adding or updating contact info (addresses, phone numbers, etc)
Adding any notes about birthdays or children
Editing keywords or labels/tags (maybe you want to be able to search up all the lawyers or nurses you know?)
Adding notes about your history together
And of course: actually pinging the person

Of course, contacting a person IRL doesn’t update the timestamp of the gissue, so either leave a comment with a brief description of how/why you pinged them, or add a list of dates you talked.

What if there’s someone whose entry in your rolodex has reached quiescence/efflorescence and you don’t have it in you to reach out to them? (Hopefully this isn’t because it’s been so long that you’re embarrassed to, but that’s all the more reason to get a rolodex freshening goal up and running!) Whatever the reason, there’s one other option for freshening them, which we call snoozing them. Technically, that means applying a special label that you exclude when you query for the stalest contact in the rolodex. Pragmatically/morally, it means admitting that you’re ok with that person going out of sight and out of mind if they don’t initiate contact themselves. They’re still in the system, still searchable, but you’re taking them out of your freshening rotation. Probably permanently, since Beeminder will never prompt you to ping them again.

Dialing It In

I’m at 316 freshenings and counting, dialed to 0.29 per day. It doesn’t really matter how steep your bright red line is but one way you might want to think of it is that, if you dialed it to 1 per day and you have, say, 350 unsnoozed contacts, that’s almost a year between freshening each contact. If you have 700 contacts, maybe commit to 2/day (or 1.9) if you don’t want to neglect anyone for more than a year. If you’re ok going a few years between freshening any given person, well, you can do the math. (Namely, dial the bright red line to $\frac{n}{d}$ where $n$ is how many contacts you have and $d$ is your preferred staleness cap in days.)

We’ve been evolving this system for three years now and hope others can get value out of it as well, or take inspiration from it. We’ll end with Beeminder superuser Lanthala’s description of her rolodex system, since it’s more normal-person-friendly and generally much simpler than ours:

I keep a spreadsheet called Friends & Contacts, where each person is a row. One of the columns is “last pinged”, and I usually keep the sheet sorted by that column so I can see who has been neglected. It is very low lift and high usefulness, especially for former work colleagues who I’d normally lose touch with. And I have a Beeminder goal to ping a person on my spreadsheet about once every 10 days, which keeps me checking the sheet.

Beeautiful!

Maximal Motivation At Minimal Cost: A Calculus Problem

dreev — Wed, 07 May 2025 00:00:00 GMT

The golden mean of Beeminder: If you never derail, dial up your bright red lines — if you’re derailing all the time, dial them down. Thanks to felixm and others in the forum for inspiring this post.

The previous blog post in our series on how derailing is good-actually (see the sidebar for a review) ended thusly:

The (meta) goal is to be pushed to do as much as possible — or whatever maximizes the motivational value you get from Beeminder — at minimal cost.

Shall we do math to that?

Backing up, we’ve been making a gradual shift in Beeminder philosophy from the early days. We used to lean in to the punishment framing, where pledge payments were penalties for falling short of your commitments and quantified how bad of a person you were. Just kidding. But we’re now pretty serious about treating it as the exact opposite of that. The pledges you commit towards your goals are helping you make progress on your goals, even when you pay them.

Sidebar: The “derailing is good-actually” series

“Bee Nice To Yourself” (2014) planted the seed for the rest of this list. It’s our cofounder, Bee, pointing out that treating oneself to a derailment now and then is ok. It even helps clarify the value you have for various behaviors. Bee asks herself, for example, “how much do I want to not go for a run right now?” If it’s less than $10, she’ll run. If it’s more, she’ll pay. And if she gets up to $90 at stake and still does’t want to go running, she’ll re-evaluate why she’s trying to get herself to run in the first place.
“Derailing Is Not Failing” (2019) argues that Beeminder revenue is proportiional to user-awesomeness — that pushing yourself hard enough that you sometimes derail is great for us and great for you. You don’t know how much more you could be accomplishing unless you sometimes find your limits!
“Paying Is Not Punishment” (2022) is a prelude to the subsequent announcement of No-Excuses Mode and advocates for a generally less excuse-making and more results-oriented mentality with Bee minder.
“Derailing It Is Nailing It” (2023) turns the concept up to eleven and gives it a positive spin.
“Beeminder As Your Personal Pigouvian Tax” (2024) argues for a reframing from a punitive to a taxing mindset, where you treat Beeminder’s stings as a behavior-shaping tax and accept that some derailments are inevitable. You can view that tax as the cost of the service Beeminder provides: nudges or rumble strips keeping you on track.
This post (2025) refines the derailing=good advice. The ideal amount to derail on Beeminder is not never and it’s not constantly. You don’t need to solve a calculus problem but do dial in the slope of your bright red line and your pledge cap to find your happy medium by feel.

Why not not maximal motivation at zero cost?

This is theoretically possible! Nick Winter advocates for it in his 2013 post, Spiraling Into Control. And see especially his classic book, The Motivation Hacker. We’re in awe of Nick but we don’t think this is sustainable (for anyone but Nick?). Or at least that it entails non-monetary costs, like stress and elaborate setup. Nick had to literally commit to writing a book about it to make that all work.

The extremes

If we don’t think the best of both worlds is realistic, how do we optimize this? Let’s first consider two extremes.

At one extreme is a total lack of ambition: A flat red line, no risk, no cost, nothing accomplished beyond what you would’ve accomplished with no Beeminder graph at all.

At the other extreme is maximal ambition: your red line is as steep as is humanly achievable, your pledge is at the edge of affordability, and so you get the maximum accomplished at high cost.

Neither of those sound great and they suggest a happy medium in which you accomplish a lot and derail sometimes but not too often.

So the final section of this post is just for fun. We don’t actually have to do math to this. In reality, you can and should dial in your sweet spot by feel. If you haven’t derailed a goal for months, make it a bit harder. If you’re derailing every week, ask yourself: is it that the goal is too hard, or is it too cheap to be motivating? Adjust the stakes or the steepness of the bright red line accordingly. If it’s stressing you out, drop the pledge and/or dial down the commitment. Maybe keep reassessing week by week.

The calculus problem

Ok, but pretend you’re a robot, or homo economicus, and can model everything about your life with a nice clean equation. (Even I wouldn’t actually do this; like I said, this is just for fun.) We’ll assume that:

$s$ is your stakes
$r$ is the daily rate of your bright red line — the amount of work you’ve committed to do per day
$R$ is the maximum possible value for $r$
$v$ is the value you get per unit of work accomplished
$u$ is the probability of an unavoidable derailment (we’re assuming No-Excuses Mode here)
$\lambda$ and $\alpha$ are tunable parameters determining how steeply your risk of derailing drops as your stakes ($s$) go up and how much it increases with your rate ($r$).

So we’re treating $v\cdot r$ as your total daily motivation, assuming you don’t derail. Next, assume your daily probability of avoidably derailing is (why not?) like so:

$$ d(r,s) = \left(\frac{r}{R}\right)^{\alpha} e^{-\lambda s}. $$

So your total probability of derailing is $d(r,s)+u$. Now we can work out your expected motivation per dollar:

$$\text{MP\$}(r,s)=\frac{\text{motivation gained}}{\text{dollars lost}}.$$

Which is:

$$ \frac{(1-d-u)\cdot v\cdot r}{(d+u)\cdot s}. $$

Inserting $d(r,s)$ and simplifying yields:

$$ -\frac{r v\left(\left(\frac{r}{R}\right)^\alpha+(u-1) e^{\lambda s}\right)}{s\left(\left(\frac{r}{R}\right)^\alpha+u e^{\lambda s}\right)}. $$

That’s strictly concave in $r$ if $0<\alpha<1$, so a unique interior maximum exists. Finally we’ve found our calculus problem! For given stakes, the rate that maximizes motivation per dollar is the one satisyfing

$$ \frac{\partial}{\partial r}\text{MP\$}(r,s)=0. $$

Differentiation, whee:

$$ -\frac{v \left((\alpha+2 u-1) e^{\lambda s}\left(\frac{r}{R}\right)^\alpha+\left(\frac{r}{R}\right)^{2\alpha}+(u-1)u e^{2 \lambda s}\right)}{s\left(\left(\frac{r}{R}\right)^\alpha+u e^{\lambda s}\right)^2} = 0. $$

And algebra, whee:

$$ r^{\ast}(s) = 2^{-1/\alpha} R \left(-e^{\lambda s}\left(\pm \sqrt{4 \alpha u+(\alpha-1)^2}+\alpha+2u-1\right)\right)^{\frac{1}{\alpha}}. $$

Here’s how it looks in Mathematica:

So there you have it, gruelingly Goldilocksed. Under a blizzard of contrived assumptions, you now know that when your stakes are at $90 you should dial your bright red line to 29.9% of the maximum you can do, in order to maximize motivation per dollar spent on derailments.

Beeminder ♥ Maayot

Maayot — Tue, 22 Apr 2025 00:00:00 GMT

We’re excited to officially announce our newest integration partner: Maayot! We’ve even got the Maayot folks here in their own words to tell you about it.

Want an extra push to stay consistent with your Chinese reading? We (Maayot) are thrilled to announce our new integration with Beeminder, a powerful tool that helps you stick to your goals using commitment pledges. You can now:

Track stories you read directly in Beeminder, and graph out your progress.
Set goals for yourself, such as to read your daily story, every day; or a couple of times a week.
Add real accountability with Beeminder’s pledge system to keep you motivated: Pledge a certain amount of money that will be debited by Beeminder if you don’t meet the commitment you set.

Keep your learning sharply on track and build lasting habits.

As a word of advice, if you find committing to a story a day too much, you can enter, for example, “5/7” in Beeminder to commit to read 5 stories a week.

Us bees chiming in again. If you’re sold, head to beeminder.com/new, pick the Maayot icon from the list of integrations, and follow the prompts to connect Beeminder to your Maayot account. Or, here, have a button:

小心蜜蜂

(See also the full documenation of the Maayot integration in our help docs.)

Image credit: The MET

Book Brigades

dreev — Wed, 09 Apr 2025 00:00:00 GMT

This is a sequel to “The Power of Rearticulating Insights in Your Own Words.” If that post convinced you, this post is a way to turn all that up to eleven. Also conceivably related is our latest autodata integration, Postminder, for beeminding forum posts. The extremely curious can check out the forum thread last year where these ideas originated.

You know how if you just passively read a book you eventually forget literally everything you briefly learned? Possibly including the fact that you ever read the book at all, after enough years? Book brigades aim to solve that. A book brigade is a small group of very like-minded people collaborating on getting a book read and understood by taking turns reading sections of it and recapping for the others. It’s powerful because:

you can get a book loaded into your head from only reading a fraction of it,
the part where you have to explain it to others gets it cemented in your own head,
the part where someone similar to you explains it in their own words means skipping all the fluff and obvious parts, focusing on the new information, and
you end up with a nice Cliff’s notes of the book from all the summaries (see the appendices below for examples).

Also social accountability! Of course we’re making a few assumptions here:

The book isn’t fiction, where the point is to get absorbed in the story — you just want the information transferred from the book to your brain
The others in the book brigade are similar enough to you to know what to highlight in their summaries (and what’s safe to skip)
Everyone is committed and diligent (Beeminder to the rescue here)

I’ve found it a nice collaborative exercise that leads to good discussions, like a book club, but better.

Last year I formed a group of 9 other Beeminder superusers to read Thinking in Bets, by Annie Duke. Then Bee and I did a two-person book brigade for the ur-self-help book, How To Win Friends and Influence People, by Dale Carnegie. You can see the notes we generated for both of those below.

But first let me tell you how we set these up. The central piece is creating a group goal on Beeminder. That’s a single graph that a group of people can all add data to. Also if the goal derails, everyone gets stung (charged money).

That also means the group has to pick how many pages per day to commit to reading. We voted on that like so:

Bee and I read Carnegie somewhat slower — just a page a day, so it took the better part of a year. We called the goal for the Annie Duke book “dukeitout” and it looked like this at the end:

We called the goal for the Dale Carnegie book “dailydale” and it ended up looking like this:

No derailments at all on dukeitout (so much social accountability!) but Bee and I derailed dailydale once. Total cost of finally getting that book read: $5 (each). Cheap!

Other logistics we settled on:

We used Odometer goals on Beeminder, so you can enter your current page number rather than the number of pages you read.
No weekends-off, just get into the green by Friday if you want that.
When it’s your turn, read till a natural stopping point. This was the end of a section for Duke and the end of a chapter for Carnegie.
Tell Beeminder the last page you finished — both reading and summarizing — before passing the baton.
When in doubt about how far to read, or anything else, talk to the person you’re passing the baton to.
No extra time for summarizing/writing — plan ahead to leave time for that when you approach the end of your section.

I think the biggest lesson was to be less thorough than you’re inclined to be, at least among the hyper-conscientious group we had. The temptation for us was to describe everything the author said, point by point. The other extreme would be just giving a gestalt impression of a chapter or describing what happened to stick out in your memory. Instead, aim to compose something tailored to the audience (i.e., each other). Consider each point the author makes and deliberately include it or not according to your own assessment of whether the group will benefit from hearing it.

If you’re inspired to try this and looking for book ideas, here’s the voting we did for which book to brigade:

Appendix: Book Brigade notes for Thinking in Bets by Annie Duke

NB: These notes are by 9 different people, all concatenated with no indication of who wrote what. The best part is all the discussion in between the summaries but I’ve omitted most of that for fear of this ending up longer than the book itself.

Introduction

Duke made millions playing professional poker and thinks it’s a nice microcosm for studying human decision making. All decisions can be (wait for it) thought of as bets.

Here are the goals of the book in, as Dynomight says, superior list form:

To avoid common decision traps, learn more effectively from errors, and keep emotions out of it. (This is standard rationality community fare. Except the last one, ironically. The rationality canon emphasizes the value emotions can have for optimal decision-making. Maybe this will come up later.)
To “build and maintain pods of fellow truthseekers to improve our decision process”. Hi friends!
To “recruit our past and future selves to make fewer emotional decisions”. No idea what that means yet but bee-dar is buzzing.

Duke emphasizes that we’ll still make mistakes, succumb to emotion, and lose – since we’re humans. Just that we can do that less and less, and that our improvement compounds. (This is the inspiration for the name Less Wrong.)

Her last point is that everything comes down to exactly two things: the quality of our decisions and luck. Thinking in bets means recognizing the difference.

Chapter 1: Life Is Poker, Not Chess

Pete Carroll and the Monday Morning Quarterbacks

Duke tells a sports story about a football game to illustrate “Monday Morning Quarterbacking,” an American metaphor for second-guessing a decision after it’s been made.

This (American/gridiron) football example might be confusing to those unfamiliar with the game. Pete Carroll, the coach of the NFL team Seattle Seahawks, made a controversial decision in the Super Bowl, the annual championship game. They were very close to scoring a touchdown, which could win them the game. Carroll decided to have the quarterback, the player who executes the coach’s plan when play begins, pass (throw) the ball to another player instead of handing it to another player.

The result of this decision was very unlikely: the pass was intercepted, or caught, by the other team. According to Duke, in the previous fifteen seasons, interceptions like this one only happened 2% of the time.

The press and most viewers thought Carroll’s decision was very bad, although contrarian journalists who think about statistics argued that it was sound. Carroll also defended his choice. Duke uses this case as an example of “resulting,” using the outcome of a decision to influence one’s judgment of whether it was good or bad. She says that making this kind of error is a very common habit, calling it “our tendency” and implying that it’s basically a universal human failing.

The last paragraph of the section is a bunch of questions, promising answers to why people tend to make this kind of mistake and how to avoid it.

The 2% statistic had me wondering if interceptions would really be that unlikely in that particular condition: are they more likely than 2% when the opposing team is fired up to win the Super Bowl?

DIALOG:

Ooh, I didn’t know this verb — “resulting” — and am instantly in love. Sounds like it’s Duke’s coinage. Another existing term is outcome bias.
She says it’s a term poker players use, so probably not her coinage? I prefer “resulting” to “outcome bias,” because the meaning is more obvious.
Another term I have heard, from somewhere in the poker lineage, is “results-oriented thinking”

The Hazards of resulting

People basically always judge their decisions by the results rather than what went into the decision. The don’t identify lucky results as bad decisions or vice-versa.

She asks CEOs and business owners to describe their best and worst decisions in the previous year. They have without exception presented their best and worst results instead.

Specific example: a CEO identified his worst decision as firing a president who it turned out could not be replaced even at the same quality level. He felt he had made a grave mistake, and his decision making after that incident was negatively impacted.

When investigating the decision process, he had considered and tried enough things that the group of business leaders all though he had been correct to think that he could likely find someone better and that it was not a bad decision. (The main steps of the process were concluding that poor company performance was due to poor leadership, coaching president to attempt to improve leadership skills, and considering past company experience hiring people at high levels and also expected current available talent.) The CEO was merely experiencing hindsight bias: the tendency to believe that known outcomes could have been predicted beforehand.

Analogy: if someone drives home drunk and it works out, they don’t conclude that it is a good idea to drive drunk. No one would do that, but they do all kinds of similar things such as this case.

DIALOG:
This is going beautifully. Thanks, y’all!

I see the book does not contain the terms ex ante and ex post (Latin for “from [the perspective of] before” and “from [the perspective of] after”). I find these highly useful for avoiding resulting. Like for the CEO anecdote you can clarify that the firing was a good decision ex ante but a bad decision ex post. Of course you can’t make decisions ex post so that just means it was a good decision, full stop.

I guess the widespread confusion about this is why it’s worth being technically redundant and saying “good decision ex ante”.

Another way I like to drive this point home: Wearing a seatbelt has so far been a total waste of time. We’ve never crashed! But ex ante it’s been prudent and we should keep doing it.

Quick or dead: our brains weren’t meant for rationality

The author begins the section by citing “a number of excellent books” including Dan Ariely’s Predictibly Irrational, so not a great start. But then again, the citation is for nothing more than the general proposition that humans don’t always make perfectly rational decisions, so sure, I guess.

Type I errors were less costly to our ancestors than type II errors, so we evolved to overreact. Better to make the sort of mistake where you run away from a lion that doesn’t actually exist than to mistakenly not run away from a lion that does.

Our brains can be modeled as having two subsystems, System 1 and System 2, the reflexive mind and the deliberative mind. System 1 is more powerful but less flexible, so while we can’t use it for everything, we should use it for everything it’s good at. It’s a mistake to use the expensive and slow System 2 for stuff we can do with System 1.

System 1 functions on the basis of “the perfect is the enemy of the good”. The point is to be cheap and efficient. Perfection is unnecessarily expensive, better to use heuristics which are mostly right. Being sometimes wrong is not the end of the world.

One heuristic is to round off probabilities to certainties. If you calculate that there’s an 80% chance you are about to be ambushed by a lion, you’ve done something wrong—the extra effort involved in figuring out the exact probability is wasted, as whether the chance is 80% or 100% or 30% you’re going to run away just the same.

Point is: Heuristics are awesome, cheap, and effective, so system 1 has a huge arsenal of them, and uses them freely. This is a good thing; life wouldn’t be possible otherwise.

But this means we’re not in the habit of thinking in probabilities; which means that we’re out of practice, as it were, when we try to do so. Poker is a good way to get that practice.

DIALOG:
[endless discussion of how much of a charlatan and fraud Dan Ariely is, which I’m tempted to include but it’s just too much so I’m mostly omitting discussions from here on]

Two Minute Warning

In poker, speed is of the essence - you have to make multiple decisions, all with significant consequences, in a short time frame. The rules of the game mean you can’t slow down and think about your decisions – taking too much time will result in another player “calling the clock” on you.
The goal is to get our “reflexive” minds to execute on our “deliberative” minds’ best intentions.
Solving the problem of “how to execute” is the most important thing. “How to execute,” meaning, how to execute on the intention of the deliberative, (more) rational mind, while playing in a high-stress environment, at the speed expected. Learning to get better at poker will involve doing post-mortems of games, separating the signal from the noise, analyzing decisions, and guarding against resulting.

Dr Strangelove

This section talks about John von Neumann (apparently also a poker player), one of the inspirations for Stanley Kubrick’s Dr. Strangelove.
John von Nuemann, in addition to immense contributions to Math, and a whole lot of other achievements, was the father of game theory. He co-authored Theory of Games and Economic Behavior, a classic book on the subject. Game theory, of course, is hugely influential, revolutionizing economics, and influencing other fields like the behavioural sciences. Game theory forms the basis for the study of decision making, including the challenges of hidden information, luck and other variables.
The author says John von Neumann modeled game theory on a basic version of poker.

Poker vs. chess

John von Neumann, father of game theory, was asked by Jacob Bronowski (notable intellectual and author of “The Ascent of Man”) if game theory is like chess. Von Neumann replied:

“Chess is not a game. Chess is a well-defined form of computation … In theory there must be a solution, a right procedure in any position … Real life is not like that. Real life consists of bluffing, of little tactics of deception, of asking yourself what is the other man going to think I mean to do. And that is what games are about in my theory.”

“Real games”, therefore, are made under conditions of uncertainty, risk, and occasional deception. Chess contains no hidden information and very little luck. Poker, in contrast, is a game of incomplete information. It is a game of decision making under conditions of uncertainty over time, where valuable information remains hidden, and there is an element of luck in any outcome.
Life is much more like poker than chess. You can make the smartest, most careful decision, and still have it blow up in your face.
Incomplete information is a challenge not just for decision-making, but also for learning from past decisions. But if we want to improve in anything, we have to learn from the results of our decisions. In chess, you can’t legitimately say “I played perfectly, but caught some terrible breaks!”; in poker, you hear that a lot.
This is why Von Neumann and Morgenstern based game theory on poker: uncertainty is key, and making - and learning from - our decisions starts with deeply understanding this.

A lethal battle of wits

Consider the following situation. Someone walks up to you and asks “I flipped a coin and it landed heads four times in a row. How likely is that to occur?”
You may be tempted to consider the probability of this observation. The probability of a coin landing heads is 1/2. The probability of this happening four times in a row is 1/2 ^ 4 = 1/16.
While this line of reasoning seems very reasonable, it also showcases the perils of underestimating the amount and the effect of what we don’t know.
In this case, the question we received didn’t specify many important details, which can undermine our calculations. Did this coin have 1/2 probability of landing tails? (Maybe it has two faces with heads. It could also be an unfair coin.) Is the person throwing the coin skilled in influencing the probability of the outcome?
- (There could be incidental factors that could help us. For instance, a much larger number of flips could help us evaluate the unknowns regarding the coin itself.)
Life puts us in many situations like this question when it comes to learning lessons from our experience. We get to make (and evaluate) big decisions like buying a house very rarely.
In such situations, realising we don’t know enough to be confident about our conclusions is valuable. There’s a correct answer to the question about the four coin flips: “I’m not sure how likely this is.”

The title of the section is about a scene from The Princess Bride. Two characters play a game of life and death. Character A leads the other to make a grave mistake: assuming that they have equal probabilities of dying in this game and that it is 50%. Under these assumptions, character B proudly “outwits” character A. Only to pay the price of his wrong assumptions with his life.

“I’m not sure”: Using Uncertainty to Our Advantage

Society teaches us that saying “I don’t know” is bad. In school, we get zero
points if it’s our answer to a question, and in a business context, it could be
interpreted as unhelpful or even evasive.
As we have learned, a great decision is not defined by its outcome (that
would be resulting). Instead, a great decision is the result of a good process,
which must include an attempt to accurately judge our own knowledge.
Therefore, to make good decisions, we need to embrace saying “I don’t know”
and “I am not sure.” (“Our strength as rationalists is our ability to notice
our own confusion.”)
Good poker players and decision-makers embrace uncertainty. They make
decisions based on the information they have, their experience, and their best
guesses about the probability of different outcomes.
Sometimes, even the best choice might have a low probability of a good
outcome.
All this doesn’t mean there is no such thing as objective truth. Our brains
don’t perceive the world objectively, but our goal should be to perceive it as
objectively as possible.

Redefining wrong

Most people are idiots.
If you say X has a 75% chance to happen and not-X happens, they will say you were wrong.
In addition to poker, gives brexit predition and polls before donald trump election as examples of this. Yeah, I was there, I remember being pissed off at the public discourse about this too.

I can’t emphasize enough how this section is really just trying to get the meaning of “don’t result” through your skull. If you already completely accept it, then you can indeed just skip reading it as you are doing. If you aren’t 100% sure you’ve drunk the kool-aid, it’s probably worth reading through the chapter and letting it sink in.

So yes, just because things go badly doesn’t mean you were necessarily wrong:

Maybe we made a decision out of many bad choices, none of which were likely to go well
Maybe we were missing crucial information, so we had no hope.
Maybe we took a justifiable risk for a huge payout, and just didn’t get lucky.
Maybe we were simply unlucky.

The poker example is easy. In real life, it is hard to “redefine wrong” because we will never actually know for sure whether we had assigned correct probabilities to events.

Finally, we are implored to also give up on feeling “right” when things happen to go our way.

Chapter 2 Thirty days in Des Moines

This is purely a fun anecdote, about the time Las Vegas legend John Hennigan bet his friends $30k he could last for a month in Des Moines, Iowa – chosen as the diametric opposite of Las Vegas. He lasted about 2 days, and paid up. Apparently Des Moines is very boring. But it wasn’t just a stunt. He was genuinely unsure if he might prefer a folksy town and seeing actual sunlight.

We’ve all been to Des Moines

Duke’s point is that any time you’re making a big life decision, you’re in a pretty similar situation to the protagonist of that anecdote. Say you’re deciding whether to move across the country for a new job. Unlike Johnny World (as he’s known, because he’ll bet on anything in the world), you don’t have $30k in cash riding on the outcome, but that’s actually small potatoes compared to the rest of your stakes. Like how much money you’ll make at your new job. To say nothing of the bet you’re making about how you’ll fit in to a new community, how much you’ll turn out to like or hate your new commute, etc etc.

(I am very much the preached-to choir here, in terms of thinking in bets.)

Or as an employer offering a job, you’re facing similar tradeoffs: making a high enough offer vs spending too much money. So, yes, all decisions under uncertainty are fairly charactized as bets.

Also betting is a tax on bullshit. Duke doesn’t use that phrase but expresses the same lovely sentiment. Put your money where your mouth is. Make the uncertainty explicit and explicitly factor it in to the decision-making.

The central claim of the book is that we can make better decisions, including mitigating human biases, by explicitly casting those decisions as bets.

All decisions are bets

In this section, Duke presents a thesis about human decision-making. According to Duke’s definition, every time we make a decision, we are making a bet. She encourages us to widen our definition of a bet to include every instance of taking a risk in uncertain conditions.

Laying out the qualities that she believes make something a bet, Duke comes up with the following criteria: choice, probability, risk decision, and belief, and she proceeds to argue that all decisions are bets because they share these aspects. I feel as though it’s kind of gimmicky to shoehorn all decisions into “bets” - if every decision, and we constantly make decisions, is a bet, isn’t “bet” kind of meaningless? Nevertheless, this is Duke’s contention. According to her thesis, even long-term decisions made in relatively low-pressure environments, like parenting choices, are bets because they share these aspects. She closes the section with the sentence, “Everything is a bet.” If everything is a bet, is anything a bet?

Most bets are bets against ourselves

With the title of this section, Duke bolsters the argument that everything (every decision) is a bet, anticipating that the reader might object to the thesis by objecting that a bet requires two participants. Even with one individual participating, there can be a second participant: the same individual separated by time and choice. Duke asserts that the second party to a bet against oneself is “all of the future versions of ourselves that we are not choosing.” This passage sounds a little like an alternate reality speculative fiction: we’re making bets against our hypothetical future selves. To ground this idea a little more, we can think not of imaginary selves but of missed opportunities. Like John Hennigan, with every decision, we are giving up opportunities that are closed off by that decision.

When we make any decision, we confront the uncertainty that the choice we’re making is the best one. At the end of the section, Duke returns to talking about poker. I have the idea that we’re heading toward a call to action. In returning to poker, Duke may be heading towards introducing a way to use the “everything is a bet” framework in everyday life. Good poker players have a strong awareness both of the probability that their decision will result in a good outcome and in the stakes of their bets. I’m envisioning Duke explaining that we can apply probability to ordinary decisions and that this application will help us get better at making those decisions.

Our bets are only as good as our beliefs

This opens with a fictional scene from a sitcom where someone tries to run a turkey giveaway promotion by dropping live turkeys out of a helicopter. It is a disaster. The punchline is that he believed that turkeys could fly. We then revisit the anecdotes we have covered, and note that the bets that people made were based on their beliefs. The takeaway is that we need to (1) improve the accuracy of our beliefs by taking in information and experience and (2) notice what thinking patterns take us astray and develop strategies to be more open-minded, objective, and accurate.

Hearing is believing

Short summary:

We default to believing things we hear, only occasionally verifying after forming the belief. This makes sense evolutionarily; prior to humans, beliefs were formed based on direct sensory evidence. Worse, even when we receive information to the contrary, we tend to continue to believe what we originally heard.

Long summary:

People think that they vet and think about something they have heard before believing it, but in reality they believe it by default and sometimes think and vet later if they have the time and inclination.

Evidence that have a strong tendency to believe what they hear includes (1) The strength of myths such as baldness coming from the maternal grandfather and dog years x 7 = human years and (2) an experiment where students were given color-coded true and false statements under time pressure and distraction. The mistakes they made were to mistreat false statements as true rather than true statements as false.

Abstract belief formation is new evolutionarily. It does make sense that we should not waste time and energy doubting our senses when forming perceptual beliefs (beliefs about our surroundings based on sensory input, such as believing a tree is there because we see it). Believing and then possibly verifying later is the correct thing to do when you see something that might be a lion.

Example from poker: In the variant Texas Hold’em, each player has two hidden cards and additional face up cards that count towards everyone’s hand are dealt after each round of betting. Two consecutive cards of the same suit are playable in some specific situations, but the subtleties were lost as this idea was repeated and it turned into “win big or lose small” and people at poker seminars she teaches tend to think that this is a good starting hand without having verified it. When she tells them to track their profit/loss with it, they find that they are losing.

Example from life: Based on some studies that were secretly funded by the sugar industry, Americans cut calories from fat by 25% in one generation, replacing the fat with carbs. It had the opposite of the desired effect.

The problem isn’t just that we default to true, it’s that we don’t easily update to false. There was a study where subjects read messages including a retraction of part of an earlier one, and when asked questions later they often answered as if they had not seen the retraction.

“They saw a game”

This section is first and foremost about a 1954 paper. 1954! Way before the replication crisis—I give it about as much credence as a random overhead anecdote, and you should too. That doesn’t mean it’s wrong—but it’s not remotely a source to be relied on.

If I seem annoyed about this, I am. So far my experience with this book has been the author citing sources that range from outright frauds to completely unreliable. That doesn’t mean anything in particular is incorrect; but inasmuch as the point of citation is to build trust with the reader, this is doing very much the opposite for me. It’s not even like in the Ariely case, where you can say she was bamboozled by him; at the time this book was written the sorry state that social science was in pre-replication crisis (and honestly nowhere nearly completely cured since) was well-known.

Be that as it may: there was a hotly contested college football game between Princeton and Dartmoth, and when college students at each university where shown a video of the game, they focused on different things in it. Specifically, the students from the different colleges had different views on the number of penalties each side committed.

A 2012 study called “They saw a protest” in reference to the earlier study’s name (“They saw a game”) showed subjects a video of a police action halting a political demonstration. One group were told this was a protest outside an abortion clinic, and the other were told it was outside a college career-placement facility at which the military were recruiting (with the protest being against the military’s “don’t ask, don’t tell” policy.)

In theory the video was cleverly edited to not show the actual topic of the protest. I’m skeptical. Yes, you can blur out signs and so forth, but if those different potential protests might have for instance different demographics you’re not going to be able to hide that. This too is an example of how one should read all social science, even post-replication-crisis, with a very critical eye; humans are complicated and multi-dimensional, and no, you can’t “just” control for traits or likewise flatten people out in any reasonable way.

In any case: the people who saw a video that was alleged to be about a protest for a cause they said up front they favored saw the police being more aggressive and intimidating to the protesters, despite the fact that everyone saw the same video.

I’d take this book a lot more seriously if it said the same things but without the thin veneer of social science. If it just said “hey, you know how when people see the police disrupting a protest that aligns with their own political opinions they think the police are being more aggressive than when the protest doesn’t align with their own political opinions?” then I’d say “yeah, that tracks my own experience, I guess”. That’s all you can say even with the book as it is, trusting it insofar as it makes intuitive sense and aligns with your own experience. The facade of social science is meant to make it seem more authoritative than that, and that the author thinks that it makes the book in any way better is deeply worrying.

DIALOG:

Ha, [redacted], you’re pooping on poor Annie Duke so hard that I went and read “They saw a game” out of morbid curiosity. I’m normally similarly critical of this kind of thing (see my review of Katy Milkman’s behavior change book) but didn’t feel like it was particularly egregious here. In fact, I think she pretty much did treat the two studies as anecdotes, and they seemed like good ones. I may be being too credulous. Or just excessively benefit-of-the-doubt-giving. But I guess I think we should reserve our ire for when she says something actually wrong, which I’m not sure she has so far.

I’ll at least agree that acknowledgment of the replication crisis is important when citing social science studies. Just that these particular papers are transparently so much better than “power pose” style papers that the replication crisis mention would’ve been pretty perfunctory (like “obviously don’t believe social science studies, especially before the shit hit the fan on the replication crisis, but this one’s perfectly commonsensical”). So I guess the omission isn’t bothering me too much here. We’ll see if it gets worse as the book continues!

(The thing that galled me in Milkman’s book was when she went out of her way to say that a study from the 1800s replicated but just credulously cited a million other more recent things, including more than one power-pose-style thing.)

We started talking about this in the Discord a bit and it reminded me that I have a super dorky version of thinking in bets, in a sense, that I call “Danny’s Agonizing Guide to Agonizing Decisions”. I’m, ironically, torn about how tongue-in-cheek I mean it to be taken as.
I thought power poses were good, actually.
Oh ho! I stand corrected. And duly unpowerfully. It seems I read about power posing not replicating and just blindly believed it. Silly human brains.

It seems there’s a study showing that power posing has a real, albeit small and inconsistent, effect after all. Of course that itself is just one study. So, let us embrace our uncertainty!

The Stubbornness of Beliefs

Once you believe something, it’s difficult to dislodge that belief. The mind starts noticing things that confirm the belief, and actively ignoring, and even discrediting information that contradicts the belief. (Basically confirmation bias)
When we encounter new information, the urge to protect what we already believe guides how we treat the new info - whether we accept or reject it. This irrational, circular information-processing pattern is called motivated reasoning.
This explains the rise of fake news and disinformation. The author draws a difference here, saying fake news is an intentionally false story. It’s not meant to change anybody’s mind at all – but aimed specifically to entrench and confirm the beliefs the target audience already has. ‘Disinformation’, unlike fake news, has some true elements to it, which is powerful (and dangerous) because it lends an air of credibility to the narrative.
On top of people’s propensity to seek sources that confirm our belief, things are made worse on the internet by algorithms that learn what we like, and show us more of that, trapping most of us in a “filter bubble.”
Why is it so hard to accept new information that contradicts our beliefs? Because most of us don’t want to be “wrong” - it feels bad to be wrong and it’s tied into our identity. It’s easier to ignore or discredit the new information than shift our opinion of ourselves from being “right” to being “wrong.”

Being smart makes it worse

Duke says (without offering any particular evidence) that the popular wisdom is that the smarter you are, the less susceptible you are to fake news or disinformation. She supports this by saying that part of being smart is about being able to process information well, understanding the quality of arguments, etc, so logically this should make you better at spotting the bad stuff.

Surprise! Turns out this isn’t true: smarter people are also better at producing more plausible reasons as to why their side is right, and the other side is wrong. In short, they are better at motivated reasoning.

Duke references several studies that indicate smart people may even be more susceptible to this, and (from my 30 minutes of research) these studies seem to replicate pretty well.

Duke also references studies showing that more numerate people made more mistakes interpreting data on emotionally charged topics than less numerate subjects.

However, this “motivated numeracy” replicates less well: see “A preregistered replication of motivated numeracy” for a good survey and a clean replication with 3154 MTurk participants. This showed some effects, but not as clear-cut as originally claimed, and the paper comments that: “An emerging conclusion in this literature is that motivated numeracy, or the reasoning account of identity-protective cognition more broadly, seems unlikely to generalize beyond a relatively narrow set of conditions”. It may be, for example, that more numerate people have stronger priors, and mix those in with their interpretations of the test questions in the replications.

Overall, I’m comfortable that Duke’s main point stands: motivated reasoning is a real thing, and simply being smart doesn’t inoculate you. However, motivated numeracy - that is, being more affected by being more numerate - is on much shakier ground.

The section concludes by saying this is perhaps just how evolution built us: we protect our beliefs, and being smart and aware of our capacity for irrationality alone doesn’t help us. I’m sure we’re about to discover that thinking in bets, however, could help - over to you @aad to show us how, with “Wanna bet?”

Wanna bet?

Being challenged to bet on a belief is a signal for us: Our challenger is confident that our belief is inaccurate.
There’s an ideal response to this signal: revisiting the evidence we have for our belief.
This is very valuable response, since it moves us to a step in abstract belief formation process that was optional up to this point: vetting the reasons we have for the belief.
- (There’s a reference to “hearing is believing” section. [redacted]’s short and long summaries captured the relevant already. Still, I think it’s useful to lay this idea more explicitly now. The claim is that we form abstract beliefs in 3 stages: (1) hear something, (2) believe that and (3) later, and only maybe, actually vet the information.)
If we could go around betting on everything all the time, we’d benefit from it precisely because of this. We’d be more likely to “temper our statements.”
This is not practical.
Instead, let’s train ourselves to view the world from this lens: We are already betting on our beliefs. We bet with our happiness, attention, money, time.

Redefining confidence

We should question our beliefs because even “scientific facts” are updated, revised, and reversed over time.
Instead of binary thinking (all-or-nothing; black or white; “I’m confident” or “I’m not confident”), we can specify how confident we are in a belief.
Confidence can be specified on a zero-to-ten scale (or from 0% to 100%), where zero means we are certain our belief is not true and ten means we are certain our belief is true.
The confidence number can reflect uncertainty about our knowledge (e.g., “I am 60% sure this movie won an Oscar”) or uncertainty about a mix of our knowledge and chance (e.g., “I am 30% sure this movie will win an Oscar”).
We can also express confidence by declaring a range of plausible alternatives (e.g., “I think this movie will win zero to five Oscars”). Our range becomes tighter the more we know about a topic and the less luck is involved in the outcome. [1]
Making our uncertainty explicit has many benefits:
- We change how we view the world by moving away from black-and-white thinking.
- It becomes easier to update our beliefs and be open-minded because “I was 58% but now I’m 46%” feels much less threatening than “I was right but now I am wrong.”
- It makes us more credible communicators because sharing a confidence number signals that we are thoughtful and self-aware, which makes it more likely that people will trust us. (I am not sure if this is how persuasion works.)
- It helps our listeners when updating their beliefs.
- It invites our listeners to share their beliefs, which in turn gives us the opportunity to update ours.
Scientists publish results of experiments with p-values (akin to the confidence number) and confidence intervals (akin to the confidence range).
Now that we have identified the target (getting comfortable with uncertainty, specifying our confidence, acknowledging that decisions are bets), thinking in bets is a tool to hit it somewhat better. This section concludes the chapter.

[1] We could obviously specify a confidence number for our range, but the paragraph reads like it is an either-or thing, so I assume Annie means specifying a range for which we have 9-10 confidence that it is accurate. Since ranges are important in Poker, maybe she will explain what exactly she means in more detail later.

Nick the Greek, and other lessons from the crystal lounge

Classically, learning should occur when feedback is provided close in time to decisions and actions. But some people do not update their strategies or beliefs even when presented ample evidence of their deficiencies.
Thus, our goal in this section is to lay out some of the obstacles that might exist to learning from our experiences.

Outcomes are feedback

Experiences alone do not teach us anything—we need to actually identify what we should be learning from them
The trouble is that nothing in life is done with complete information
When we see the result of our actions, we in effect make another bet—we try to figure out what causes might have led to the outcome we observed, and update our beliefs accordingly.

Luck vs. skill: fielding outcomes

Our first task when we see the result of our actions—whether they went well or poorly—is to decide whether that was because of something we decided to do (skill) or something outside our control (luck)
This initial triage is a bet: do we ascribe the outcome to luck and ignore it, or ascribe it to a success or failure of our initial bet and thus update our beliefs?

Working backward is hard

(Recall that every decision can be characterized as starting with a belief, making a bet, and seeing the outcome unfold. What to learn from that outcome is another bet. If you decide the outcome was a result of your decision, not luck, then update your beliefs. That’s a Learning Loop.)

Wavy flashback lines to the 1990s, when conventional wisdom was that dietary fat was public health villain number one, I guess. Eating fat would clog your arteries and give you a heart attack. If you take, say, yogurt and add a bunch of sugar then the percentage of calories from fat goes down and you can call it LOW FAT and that sounds healthier. Oy.

One company (or brand of Nabisco I guess) that cynically exploited this confusion was SnackWell’s, which made sugary treats marketed as health food because they were FAT FREE.

SnackWell’s Phenomenon refers to people eating more of something because it has less of something they think is bad. If you’re gorging on SnackWells and gaining weight, it’s not obvious why. How do you know whether to ascribe the weight gain to the SnackWells? If you thought of the SnackWells as one of the healthiest things you were eating, you might blame other things.

Point being, outcomes don’t tell us what’s our fault or what we can take credit for. Life is a game of incomplete information, to use the game theory term.

Rat anecdote (literal rats, not rationalists): If you give a rat a lever to press to get a food pellet, even if they have to press it 10 times for 1 pellet, they’ll figure that out quick enough. If you disconnect the lever from the food, they’ll figure that out too, and give up on the lever. But add randomness (intermittent reinforcement) and it’s way harder to learn to press the lever (and, famously, especially hard to learn to stop). Of course humans are exactly like this with, say, slot machines in casinos.

PS: Refresher on Nick the Greek since the author keeps referencing him. He had wrong ideas about the value of unpredictable play in poker. Since the feedback loop in poker is imperfect, confirmation bias kept poor Nick from ever disabusing himself of his terrible poker strategies. If he won, he’d feel vindicated. If he lost, he’d write it off as bad luck. He failed to ever actually learn from experience.

Section: “If it weren’t for luck, I’d win every one”

Duke discusses the concept of self-serving bias in this section, exploring how we tend to attribute our successes to skill and our failures to luck. She uses several examples to illustrate this psychological phenomenon:

Car Accidents: Duke references a humorous list of reasons for car crashes, originally compiled by her father, Richard Lederer. This list showcases how people distort reality to suit their purposes. Interestingly, statistics show that even in single-vehicle accidents, 37% of drivers blame someone else.
Poker Players: The section’s title comes from a quote by Phil Hellmuth, a successful poker player. Duke uses this to demonstrate how even highly skilled individuals can fall prey to self-serving bias.
Politicians and Students: Duke continues the discussion of self-serving bias, showing how it manifests itself in various contexts, from politics to academics.

Personal Reflections

I think Fritz Heider’s description of people as “naive scientists” is apt in this context.
Regarding driving ability, I offer an alternative explanation for why most people think they’re better than average drivers. It’s possible that people divide drivers into two groups (fine and terrible) and, knowing they’re not in the terrible group, assume they must be above average.
On politics, I agree that voters tend to oversimplify what politicians can control. For example, I believe we should neither credit nor blame politicians entirely for macroeconomic indicators like inflation or unemployment rates.

Section: “All-or-nothing thinking rears its head again”

Duke attributes our self-serving thinking to a black-and-white mentality, where we see outcomes as either 100% right or 100% wrong. She emphasizes the importance of recognizing that most outcomes result from a combination of skill and luck.

Key Takeaways

Assessing causes more accurately helps us make better decisions in the future.
We should strive to be more self-confident while admitting that our successes are partly due to luck and our failures partly due to skill-related reasons.
Duke suggests feeling good about our efforts to seek the truth rather than inflating our sense of skill.

Personal Insights

I reflected on my experience with daily bridge tournaments, noting that I tend to attribute my rare successes to luck rather than skill. Duke’s perspective encourages me to consider a more balanced view.
The book briefly mentions self-deprecating bias, which can be just as detrimental as self-serving bias in decision-making.

Duke wants us to be more self-confident and admit that we fail partially because of skill-related reasons. She says we should also admit that we succeed partly due to luck. She advocates feeling good about ourselves for trying to seek the truth instead of for hallucinated skill. Admitting that overcoming this natural bias is challenging, Duke hints at a workaround at the end of the section.

People Watching

If we’re so biased about our own results, perhaps we could learn from other people’s results instead? Learning from other people’s results is valuable because it gives us a lot more information than relying on our own experiences and is free or nearly free.

However it does not help with bias. While we are biased to view our own good outcomes as skill rather than luck, we do the opposite with our peers and consider their bad outcomes as due to their action and good outcomes as luck.

Our biases in how we judge others also affect our compassion for them.

Anecdote: The Bartman play: A number of fans tried to catch a fowl ball at the same time as a Cubs player tried to catch it, which would have been an out for the opposing team. One fan, Bartman, touched the ball and knocked it away, preventing the out. Everyone blamed Bartman for the Cubs not making the World Series, rather than considering the luck involved in both the other fans not reaching the ball before him and everything that went into losing that game, despite their 3 run lead at the time, and the next.

Anecdote: When Annie first learned to play poker, her brother gave her a list of good hands to play. When people won with hands not on the list, she assumed that they were just lucky rather than realizing that there were situations where things not on the list should be played, and she missed out on a lot of opportunities to learn by observation. Her bias was so strong that she didn’t think to ask her brother about why they might be playing those hands. (Eventually she learned that the list was just a good starting point for a novice.)

Other people’s outcomes reflect on us

Poker is zero-sum: anything one player loses another gains, and vice versa. So in poker thinking of our own successes as a matter of skill means thinking of our opponents failures as a matter of skill, as our success are our opponents’ failures. Likewise, for our failures to be a matter of luck, our opponents’ successes must be a matter of luck.

But we humans default to thinking of everything this way as a zero-sum game. If someone else is winning, we must be losing, the thought goes. We judge happiness in relative terms, comparing ourselves or those around us.

When you ask people if they’d prefer to earn $70,000 a year in 1900 (when the average yearly income was about $450) or $70,000 a year nowadays, the author claims that “a significant number” would choose the first option. Those who’d make that choice would prefer to be fabulously rich relative to those around them, even though that means missing out on Novocain, antibiotics, refrigerators, air conditioning, and smartphones.

This zero-sum thinking is a bad habit, and—just like the bad habit of decrying one’s terrible luck when one loses—is one we should aspire to change in ourselves. We should objectively judge luck and skill, giving others credit when it is due, without being held back by the instinctive notion that another’s success is our failure.

Reshaping Habit

Habits operate in a neurological loop consisting of 3 parts: the cue, the routine, and the reward. [This cue-routine-reward loop in habit formation is featured in other habit books like James Clear’s Atomic Habits as well, of course.] For example, the cue could be hunger, the action could be the routine of going in search of a cookie, and the reward is the resultant sugar high. Combined, they form the habit of eating cookies when hungry. In poker, the cue might be winning a hand, the routine taking credit for this outcome, and the reward a boost to our ego.
However, as we’ve seen in previous sections, bias operates here – when we get a good outcome, we’re pleased to credit our own brilliant decision-making skills – and when we don’t, we blame luck. We need to break this habit.
Charles Duhigg, in The Power of Habit suggests that the best way to change a habit is to respect the habit loop – keep the old cue, deliver the old reward, but insert a new routine. After all, our brains are built to both crave the reward of positive self-image updates, as well as to be in competition with our peers. It’s better to work with this instead of against it, keep both the cue and reward, and change the part that’s more plastic - the routine of what gives us the good feeling.
So we should try to get the reward (of feeling good about ourselves) from actions that benefit our long-term goals, and help build our decision-making skills – like looking critically at outcomes. Annie Duke talks about Phil Ivey, a poker player who, after he won a game, would analyze his moves and discuss where he could have done better, instead of revelling in his victory, as you might expect him to do. This is a successful example of someone who gets their “reward” from learning, trying to find mistakes in even good outcomes, improving their game. This is how you avoid bias (the tendency to interpret a good outcome as merely the inevitable result of your excellent decision-making).
Top performers of various fields work to avoid the self-serving bias that interferes with learning, and in place of the instinct to seek credit and avoid blame, get their “reward” from a routine of truthseeking instead.
We can use the fact that we’re inherently competitive, by changing the features by which we compare ourselves: more willing than others to admit mistakes, more willing to explore possible reasons for an outcome with an open mind. For example, being willing – even eager, to spot mistakes in your own play is something hard that you’re doing, and others probably aren’t – a good reason to feel good about ourselves.

Overall: We need to break the habit of accepting our successful outcomes as the result of our awesome decision-making, and throwing the blame of unsuccessful outcomes on luck. The best way to do this is to develop the habit of critically examining all outcomes. We’re fighting a little bit against nature here, so, to successfully make this a habit, it’s a good idea to try to get the reward (feeling good about yourself) from actions that actually benefit us, like examining the outcome dispassionately, and trying to see if there are mistakes you made (even if you won despite them). Once you reset the habit to feel the reward from this critical exercise, you’re better placed to avoid bias and actually learn from experience.

“Wanna bet?” redux

The last section ended by talking about the mindset shift we want to make: changing our habit loop so we feel we are doing better than others because we are identifying learning opportunities that others are missing. It’s too easy to look at our win and claim it as pure skill, rather than noticing all the other elements that were involved.

Treating outcome fielding [Remember, “outcome fielding” is an analogy to outfielders making a decision about where to throw the ball: here, we’re deciding which bucket to put an outcome into - was this skill, or luck?] as a bet can accomplish this mindset shift. There are a lot of possible contributors and alternatives that contribute to an outcome. If we imagine having to place bets across these, it pushes us into a more open-minded exploration of alternatives, as well as allocating them more objectively into the appropriate buckets.

One good way to do this is to take different perspectives: we tend to discount the success of peers, so a strategy for figuring out which way we would bet is to imagine one of our outcomes happening to a peer, or a competitor. It allows us to examine that great result more objectively, identifying elements over which we had little or no control, and finding the things we could have done even better.

There is a downside: you lose the high of claiming good outcomes as 100% skill. But that’s a trade we should take - we are in a better place when we don’t have to live in a black and white world. We can lose some of the sting of a loss when we treat it as an opportunity to learn. You are also more compassionate of others, noticing that bad things aren’t always their fault and good things aren’t always just luck.

The Hard Way

Adopting the habit of thinking in bets is challenging, especially when life throws unexpected, frustrating obstacles our way. Initially, it will be very difficult, but over time, it will become easier. It isn’t all that different than other habit changes. Despite the early struggles, this effort is worthwhile.

Of course, this approach won’t fix everything or completely eliminate issues like self-serving bias or motivated reasoning. These biases may still surface, but we’ll handle them better. Duke argues that even slight improvements in managing these biases can significantly enhance our learning experiences.

Poker serves as a good example. Each game offers numerous learning opportunities. If we capitalize on even 10% of them, we’ll be far ahead of the “Nick the Greeks” of the world. Even when facing a superior opponent, we could surpass them while still not utilizing all available learning opportunities.

Duke also suggests that this effect compounds. Recognizing one learning opportunity increases the likelihood of identifying similar ones in the future. It’s like using a navigation system that introduces a tiny error each time we consult it; over a journey, these errors accumulate, and our path deviates significantly from our intended course. Thinking in bets is akin to eliminating that small error.

So far, we’ve focused on the habits we want to reshape and how to reshape them. Now, we’ll explore how to make this process easier by seeking help.

Chapter 4: “Buddy system”

“Maybe you’re the problem, do you think?”

Annie Duke relates a media sensation from the “Late Show with David Letterman.” The guest shares stories from a section of her life, essentially a chain of events with a lot of drama going on.

Duke notes that the guest’s narrative characterizes the drama as out of her control. In the context of the book, this is ‘fielding the drama into the luck bucket’.

Letterman then challenges his guest’s perspective: the guest is the only constant in a long stretch of drama, so maybe she is the problem? So he’s suggesting that at least parts of the drama could be ‘fielded into the skill bucket.’

The interview then devolves into an unpleasant exchange, making its way into magazine headlines and news outlets.

Duke’s point is that while Letterman was perceptive and offered a potentially useful alternative, he violated the social contract of their exchange by doing so. This led to interview becoming an unpleasant exchange, and rendered Letterman’s otherwise potentially useful comment utterly ineffective.

Duke’s ultimate message is a reminder: Not everyone is interested in truth seeking in the sense we’re discussing, and perhaps it’s best not to pursue it indiscriminately, regardless of the situation we’re in.

When others are involved, mutual consent is key to effectiveness of our efforts in this regard.

The red pill or the blue pill?

The decision to think in bets, focusing on what we can control (our decisions) and letting go of what we can’t (our luck), must be voluntary to be sustainable. (Like Neo in The Matrix, who consciously decides to unplug from the matrix by taking the red pill.)
A group of like-minded people, a decision or learning pod, can help us see the world more objectively and make better decisions. Others can often spot our errors and blind spot biases better than we can.
Annie was introduced to such a group by her brother early in her poker career. They taught her to focus on analyzing her decisions instead of complaining about bad luck or her opponents.
Within a decision pod, we consciously change the social contract to be open-minded to disagreement, to take responsibility, and to admit when we are wrong, even if it makes us uncomfortable.
A decision pod doesn’t have to be very big, and we certainly don’t want to force anyone to join. As long as there are three people (two to disagree and one to referee), the group can be stable and productive.
The rest of the chapter discusses features of the agreement that make a decision-making pod productive.
For example, after being eliminated from a tournament, it was still okay for Annie to complain about bad luck once in a while (with the implicit understanding that the objective analysis focusing on decisions will follow).

Not all groups are created equal

A group can help us change habits, but groups can also create an “echo chamber” which will amplify confirmation bias
For a group charter, Annie thus suggests three policies: (1) Focus on accuracy over confirmation, rewarding truthseeking and open-mindedness. (2) Accountability, and (3) openness to a diversity of ideas.
Expanding on this “accountability” point—the idea is that people should have in their heads the idea that they need to be able to justify what they are saying to a (nonexistant) outsider, and that we can leverage the desire for approval for motivation in the direction of “truthseeking” rather than the direction of “blindly agree with what the other people in the group said”
Again, further, we want to create a group where you feel approval when you admit mistakes, give credit only where it is do, find mistakes in good outcomes, etc.

“One Hundred White Castles… and a large chocolate shake”: how accountability improves decision-making

It sure was nice of y’all to get me cued up for the accountability section!

Ok, we have another wacky poker-buddies story about David Grey and Ira the Whale. They’re wagering whether Ira can eat 100 White Castle mini burgers. The odds are against it but he pulls it off. (If there’s a point to this story, we don’t find it out in this section.)

Pause to mention that betting is all about accountability: putting your money where your mouth is. Yay! Having lots of bets keeps you grounded in reality and reduces motivated reasoning. Truth-seeking! Scout Mindset! Litany of Tarski!

Here’s some more specific accountability for poker: precommit to a loss limit. If you lose that much, walk away for the night. The reason is that you might be playing poorly but deceive yourself that you’re just having a bad luck streak that you can play through. In Annie Duke’s case, just having her decision pod who she’d have to explain herself to was enough to stick to that predecided loss limit.

The group ideally exposes us to a diversity of viewpoints

Claim: Diversity is the foundation of productive group decision-making. Apparently John Stuart Mill (of Utilitarianism fame) was big on this in his second most influential book, On Liberty, though Wikipedia doesn’t seem to mention it.

(Lots of platitudes about diversity redacted, you’re welcome. Different viewpoints improve accuracy, etc.)

There’s a whole list of questions to ask yourself to improve your accuracy but they all seem so obvious. If you’re thinking at all about improving your accuracy you’re naturally going to ask questions like how you might be wrong, right?

Hmm, might I be wrong about that? Just in case, I’ll now repeat the list in my own words:

Why/how could my belief be false?
What other evidence for/against my belief might be out there?
What other areas could inform a prior on this?
What info might I have missed or minimized?
If someone had a different belief, why would they and why might they be right?
What are other perspectives/narratives for what has happened so far?

Maybe I like those after all? Book-brigading sure is great.

But just asking yourself those questions or imagining what someone with a different perspective would say only gets you so far. You’re swimming upstream against so many biases, like preserving a narrative.

So you need actual diversity in your decision pod. (None of this is about DEI-style diversity, btw, but I suppose that version is a proxy for the kind we do care about: diversity of opinion and perspective. Duke seems to not touch that question with a 10-foot pole, which is great. Maybe pretend this parenthetical doesn’t exist.)

Forming a decision pod from a group of poker players is great because it’s naturally diverse, being selected by liking poker rather than, say, political ideology. And of course the willingness of poker players to bet is helpful.

Here are some instances of trying to engineer diversity of opinion and dissent:

The US State Department has a formal Dissent Channel, which is credited with at least one policy change
The American Foreign Service Association has four separate awards to recognize constructive dissent and risk-taking
The CIA has red teams

The final point in this section is that diversity in a group is hard to maintain. Duke didn’t say it but sociologists call this homophily: people gravitate towards those who are similar to themselves. It feels good to have our ideas echoed back to us. Echo chambers are so comfy.

Federal judges: drift happens

In this section, author Annie Duke explores ideological diversity in federal judicial panels. She refers to a large-scale study led by Cass Sunstein that examined thousands of appeals and even more votes. The study revealed some intriguing patterns:

Democratic appointees voted with the plaintiff 43% of the time overall but only 10% when seated with two Republican appointees.
Republican appointees voted with the plaintiff 20% of the time overall but 42% when seated with two Democratic appointees.

These statistics suggest that judges’ voting patterns can significantly shift based on the composition of the panel they serve on.

Personal Reflections

I found it surprising that Democratic appointees voted even more conservatively than Republican appointees when outnumbered and nearly vice versa.
I wondered about the nature of the cases, given that Democratic appointees were more likely to side with plaintiffs overall. This trend seems to contradict the party’s reputation for being “soft on crime.”
The study concludes that diversity on these panels leads to better decisions, but I have some reservations about the interpretation of these numbers. In particular, I was suspicious that it looked like each side more or less flipped when outnumbered two to one by appointees of the other party.

Duke also mentions that in decades past, Supreme Court justices hired clerks from the opposite party, a practice that has largely disappeared since 2005. She discusses the “filter bubble” in modern media and how people tend to believe they’re rational while their opponents consume media in a metaphorical echo chamber.

Social psychologists: confirmatory drift and Heterodox Academy

Duke references Jon Haidt’s work on the lack of ideological diversity in social psychology and sociology. Key points include:

Haidt found only one widely recognized conservative social psychologist in the field.
Haidt founded the Heterodox Academy to combat ideological homogeneity in these fields.
A paper Heterodox Academy researchers published in Behavioral and Brain Sciences (BBS) documented research into this trend.

Duke argues that ideological balance is crucial in social psychology because the field covers topics people of different ideologies disagree strongly about, like “racism, sexism, stereotypes, and responses to power and authority.” She asserts that research quality suffers if it comes from only one side of the political spectrum.

The BBS paper argues that even well-meaning academics tend to be biased towards their ideological side, favoring papers that align with their views.

Personal Insights

Duke’s suggestion to follow people from the opposite side of the political spectrum on social media is something I’ve tried but found challenging and unenjoyable. I may be doing it wrong.

Duke concludes by emphasizing that the biases observed in judges and academics are universal. She implies that if these truth-seeking professionals are susceptible to such biases, the rest of us are likely even more prone to them.

Wanna bet (on science)?

Betting can be a tool to motivate people to seek correctness and express contrary opinions, because it makes their goal be winning the bet rather than fitting in with the group.

A study involving asking scientists whether they thought papers would replicate found that they were right 58% of the time when they performed traditional peer review and 71% of the time when they participated in a betting market.

Chapter 5: Dissent to Win

CUDOS to a magician

Robert K. Merton, known as the founding father of modern sociology, was very interested in the institutional influences on science, such as how geopolitical influences can spur scientific advancement and how science struggles to remain independent from those influences.

He wrote a paper in 1942 (and continued to perfect it until the final version in 1973) about the normative structure of science. (This paper was referenced in the BBS paper.) He laid out rules with the acronym CUDOS:

Communism: data belongs to the group
Universalism: apply uniform standards to claims and evidence, regardless of where they came from
Disinterestedness: vigilance against potential conflicts that can influence the group’s evaluation
Organized Skepticism: discussion among the group to encourage engagement and dissent.

Duke finds these broadly applicable to any truthseeking group, and that when people are starting to pursue confirmation rather than accurancy it is often because one of these was lacking.

Mertonian communism: more is more

Science has a norm (though honored often in the breach) that researchers are supposed to publish all their data. Feynman memorably espoused a very strong version of this: “a kind of utter honesty—a kind of leaning over backwards. For example, if you’re doing an experiment, you should report everything that you think might make it invalid—not only what you think is right about it: other causes that could possibly explain your results…”

That’s tough, but it’s an aspirational ideal. Within “decision pods” it is a good idea to get out there all the possibly relevant information, in a very broad definition of possibly relevant. Often it’s the very details that we have an urge to leave out because they make us uncomfortable that are the crucial details.

The Freedom of Information Act is a similar aspirational information-sharing mechanism, so that citizens can know what the government is doing, and thus can make up their minds about if it’s doing a good job. The first amendment freedom of speech and of the press are primarily about the importance of self-expression, but in addition they also similarly prevent censorship of information about what the government is doing and how well it’s doing it.

All truthseeking is in large part doing by agreement. Researchers agree to share their data, the government legislated FOIA to cast light on their own doings, and likewise within these decision pods we can voluntarily decide to share information. Keeping secrets isn’t intrinsically bad and in some situations is important, but choosing to share information in the specific context of a decision pod helps with productive truthseeking.

In group discussions, if we’re missing some details, it could be because someone didn’t think they’re relevant, or because they have a bias to encouraging the narrative in a flattering direction.

As Duke says Jonathan Haidt points out, and as we’ve been discussing recently, “we are all our own best PR agents, spinning a narrative that shines the most flattering light on us.”

Different people often give complety different accounts of an event, each influenced by their own perspectives, as is demonstrated in the film Rashomon, in which four separate people give completely different accounts of a scene they all observed.

No one version of a story will ever be completely accurate. One person’s telling of the story will always incomplete. So we should strive both to be as complete as possible in our sharing within the decision pod, but also when hearing others’ stories we should understand that we’re still not getting all the information and try to dig deeper.

In the earlier story about the CEO who blamed his company’s problems on firing the present is an example of this, because only when Duke dug down deep with follow-up questions did she unearth all the facts that lead to the conclusion that the firing had actually been quite reasonable.

Sharing lots of details gives people the opportunity to ask you good questions. Poker players describe hands to each other in what seems to be irrelevant nitpicks detail, but all this detail actually helps them. And because they share all the details each time, they avoid the possibility of leading the listener to the desired conclusion via partial and thus misleading information.

The author then tells an anecdote about how Vince Lombardi, who is apparently is a famous football coach, once held an audience (of other football couches) spellbound for eight hours describing a single football play in great detail.

We can encourage others within our decision pods to share information they otherwise would be reluctant to by given positive feedback every time someone shares information that might cast them in a bad light.

Universalism: Don’t Shoot the Message

“Don’t shoot the messenger” means not to take out your frustration with the message’s content on the person delivering it. An early example is from Plutarch’s Lives, about the King of Armenia (literally) shooting the messenger. From Wikipedia: “The first messenger, that gave notice of Lucullus’ coming was so far from pleasing Tigranes that, he had his head cut off for his pains; and no man dared to bring further information. Without any intelligence at all, Tigranes sat while war was already blazing around him, giving ear only to those who flattered him.”
Universalism is the converse of this principle. This section is about shooting the “message” (not the messenger) – reacting to an idea in different ways depending on whether you like the messenger or not.
When we have a negative opinion of someone, we close our mind to what they’re saying – and likely miss out on a lot of learning opportunities. Similarly, when we like the “messenger,” we tend not to examine what they’re saying too rigorously, and accept it too easily. Both are bad. Acceptance or rejection of an idea shouldn’t depend on who or where it comes from, but must instead be subjected to pre-established impersonal criteria.
The author talks about her personal experience in poker – when she started out, she was given a list of “good beginner moves” by her brother. She treated these as absolute truth (another example of shooting the message?), and mentally dismissed players who deviated from these rules as “bad” players – which meant she shut her mind to potentially learning from these players.
Once she realized she was doing this, she came up with a way to practice and reinforce universalism. When she had the impulse to label someone as a “bad” player, she’d force herself to find something they did well. This had the effect of making her think more deeply about their strategy, instead of dismissing it altogether and thereby losing out on learning from them.
Another exercise to reinforce universalism, is to deliberately seek out the opinions of, and read the arguments of, people on the opposite side of what you believe, and then find things that you actually agree with them on.
Yet another way to separate the message from the messenger is to imagine the same message coming from someone you either value more, or value less, or omitting where you heard the idea, to make sure it gets examined critically without bias.

Disinterestedness: we all have a conflict of interest, and it’s contagious

Through the 1960s, scientists were at odds as to whether fat or sugar was the culprit in the increasing rates of heart disease. In 1967, a comprehensive survey by three Harvard scientists, published in the prestigious New England Journal of Medicine, firmly pointed the finger at fat as the culprit. This shifted the diets of hundreds of millions of people for decades. But it turns out, according to an article in JAMA Internal Medicine in September 2016, that the three Harvard scientists were paid by a trade group representing the sugar industry. Science relies on an assumption of disinterestedness: knowing about conflicts of interest is important data that we need to know.
But our brains have built-in conflicts of interest: a natural desire to avoid admitting error and blame bad luck, or to take credit for good results as being the consequence of decisions we made.
Feynman recognised that even physics, the hardest of sciences, has a demonstrable outcome bias: knowing the hypothesis under test can unconsciously bias the analysis. Techniques such as using a random variable in recording the results can allow “outcome-blind” analysis (e.g. introduce a random variable X, range 1-5, to code the outcome, and don’t tell those doing the analysis if X=1 or X=5 corresponds to an improvement in patient outcomes)
We can apply this idea as we workshop decisions: sales teams can evaluate their strategy before they know if they won the deal. Traders can vet processes prior to options expiring. Even with chess games (my drug of choice), you can evaluate a series of moves before knowing who won.
Beliefs, too, are contagious: if our listeners know what we believe to be true, they might unconsciously work harder to justify our beliefs. So we can try not sharing our personal opinion as we seek that of the group.
Key to a successful group is reinforcing habits that are now perhaps associated with the Rationalist community, such as being able to successfully articulate and steel-man an opponent’s point of view. This can in turn give us a deeper understanding of our own position.

Organized Skepticism: Real Skeptics Make Arguments and Friends

Duke emphasizes that skepticism involves recognizing that some of our beliefs are likely untrue. True skeptics actively seek out these potential inaccuracies.
When we are precise about the confidence we have in our beliefs, we create an environment that reduces confrontational dissent. Instead, skepticism can foster cooperative exploration, where people express both their beliefs and the uncertainty surrounding them.
Contrary to common associations with skepticism—such as disagreeability or cynicism—true skepticism often correlates with good manners, civil discourse, and friendliness.
Duke suggests that skepticism should be encouraged and even institutionalized through practices like devil’s advocacy or red teams. These methods help create structures for challenging assumptions productively.
On an individual level, inviting others to challenge our beliefs can be helpful. Doing this proactively ensures others don’t feel like they are being discouraging or negative.
Engaging in skepticism, recognizing our own inaccuracies, and improving them is inherently difficult. We should make this process as easy as possible, ensuring that unproductive behaviors—such as cynicism or dismissiveness—don’t hinder constructive, collaborative exploration.
It’s important to remember that even with a cooperative, exploratory attitude, skepticism can still feel confrontational to those who are not part of the skeptical effort. The David Letterman we’ve read about earlier example illustrates this.

Communicating with the world beyond our group

To apply the lessons outside a truth-seeking group, there are ways to avoid Letterman-like situations [1]:

Lead by example, i.e., express uncertainty about your own views.
Lead with assent, meaning you point out areas where you agree, hoping that the other party sees you as a good-faith actor when you later disagree.
Ask for temporary agreement to engage in truth-seeking, i.e., explain the general framework and then ask for permission to pursue truth-seeking in that context.
Avoid saying “no” or “but,” and adopt a “yes, and” mindset.
Focus on how to improve the future instead of making judgments about the past. That will make people more agreeable.

That concludes the section and chapter. The next chapter is about time travel (or something).

[1] These points could appear like that in any random book about communication.

“Let Marty Mcfly…” and “Night Jerry”

Annie suggests we combat akrasia by choosing to remember the past and imagine the future.

“Moving regret in front of our decisions”

Regret is a strong emotion. But we wallow in regret after things go wrong, when it is too late to change them. Better to try to imagine them going wrong, and let this push us to consider the future when deciding on our actions.
A concrete suggestion: before a decision, ask yourself what the consequences of each choice will be “in 10 minutes? in 10 months? in 10 years?”

A flat tire, the ticker, and a zoom lens

Anecdote! You’re changing a flat tire at night in the freezing cold and it suuuuucks. In the moment it feels like massive suffering. But zoom out. How much worse did your life get because of this flat tire? Maybe literally less than zero? You lost an hour or something and have a cool story to tell.

Similarly if you have your retirement fund in the stock market and you watch its value bouncing up and down each day (“watching the ticker”). Zoom the heck out and look at the big picture. It’s a retirement fund – the right granularity to look at it is more like decade-level.

So when you make decisions, zoom out to the right level when assessing them.

Review: A 10-10-10 strategy means considering what the the consequences will be in 10 minutes, 10 months, and 10 years. And also looking backwards: How would I feel today if I had made this decision 10 minutes ago? 10 months ago? 10 years ago? (Related idea: outside view vs inside view.)

Doing that discourages overreacting to the immediate moment, where you focus on offloading negative emotions (like by blaming them on luck) or sustaining positive ones (by taking credit for something that might be luck). Again, zoom out.

“Yeah, but what have you done for me lately?”

Abstract/Executive summary

People tend to focus on the latest result, and that’s irrational. If we’re gambling, we feel good when we’re up in the moment, even if we’re down overall. We should avoid this recency bias and focus on best practices, whether we’re doing well at the moment or not.

Detailed discussion

Duke explores how recent successes and failures influence our emotions and cloud our decision-making abilities. She introduces the idea of “watching the ticker,” a form of “resulting,” where we focus on the most recent results.

Duke describes two gambling scenarios: both end with breaking even, but one starts well and ends poorly, while the other begins badly but finishes strong. Despite identical net outcomes, Duke surmises that people typically feel better about the second scenario. She argues this pattern persists even when the first scenario has a better net result.

This pattern reminds me of Dan Ariely’s observations in “Predictably Irrational” about his burn treatment experiences: he remembered his treatment more positively when it ended gently, regardless of the overall intensity or duration of the experience.

Duke argues that this recency bias is irrational. Feeling good when things are going poorly overall (or vice versa) just because of recent results can lead to poor decision-making. She suggests that skilled decision-makers actively work to overcome these emotional responses to recent outcomes. A solution? Duke suggests trying to view your situation as if you’re observing yourself from one year in the future, a perspective I appreciate. I like the idea that in a year, today’s bad experience will be a good story.

Duke notes that “Poker players think about this a lot.” This focus makes sense. I imagine that poker players enjoy the emotional highs and lows of the game, making them susceptible to recency bias. Nevertheless, it occurs to me that mastering these emotions could become part of the challenge and appeal of the game itself.

I suspect the following sections will try to bridge the gap between professional gambling and everyday decision-making. Just as poker players try to distance themselves from momentary successes and failures, I anticipate Duke arguing that their techniques can work in our professional and personal lives.

Tilt

Abstract/Executive summary

When emotions from bad results lead to bad decisions, you’re “on tilt.” This feedback loop can occur in any context, not just poker. To avoid or recover from tilt, try taking the long view. Will this matter in the long run? If self-talk doesn’t work, enlist friends to help you maintain perspective.

Detailed discussion

Duke begins this section by discussing surfers and their extensive vocabulary for waves. This illustration reminds me of the “Rain God” passage from Douglas Adams’s So Long, and Thanks for All the Fish. She uses this exposition to characterize how specialists develop precise jargon to describe nuances that outsiders might miss. Surfers need specific terms for different types of waves so they can develop and discuss strategies for handling them; poker players have developed a vocabulary around the intersection of emotions and decision-making.

One important piece of poker player jargon is the word tilt, a term I encountered recently through Nate Silver, the poll aggregator and poker player. The word originates from pinball machines, which, since 1935, have had mechanisms to detect when players violently jostled them. When the sensors are triggered, these machines display “TILT” and shut down. In poker, being “on tilt” means you’ve become emotionally overwhelmed by recent outcomes, leading to poor decision-making.

Duke emphasizes that tilt is “the poker player’s worst enemy.” It creates a dangerous feedback loop: bad results lead to emotional decisions, which lead to worse results. The signs of tilt are both behavioral (yelling, cursing) and physiological (flushed cheeks, racing heart, rapid breathing). Although Duke uses poker, where the term originated, as her primary example, she emphasizes that tilt can occur in any context in which emotions interfere with decision-making.

To alleviate tilt, Duke suggests developing habits to recognize its signs and pre-committing to exit situations when you notice them. She offers various self-talk strategies, although I’m skeptical about their effectiveness when someone is already emotionally flooded. Perhaps the better approach is avoiding tilt altogether. Duke suggests enlisting a “truthseeking pod,” people who can help talk you down when you’re on tilt. They might help you take the long view: “Will this matter in the long run?”

Duke concludes the section by advocating a long-term perspective, noting, “It’s all just one long poker game.” This approach to decision-making reminds me of index fund investing, in which a successful strategy ignores short-term market fluctuations. Just as long-term investors outperform market timers, perhaps decision-makers who maintain emotional equilibrium outperform those who react to every fluctuation. Nevertheless, there’s no such thing as “index poker playing.” The gambling environment requires making many fast decisions and observing the results, increasing vulnerability to tilt.

Ulysses contracts: time traveling to precommit

We can do things to make it more likely that we will stick to decisions with a longer term perspective. None of them are impossible to circumvent in the moment and we can’t avoid all emotional irrational decisions, but even very simple things decrease the amount that it happens.

Even just articulating beforehand what circumstances we would act in triggers deliberate thought about the decision in the moment. (For example investment advisors will determine in advance what situations they would want to buy or sell, rather than emotional reactions to a sudden change in the value of an investment.)
You can do things that make the bad decision harder. (Such as when Odysseus (who the Romans called Ulysses) had himself bound to the mast to avoid acting on the Siren’s song. Hence the term Ulysses contract for this kind of thing.)
You can do things that make the good decision easier. (For example if you are going to be waiting for a friend in a food court and want to avoid eating junk food, you can pack healthy snacks).

Decision swear jar

This is what we’ve all been waiting for, I guess, the commitment-contract chapter. The author gives ideas for things one can commit to not doing. This is framed in terms of a “swear jar”, which serves here as an ad-hoc version of Beeminder. What any of us would get out of this chapter is the list of ideas of what to commit to. These are all framed negatively—things to pay up for when you do, what in Beeminder-land we’d call a do-less goal.

The framing here is that you’d put some money in the “swear jar” whenever you find yourself expressing certain words, phrases, or thoughts; the idea being to bind the payment to concrete and distinct potential mistakes of thinking. This also helps you catch yourself in the moment and reflect—toss a dollar into the swear jar (or enter a Beeminder data point), and then rethink that last thought in light of what you know about that kind of thinking having the potential to lead you astray.

There is a good long list of things you might want to commit to do less of. I’ll give here a few sample examples from the chapter, but I’m not going to just copy them all down, so I recommend that anyone thinking of making Beeminder goals based off this book read through the full list.

Expressing the illusion of certainty: pay up whenever you find yourself saying something like “I know”, “I’m certain”, “There’s no way that’s true”, etc, or expressing a numerical probability of 0 or 1 (0% or 100%.)
Pay into the swear jar whenever you use the word “wrong” when in exploratory discussion; “wrong” is a conclusion, not a rationale.
Pay up whenever you find yourself moaning about bad luck.
Discouraging others from expressing their opinions: pay up when you respond to an opinion you ask for with “no” or “but…” or building on it without “yes, and…”
Lack of self-compassion: pay into the swear jar whenever you say “how could I have been so stupid” or similar.

Reconnaissance: Mapping the Future

Just as a military mission relies on reconnaissance and won’t begin operation till they find out every detail about the terrain and situation, and map out every possibility that could result from various variables (like weather), we shouldn’t make decisions without considering the range of futures that could result from any of the decisions we make.

First, we imagine the “range of potential futures”, identifying as many of the possible outcomes as we can – known as scenario planning. (i.e. what are all the scenarios that could possibly occur?)

Then we make our best guess at the probability of each of those futures occurring. People hesitate to do this because they can’t be sure of the probability – but that’s the point. It’s not about approaching future predictions from a point of perfection, but about acknowledging that we’re making a guess. By doing so, we move away from a dangerous 100% certainty that it will turn out this way or that.

Poker players do this kind of scenario planning all the time - they consider each of the opponent’s possible responses, and estimate the likelihood of each, before making their move.

Scenario planning is great when done with a group, since diverse viewpoints help identify more scenarios, deeper into the tree, and help the group estimate the probability of their occurrence better.

Scenario planning has many benefits:

It makes explicit the fact that the future is inherently uncertain, which leads to a more realistic view of the world.
It helps us plan our moves in advance, which means we can avoid being reactive. If we’ve identified a scenario where we’re susceptible to irrationality, we can try to address it preemptively with a Ulysses contract, for example.
Anticipating the possible futures keeps us from emotions – both unproductive regret or underserved euphoria – when a certain future happens.
It makes us less likely to fall prey to both resulting and hindsight bias.

Scenario planning in practice

Expected Value

This section starts by introducing the concept of Expected Value [As a reminder, Expected Value = (probability of outcome) x (payoff if outcome happens)], which will be well known to many of us here. Duke gives an example of an organisation that was applying for grants: initially, they would focus efforts on the grants with the highest headline value. But many of those had low probability of success, so their EV was worse than grants lower down the potential value list. By refocusing on the EV rather than on the headline value, they had more realistic budgets, could focus on ways of increasing probabilities, and were less likely to fall prey to hindsight bias or resulting, because they were thinking in terms of success probabilities all along.

The important of Reconnaissance of the future

More sophisticated scenario planning goes deeper into the decision tree: in the example that opened the book, of the Seahawk coach’s Superbowl decision, the two initial choices for Pete Carroll were run or pass. Each of those in turn could lead to different future outcomes. Importantly, calling a pass was likely to give Seattle three plays to score, as for example an incomplete pass or a penalty would stop the clock [As a Brit, it’s not completely clear to me if a sack in the last 2 minutes of a game would stop the play clock. I think an intentional grounding would, though, which is another possible outcome of a pass decision.]. Only by looking deeper into the decision tree do these important (and not unlikely) outcomes become visible.

Backcasting: working backward from a positive future

Directly trying to forecast the future, starting from now and moving into the future can distort our view. We have a sharp/detailed image of what’s in front of us, and a blurry/low-resolution image of future.

This can lead to a view where we end up giving disproportional attention to the problems of today, instead of preparing for problems of the future. Combined with an implicit (and tempting) assumption like “things are like this now, and they will stay this way”, this can be hazardous.

This section talks about an idea that I’d bet will be familiar to many of the brigadiers, which is supposed to remedy that.

Imagine you want to go on a huge journey. Instead of planning from the first step forward, start by picturing the journey completed and work backward from there.

Duke focuses on one approach to “working backwards”, known as “backcasting.” In this approach we imagine we realised the desired outcome, and we think about how we got there.

Here’s why it can be effective:

A Deeper Look into the Decision Tree: Starting at the end lets us focus on the big picture, then work toward today. This approach helps us form a deeper decision tree.
Spotting Key Challenges: Backcasting helps identify low-probability events that could impact success. By doing this, we can decide whether to (try and) increase the chances of these events happening or recognize our goal as ambitious.
Preparing for Setbacks: By mapping out key steps, we can prepare responses for potential setbacks and commit to how we’ll handle them.

Example: Weight Management Goals

Let’s say you’ve hit your weight management goals after six months. To understand how you got there, you’d ask questions like: What actions helped you reach your goal? How did you avoid unwanted habits? How did you adjust physical activity? And how did you stick to your food plan?

Premortems: working backward from a negative future

If backcasting is the process of imagining a positive future and the paths to it, then a premortem is the opposite, as it involves imagining a negative future and the paths leading to it. A premortem is an implementation of the Mertonian norm of organized skepticism.

Only by considering both the negative and positive event spaces can we see the world objectively, which enables us to make better decisions in the long run.

Returning to the weight loss example, we might anticipate scenarios such as feeling obligated to eat cake at a birthday party, encountering free cookies and bagels at a conference, and lacking time and motivation to go to the gym. We can then precommit to positive actions in such scenarios. (This reminds me of another self-help book where the author recommended a trigger-action plan, such as “whenever the waiter asks me if I want dessert, I order unsweetened tea instead.”)

Popular self-help literature often focuses on the benefits of positive visualizations. However, studies by Gabriele Oettingen, author of “Rethinking Positive Thinking,” have shown that study participants who imagined negative outcomes for weight-loss goals lost 24 pounds more than participants who only engaged in positive visualizations.

Being a “heckler” and pointing out ways in which things can go wrong might go against a “positive attitude” culture in professional settings. Therefore, it is important to clearly set expectations and explain how a premortem is helpful in reaching positive goals by illuminating the negative event space.

Dendrology and hindsight bias

When we look to the future, we hopefully see a branching tree of possibilities—a series of points where one thing might happen with high probability, or perhaps another thing will happen with low probability. In the end, we will live out one of these futures.

When we look back at the past, it’s very easy to forget that the present we live in was just one of these tiny branches, and to feel like it was somehow inevitable. After all, we can see the chain of causation leading to it, and it all makes sense—never mind that each of those things could have gone differently, and we would have gotten a different chain of events that also makes sense.

The book concludes by imploring us to try to always keep this in mind. When something bad happens, it’s always going to seem like it was obvious in retrospect—we can see the chain of events that led to it. But the only way we’ll be able to learn from our mistakes—and reach peace with things when we didn’t really make a mistake at all—is by thinking back to each decision we made and asking: “is it really true that I should have known things would turn out this way”?

Appendix: Book Brigade notes for How To Win Friends and Influence People by Dale Carnegie

Notes by Daniel Reeves and Bethany Soule, written as a dialog with each other

Chapter 1: “If You Want to Gather Honey, Don’t Kick Over the Beehive”

We start with fun anecdotes about vicious criminals who thought of themselves as the good guys. The point being, no one ever thinks they’re wrong and bad. Criticizing people backfires and makes them defend themselves and double down. All you do is incur resentment. There are more fun anecdotes of former presidents and Benjamin Franklin and Mark Twain who all figured this out. One of Lincoln’s favorite quotes was “Judge not, that ye be not judged.” Lincoln would write scathing letters to his generals or whoever and then intentionally not send them. Carnegie suggests a motto of “What Would Lincoln Do” and that whenever you’re tempted to admonish someone, pull a five-dollar bill out of your wallet and meditate on Lincoln’s picture.

“Any fool can criticize, condemn, and complain – and most fools do. But it takes character and self-control to be understanding and forgiving.”

The chapter ends with a parenting story that was apparently wildly popular called “Father Forgets”. I guess it’s still popular. It’s from the perspective of a dad who is just awful: yelling at his kid all the time, embarrassing him in front of his friends, total lack of affection, etc. But then the kid spontaneously hugs him good night and he has a come-to-Jesus moment and resolves to be non-awful.

Now I’ve probably made you curious to read it, though my prediction is that it’s not worth your time.

PS: One last anecdote from the author’s own life that might be interesting. He once saw a letter that had “dictated but not read” at the bottom and as a young person was very impressed by how Busy and Important someone must be to send a letter that way. He later had reason to write a letter to a bigshot in his field and thought he’d seem impressive if he put “dictated but not read” at the bottom of his letter. The bigshot replied with a sentence like “you are a jackass and I’m not answering your letter”. Carnegie 100% agreed that it was a jackass move but at the time it just made him despise the bigshot. To the point that when the bigshot died like a decade later, all he could think about was his resentment at the rebuke.

In conclusion, don’t criticize, condemn, or complain.

Chapter 2: The Big Secret of Dealing With People

Thesis: The only way to get someone else to do something is to make them want to do it.

So what do people want? According to Freud – sex and greatness. According to philosopher John Dewey – feeling important. How you get your feeling of importance determines your character.

Then he spends a couple pages discussing different ways that people get a feeling of importance. Anecdotes include his father deriving importance from his prize pigs (the pigs did not care), criminals deriving importance from infamy, rich people doing philanthropy (to get their name attached to things). Then he spends some time dwelling on invalids and the mentally ill. “Some people became invalids in order to win sympathy and attention…”, with an anecdote about Mrs. McKinley, and another anecdote about a woman in mental hospital who was dissatisfied with her life, but got all of her wishes in the delusions of her psychotic break.

Interesting assumptions about “insanity” and stuff that an enterprising comp lit or like, social anthropology student could write a whole thesis on in this: “If some people are so hungry for a feeling of importance that they actually go insane to get it, imagine what miracle you and I can achieve by giving people honest appreciation this side of insanity.” But I’ll skip that.

Okay, so the secret to motivating people? Praise. We go into listing a bunch of the titans of the industrial revolution who were lavish in their praise of their underlings and associates. We get anecdotes about Charles Schwab (first president of Andrew Carnegie’s US Steel Company, then went and fixed up / rebuilt Bethlehem Steel as well), Andrew Carnegie himself, the first John D. Rockefeller, Florenz Ziegfeld (of the Ziegfeld Follies), Stevie Wonder, Queen Victoria / Benjamin Disraeli, and others. Some of these are stories of the famous person being encouraged (to greatness?) by praise from superiors, teachers, etc.

I’ll include this quote from Schwab which (Dale) Carnegie claims will “all but transform your life and mine if we will only live them.”

I consider my ability to arouse enthusiasm among my people the greatest asset I possess, and the way to develop the best that is in a person is by appreciation and encouragement.

There is nothing else that so kills the ambitions of a person as criticisms from superiors. I never criticize anyone. I believe in giving a person incentive to work. So I am anxious to praise but loathe to find fault. If I like anything, I am hearty in my approbation and lavish in my praise.
– Charles Schwab

There’s a caution in here to distinguish between flattery and appreciation. Flattery is insincere praise. Praise only works (in the long run?) if your appreciation is sincere. I feel like there’s a mindfulness tie-in in here. What he’s suggesting in this chapter is about more than the praise, it’s about actually finding appreciation for stuff that other people are doing. Like the anecdote about Rockefeller, whose business partner lost a crap-ton of money in a poor investment, and Rockefeller didn’t berate him for the loss – it was a (literally) sunk cost at that point, but instead praised the guy for making good decisions about cutting losses and only losing 40% of the original investment.

I’m now also thinking of the How To Talk thing about praise – be concrete and specific. Which I think is to a similar purpose.

So in summary: basically be an optimist? Notice the things that the people around you are doing that improve your life, and then express appreciation for it.

Principle Number Two:

Give honest and sincere appreciation.

Chapter 3: “He Who Can Do This Has the Whole World with Him. He Who Cannot Walks a Lonely Way”

Bait the hook to suit the fish. Or a less Slytherin-sounding version: focus on what other people want. Or to ramp up the Slytherin again: “First, arouse in the other person an eager want.”
Example (I wonder if this was in the original 1936 edition): Instead of preaching to your kids not to smoke, talk about how cigarettes would keep them from making the basketball team or winning the hundred-yard dash.
Cute story of Andrew Carnegie wagering that he could get his college-aged nephews to reply to a letter (their mother couldn’t get them to reply no matter how frantically she wrote to them): He wrote a chatty letter that casually mentioned he was enclosing a five-dollar bill, which was not in fact enclosed. Back came the replies, “Dear Uncle Andrew, thank you for the kind note and…” – you can guess the rest.
Another story about a kid throwing a fit not wanting to start kindergarten the next day. The family started fingerpainting and told the kid he couldn’t join them without going to kindergarten to learn how. Tada, the kid woke up early the next morning anxious about being late. Is this compatible with how-to-talk?
In conclusion, before trying to persuade someone to do something, think about how to make them want to do it.
Savvy-sounding strategy that Carnegie used to argue against a price hike that a company tried to spring on him: He told the manager that it makes total sense to raise prices and “I’d do the same if I were you”. Then he said, let’s make a list of advantages and disadvantages of the price hike. This was all 100% from the manager’s perspective but the real arguments were “you’ll lose my business” plus reasons that Carnegie was especially valuable as a customer.
How Henry Ford put it: “If there is any one secret of success, it lies in the ability to get the other person’s point of view and see things from that person’ angle as well as from your own.”
I just remembered that this is conventional startup wisdom for webcopy. Don’t list features, talk about what the customer can achieve with your product.
Ironic thing reminiscent of the last paragraph of [redacted link to doc] about whether the money-hating rule is karmically ROI-positive: “the rare individual who unselfishly tries to serve others has an enormous advantage”. I guess it’s also a bit like the concept of heaven and hell – trying to selfishly motivate people to be selfless.
Some of these anecdotes are examples of Tom Sawyer’s trick: make people want to do the thing you want them to do.
Make people think something was their own idea. Plant a seed and let them cook and stir the idea themselves. They’ll come to regard it as their own. (Related anecdote about getting a kid to eat breakfast by having her help cook it.)

Review of the principles so far from the end of Part 1:

Don’t criticize, condemn, or complain
Give honest and sincere appreciation
Arouse in the other person an eager want

Part 2: Six ways to make people like you

Chapter 1: Do This and You’ll Be Welcome Anywhere

The basic thesis of this section is “showing interest in other people makes them like you and care about you.” That’s why everyone loves dogs. They are always interested in what you are doing and eating and excited to see you.

Claims people are fundamentally selfish and get all caught up in being interested in themself. This is kind of like the thing where I, a sufferer of social anxiety, have to remind myself that no one else in the room is paying attention to me at all, so I don’t have to freak out and be embarrassed that I just tripped on the carpet or whatever. No one is going to start pointing and laughing at me. Probably no one noticed. And even if they did they’d generally fundamentally only care as far as it affected them. People aren’t interested in you, they are interested in themselves. You can’t force people to be interested in you, and trying to get them to like you doesn’t make them like you.

Quotes Alfred Adler:

It is the individual who is not interested in his fellow men who has the greatest difficulties in life and provides the greatest injury to others. It is from among such individuals that all human failures spring.

Harsh.

Anecdotes

He had a dog named Tippy who he loved. The dog was a dog, and therefore incredibly loyal and excited to see him all the time. The dog was tragically killed by lightning (what?!). Be like a dog – enthusiastic and super into other people – and they will like you.
He once spent an evening hanging out in the dressing room of some at-the-time famous Broadway performer – Howard Thurston – who was a beloved and very financially successful magician. Thurston said that most magicians view the audience as rubes there to be conned, but not him. He is grateful for their patronage and thinks every time before he goes on stage how lucky he is to be there, and how the audience makes it possible for him to make his living doing this thing he loves.
Teddy Roosevelt was loved by everyone including his servants. His valet wrote a book about him. Among the stories the valet told were various stories about how the President was solicitous of all his servants as if they were his friends, knew everyone’s name, even when visiting the white house after his term.
Some salesman who almost lost an account with a druggist who was going to switch to some major retailer as supplier, but then the owner of the drugstore changed his mind on the recommendation of his staff, because the staff really like the salesman, cuz he knew all their names and always stopped to chat.
Found out the birthdays of all his friends and wrote them in his calendar and always sent a card.
Another salesman who couldn’t get an interview with the president of some company, but overheard the president asking the secretary about stamps for his son’s collection. So the salesman went back to his office, collected up a bunch of unique stamps from their international correspondence / customers, and then returned and said he’d brought stamps for the guy’s son. That got him an hours long interview where they chatted about stamps etc, until the president then brought up the business thing himself.

Summary

Roman poet Publius Syrus says “We are interested in others when they are interested in us.”

The interest must be sincere, but it’s a two way street, and both sides benefit.

Part 2, Chapter 2: A Simple Way to Make a Good First Impression: 😃

This is mostly an 8-page paean to the power of smiling. Sometimes it goes a bit over the top: “A smile is rest to the weary, daylight to the discouraged, sunshine to the sad, and Nature’s best antidote for trouble.”

So, yeah, you’re welcome for this abridgement. There are various stories about people resolving to smile at their spouses and coworkers and how instantly that transformed their lives. I’m not disagreeing with this, to be sure. It’s powerful.

On to pragmatics, Carnegie strongly endorses fake-it-till-you-make-it aka act-as-if aka the Mormon trick. He doesn’t use any of those terms but says this:

Action seems to follow feeling, but really action and feeling go together; and by regulating the action, which is under the more direct control of the will, we can indirectly regulate the feeling, which is not. […] If our cheerfulness be lost […] act and speak as if cheerfulness were already there.

Scooped by a century. One last quote worth pondering from this chapter:

People rarely succeed at anything unless they have fun doing it. “I have known people,” [some executive] said, “who succeeded because they had a rip-roaring good time conducting their business. Later, I saw those people change as the fun became work. The business had grown dull. They lost all joy in it, and they failed.”

PS: The “actions follow feelings but also feelings follow actions” part is part of why Beeminder is not self-blackmail and is aligned with Cognitive Behavioral Therapy.

Part 2, Chapter 3: If you don’t do this you are headed for trouble

Learn and remember people’s names.

Basically there are a bunch of cute anecdotes about famous and successful people who made an effort to do this. It’s fun to read. But also I don’t really need to be sold on the idea. Learning people’s names is great. Learn their names and use them.

There are kind of a couple tips on how to do this, but it mostly amounts to: ask and write it down as soon as you can. Finding a way to anki people’s names would be great.

Part 2, Chapter 4: An Easy Way to Become a Good Conversationalist

Spoiler: it’s to listen. So many anecdotes. And slogans: “To be interesting, be interested”. Also browbeating: “[People who] bust right in and interrupt in the middle of a sentence [are] bores, that is all they are – bores intoxicated with their own egos, drunk with a sense of their own importance”.

In the middle of this chapter I was pleased to see an anecdote that amounted to advocating upside-down support. Here’s a business owner talking about dealing with an unhappy customer:

I listened patiently to all he had to say. I was tempted to interrupt, but […]. When he finally simmered down and got in a receptive mood, I said quietly: “I want to thank you for coming to Chicago to tell me about this. You have done me a great favor, for if our credit department has annoyed you, it may annoy other good customers”.

It continues with a heaping of empathy and even models Beeminder’s take on competitors (or maybe the dog-eat-dog post is the better one to link to here?):

I told him that I understood exactly how he felt and that, if I were in his shoes, I should undoubtedly feel precisely as he did. Since he wasn’t going to buy from us anymore, I recommended some other woolen houses.

The punchline is the customer not only did a 180 but named his firstborn child after the company. No tattoo though!

In conclusion, be a good listener, ask questions the person will enjoy answering, encourage them to talk about themselves.

Discussion question: What if both people have read this book?? Are they like the north-going Zax and the south-going Zax? Ok, probably they will muddle through and take turns or something.

Part 2, Chapter 5: How to interest people

This was a short one. Just a couple anecdotes, that feel pretty similar to a lot of the other principles so far. Interest people by being interested in and listening to their interests.

Has a handful of anecdotes about people researching someone ahead of time, finding out their interests, and going to talk to them about that instead of the Business proposition, and then once they’ve buttered the person up, gotten them talking etc, they essentially think you’re really great and because they just got to talk about themself and what they like for 45 minutes, and then they want to do Business things with you.

Talk in terms of the other person’s interests.

I guess this is a fine “in” for business relationships, but what about personal relationships? I feel like there’s some common wisdom these days about how this is terrible dating advice or whatever, with stories in the popular culture (rom coms, sit coms, etc), about a person pretending to be super into what the other person is into and then winding up in a stupid place romantically because of it?

I guess this is a bit of GGG-ness involved. Like being genuinely interested in the other person’s interest (“listening to people talk about stuff they’re really excited about”) is different than feigning interest in that thing yourself in order to impress them or whatever?

So business-wise, maybe this comes back to being a good listener, and doing your research? Like you don’t have to pretend to be a stamp collector because the person is into stamps, you just ask them about the thing they’re interested in and let them talk about it?

Part 2, Chapter 6: How to Make People Like You Instantly

Ask yourself, “what is there about this person I can honestly admire?”.

Claims:

The desire to feel important, the craving to feel appreciated, is the deepest urge in human nature. It is responsible for civilization itself.
The feeling of doing something for someone (like making them feel important and/or appreciated) without them being able to do anything in return is a “feeling that flows and sings in your memory” long afterwards.

Also a discussion of the Golden Rule and its history and incarnations in different religions and philosophies. Zoroaster, Confucius, Lao-tse, Buddha, Hinduist teachers, Jesus.

That’s really all. Everything else is over-the-top anecdotes about how so-and-so expressed appreciation and made someone feel important and that person immediately wrote their family out of their will and gave their whole estate to the appreciation-giver. I may be mildly exaggerating, but it’s basically like that. One story I amused myself by assuming it was describing a gay love affair with the juicy parts carefully redacted. George Eastman (of Kodak fame) and James Adamson. It makes more sense that way. Adamson has a meeting with Eastman to pitch a contract and is warned to get to the point immediately because Eastman is so busy. Instead he ignores the point of the business meeting, runs his hands along the fancy wood of the office, flatters the fuck out of Eastman, one thing leads to another, and they go back to Eastman’s place for “lunch” and are “close friends” for the rest of their lives. It starts at the bottom of page 101 if you want to see if I’m exaggerating.

This ends Part Two. Review of the 6 ways to make people like you:

Get yourself to be genuinely interested in other people. Try Act As If for this?
Smile.
Use people’s names.
Listen. And get people talking about themselves.
Focus on and learn about other people’s interests. Seems like part of principle 1?
Make the other person feel appreciated and important – and do so sincerely.

I’m not sure why 1, 4, and 5 aren’t all facets of the same thing. Listen and show interest in the person and what they care about. Other-focus. Maybe spotlighting them? Then it incorporates 6 as well.

Part 3: How to win people to your way of thinking

Chapter 1: How to win an argument

Short answer: don’t get into arguments. People are bad at changing their mind and they get defensive and if you argue with them they’ll just dig their heels in and you will get nowhere.

What can you do?

Welcome the disagreement. Maybe the other person is right? Maybe you were about to make a huge mistake? Maybe you are going to learn something, or at least get to understand and know the other person better? What can you be thankful for in the disagreement?
Distrust your first impression. Probably your initial reaction is defensiveness, so probably you should distrust it, try to remain calm, try to see the defensiveness.
Control your temper.
Listen first. Don’t resist or defend or debate before you’ve actually heard them out.
Look for areas of agreement.
Be honest. By which he means look for things where you can honestly admit errors on your part, and apologize for them. That disarms the other person and reduces defensiveness.
Promise to think over your opponent’s ideas and study them carefully.
Thank your opponents sincerely for their interest. Anyone who takes the time to disagree with you is interested in the same things you are! Think of them as someone who really wants to help you.
Postpone action. Schedule a new meeting so you both have actual honest time to think it over. In preparation for that meeting ask yourself the hard questions like

“Are they right? Partly right? Where is the merit in their argument? What price will I pay if I win?” etc etc.

Finally, a quote from some opera singer:
“My wife and I made a pact a long time ago… When one yells, the other should listen – because when two people yell there is no communication, just noise and bad vibrations.”

Principle 1

The only way to get the best of an argument is to avoid it.

Chapter 1 sounds like a reprise of Scout Mindset. I especially like the tip of disarming the other person by finding errors of your own you can honestly admit and apologize for. (One of points in chapter 2 is that that will inspire the other person to match your fair-/broad-mindedness and want to admit that they too may be wrong.) Thanking debate opponents for helping find the truth is great too.

Also the opera singer strategy. Like taking turns being upset and when it’s not your turn, only listen.

Part 3, Chapter 2: A sure way of making enemies – and how to avoid it

This continues the soldier-mindset theme.

First tip: “If you’re going to prove anything, don’t let anybody know it. Do it so subtly, so adroitly, that no one will feel that you are doing it.”

Lots of clever quotes from famous people like Alexander Pope:

Men must be taught as if you taught them not
And things unknown proposed as things forgot.

If someone is wrong, be all diplomatic, like “Well, I thought otherwise but I may be wrong and if I am, I want to change my mind. Let’s examine the facts…”

I’m worried that that ends up being a ritual you perform when you think someone is wrong and they end up seeing through it and bristling the same as if you blurted “au contraire!”. I guess that’s where the subtlety and adroitness come in.

Another nice example of upside-down support:

Our dealership has made so many mistakes that I am frequently ashamed. We may have erred in your case. Tell me about it.

And the standard reason intelligence and correctness correlate so poorly: “most of our so-called reasoning consists in finding arguments for going on believing as we already do”.

Ben Franklin has advice! He forbade himself words like “certainly” and prefaced everything with “I conceive” or “I apprehend” or “I imagine” (probably “I feel like…” works too). And diplomacy again: when someone is wrong, first comment on a way or a sense or a circumstance in which they’re right. By saying things circumspectly, people feel less contradicted. And it’s less embarrassing for you when you’re wrong.

Then we got a yarn anecdote. Some executive concocted a better incentive system for employees of a yarn company. When they led by explaining how wrong the current system was, they were met with defensiveness. They tried again by asking lots of leading questions and get the stakeholders to basically develop the new system themselves. Then they loved it.

And a lumber anecdote. Some lumber customer was upset about the quality of the lumber they bought. Turns out it was the customer’s fault for buying the wrong grade. The way to make the customer see that was to “ask questions in a very friendly, cooperative spirit” and when they were warmed up enough, delicately incept the idea in the customer’s that some of the rejected pieces of lumber were within the grade they purchased. Sounds like hard work. But believable that that’s the only way you’d get someone in that situation to admit they were wrong.

Finally, throw in a weirdly juxtaposed pair of anecdotes about how fair-minded MLK and Robert E Lee are, and we’re done.

Concluding principle: Show respect for the other person’s opinions. Never say “You’re wrong.”

Part 3, Chapter 3: If you’re wrong, admit it.

Another Robert E. Lee anecdote in this chapter. And several others which all amounted to: “look how much respect you get from others when you take responsibility for your errors. Or even for other people’s errors!” Many of the anecdotes were like “look, this person avoided punishment by admitting error”.

This is kinda like that support principle, perhaps? (have we had occasion to bring this up in this review before?).

There are two cards, the “everything fine” card and the “omg this is terrible card”. If you, in customer support, take the “everything fine” (i.e., stop complaining, it’s not so bad, calm down, etc), then the other person will take the “omg this is terrible card” and wind up yelling for your supervisor. But if you take the “omg this is terrible” (i.e. oh no! we fucked up! how can i fix it? this is awful!, etc), then the other person has to take the “everything is fine” card, and you thus de-escalate the situation.

Principle number 3: When you are wrong concede quickly and emphatically.

Part 3, Chapter 4: A Drop of Honey

This starts with an somewhat implausible-sounding anecdote about Rockefeller. Apparently the workers of one of his “fuel and iron companies” were striking for higher wages and it turned into a literal war with some strikers being shot dead by the company. So in comes Rockefeller and starts visiting all the workers in their homes and then gives this inspiring speech and “the strikers went back to work without saying another word about the increase in wages for which that had fought so violently”. Those silly, feisty strikers.

(Carnegie talks about Lincoln a lot. Fun fact: Lincoln was more recent history to Carnegie than Carnegie is to us. (Lincoln was elected 76 years before Carnegie published this book, which was 88 years ago for us in 2024.))

Anyway, all these anecdotes are to drive home the point that before you try to convince someone of anything, first convince them that you are their sincere friend. Friendliness begets friendliness. Exhibit good will and enthusiasm.

A couple anecdotes were about getting landlords to decrease rent or pay for damage. Reminded me of [redacted]. The advice was to first gush about how amazing they and their property are.

Also the old fable of the wind and the sun competing to get a coat off a man. The harder the wind blows the tighter the man clutches the coat. When it’s the sun’s turn the sun just shines on him till he’s warm enough to take the coat off on his own. In conclusion: don’t let your counterparty go into soldier mindset.

Principle 4: Begin in a friendly way.

Part 3, Chapter 5

Begin in a friendly way, and then get them saying “yes, yes” immediately.

It’s a little like anchoring maybe? In fact, I wonder if it works if you get their “yes” about something entirely unrelated to the disagreement? I guess the previous chapter might claim so. Just be friendly first etc.

A “No” response, according to Professor Overstreet, is a most difficult handicap to overcome. When you have said “No,” all your pride of personality demands that you remain consistent with yourself. … Once having said a thing, you feel you must stick to it.

No terribly interesting anecdotes in this section. But he does frame the Socratic method as “keep asking questions that the other person agrees with until almost without realizing it, his opponent found themselves embracing a conclusion they would have bitterly denied a few minutes previously.”

Also he refers to Socrates as “the gadfly of Athens”, which is kinda hilarious, though also he quotes it when he says it, so apparently it’s not attributed to him.

Ends the chapter with a Chinese proverb, and a little bit of anachronistic praise of the Chinese (“the age-old wisdom of the Orient”, etc):

He who treads softly goes far.

(Aside, it’s interesting how his comments about Chinese culture are totally praiseful, but would absolutely count as racist nowadays. I guess because it’s stereotyping the entire culture and sorta white-washing it or something? Anyway.)

Principle 5: Get the other person saying “yes, yes” immediately.

Part 3, Chapter 6: The Safety Valve in Handling Complaints

Tiny chapter here with a handful of anecdotes:

Some businessguy got laryngitis before a big sales pitch. He just nodded while the one person at the client company who’d he’d previously been talking to did the talking. He kept wanting to jump in but couldn’t, but it became clear that it would’ve backfired. All the bigwigs in the meeting just gradually talked themselves into saying yes to the pitch.
A teenager story pretty reminiscent of how-to-talk. The mom kept punishing the kid but eventually she kinda gave up when the teenager snuck out or whatever and just was like “why?”. Magically the teenager started gushing about how the real treasure was the friends we made along the way or whatever, or she just needed to be listened to.
Job interview pro tip: Learn about the prospective employer, praise them, and get them talking about how great they are. Let them do all the talking and they’ll think you’re amazing.

Also don’t brag and stuff. I think that was about it.

Principle 6: Let the other person do a great deal of the talking.

Part 3, Chapter 7: How to Get Cooperation

Don’t give people the solution, ask questions and let them come to the conclusion themselves.

Anecdotes:

An manager of car salesmen called a meeting and asked the salespeople what they expected from him. Then what he could expect from them. Basically after getting to brainstorm all the stuff they wanted the boss to do, they then assigned themselves a bunch of responsibilites etc, and because they’d come up with them themselves they were enthusiastic in complying or something?
A designer trying to sell their designs to someone who always rejected them had the idea of leaving a series of unfinished drawings with the client and asked “do me a favor and look these over and tell me how you would want them finished”. Then collected the drawings and the suggestions, finished them with the buyer’s input, and sold them all.
A hospital administrator needing to buy new x-ray equipment is inundated with sales calls from manufacturers and has no idea how to choose. So one manufacturer asks for the guy to come give advice about their new machines, and then the administrator is all flattered and feels important, goes to look over the machines, and orders a bunch for his hospital.
Colonel House was an advisor to President Wilson, said “I learned the best way to convert him to an idea was to plant it in his mind casually, but so as to interest him in it – so as to get him thinking about it on his own account.” So like, he suggests a thing casually to the president, and the president appears to disapprove, but a few days later he trots it out as if it were his own idea and House thinks “great. let him have credit, just so long as he’s taking my counsel” or whatever.

So yeah, plant ideas gently, don’t worry about taking credit for a good idea if you’re getting your way, let people sell themselves on your products by letting them try it out for themself, or asking them e.g. “how would you improve this?” Also something to the effect that people will more readily follow rules they come up with themselves? Like when the elementary teacher at the beginning of the school year asks the class to come up with the classroom rules for the year, instead of just telling them what the rules are.

Principle 7: Let the other person feel that the idea is their own.

Part 3, Chapter 8: A Formula That Will Work Wonders for You

Ideological Turing Test. Simulate the other person and their motives accurately. Before an interview, have a clear idea of what you’re going to say and how you predict they’ll respond.
Praise and appreciate people. Anecdote about wife who’s into weeding, husband thought it was a waste of time. He joined her for an afternoon and found ways to praise and appreciate how hard she worked at it.
Anecdote: the bossy fire marshall. Instead of yelling at the teenagers for making a fire he was all friendly and explained how other, less responsible teenagers didn’t do such-and-such and caused fire damage. Favorite sentence: “They had saved their faces.”
Anecdote about someone way late on their car payments. They were super friendly and apologetic with the collector person and let them pour out their troubles with even worse delinquent customers. Voila, “take all the time you need, money-schmoney” etc.

Principle 8: Try to honestly see things from the other person’s point of view.

Part 3, Chapter 9: What Everybody Wants

I guess the idea here is don’t just see it from the other person’s perspective, but let them know that you do? Demonstrate to them? Give them sympathy.

“I don’t blame you one iota for feeling as you do. If I were you I would undoubtedly feel just as you do.” Like, definitionally, if you were that person of course you would feel like they feel.

Don’t hold it against people if they’re bigoted assholes, because it’s not really their fault, they’re the product of their environment. Or put in my own words: every person has their own story, and nobody thinks that they are the villain in the story. So don’t assume you know their story, and don’t attribute their motivations to inherent personality traits, as opposed to a response to some specific stimulus.

Anyway, anecdotes:

DC once gave a radio broadcast talking about Louisa May Alcott and misspoke, attributing her place of origin to New Hamphsire, rather than Massachussets. Then he got a bunch of flak for it, including a nasty letter from some woman who was terribly offended at his mistake. So he decides to try his hand at converting her hostility into friendship, and he calls her up to thank her for her letter. He is very sympathetic to her, and apologizes for the blunder and she falls all over herself apologizing for writing to him in anger. “Because I had apologized and sympathized with her point of view, she began apologizing and sympathizing with my point of view.”
an elevator repairman presenting a client with a day long closure of their elevator, when the client is like “dude! i can’t afford a closure of more than a couple hours!”, the repairman is all “I know, it’s going to be such a huge pain for you to have it closed for this long, it’s going to cost you so much business, but if we don’t do it now it could cause even more damage and be closed for days, which would be an even worse loss to your interests”
a piano teacher has a new student with very long fingernails which get in the way of playing piano. Instead of threatening or demanding the student cut the nails, she praises the girl’s nails and says what a sacrifice it would be to cut her beautiful nails, but she really believes that it would help the girl’s playing, so the student should think it over. And the student decides to cut the nails.
a prima donna opera basso who would complain to his manager that he couldn’t possibly sing in the morning, and the manager would be all “oh no! we absolutely must cancel the show, what’s the loss of a couple thousand dollars when you might be ill!” and every time the singer complains the manager is like “well of course you couldn’t perform”, and this works like reverse psychology i guess and the singer performs anyway?

So basically, tell people what they want to hear? Or with less cynicism, when you demonstrate your sympathy and understanding of their point of view, that builds trust and they are more likely to listen to your advice, or try to reciprocate with sympathy for your POV.

Principle 9: Be sympathetic with other person’s ideas and desires.

Part 3, Chapter 10: An Appeal That Everybody Likes

A person has two reasons for doing a thing: the one that sounds good and the real one. You can help them think of the former, i.e., appeal to their nobler motives.

Anecdote with an unhappy renter who was going to break their lease. “Years in the renting business have taught me something about human nature, and I sized you up in the first place as being a man of your word.” And then the landlord offers a gamble. He’s so sure the renter will do what’s fair he precommits to accepting the renter’s decision as final. Lo and behold, the renter decides not to break the lease.

Another clever one: Instead of “can you remove that picture of me; I don’t like it” say “my mother doesn’t like it”. You’re not some kind of monster who disrespects motherhood, are you? (That part is implied.)

Then there’s a think-of-the-children one, kinda similar.

Getting a famous person to do work for you by offering to make a donation to their favorite charity. A lot of sneaky stuff in this chapter!

Finally, another anecdote where a car dealership gets customers to adjust repair bills in the business’s favor by flattering the crap out of them (they’re the world’s expert on their own car) and then trusting them to do what’s most fair.

This has given me the idea to try [redacted].

Anyway, the nice version of this chapter is assume the other person (thinking of them as an upset customer) is sincere, honest, willing and anxious to do what’s right and pay what they owe, once they’re convinced it’s fair. Most people are indeed like that. They’ll react well if you make them feel you consider them honest, upright, and fair.

Principle 10: Appeal to the nobler motives.

Part 3: Chapter 11

Dramatize your case. Be a salesperson. Think like an ad exec. Be a Mad Man. Then gives several examples of kind of obnoxious salesy things. (A salesperson coming into the grocery store and dumping a bagful of pennies on the floor to demonstrate how they’re losing pennies on every transaction and should buy the salesperson’s thing! Sounds super annoying from the perspective of the person being sold to, if you ask me.)

Anyway, the most realistic-sounding one (to my ear), or “most potentially useful example of this” was an employee getting ignored by their boss, so they wrote out a form-letter with a “self-addressed stamped envelope” kind of deal, so all they had to do to reply was fill in the date & time. A little like sending a note to the boy you like in class that’s like “I like you ☐ yes ☐ no” or whatever.

Principle 11: Dramatize your ideas.

Part 3, Chapter 12: When Nothing Else Works, Try This

Stimulate competition. The first anecdote was pretty good. There were two shifts in an underperforming mill. At the end of a shift, the manager put the number of heats (whatever that is; a KPI) written in massive numerals in chalk on the floor. The next shift comes in and are like “what’s that?”. They find out and, with no other prompting, are all motivated to show up the other shift. And then vice versa again back and forth forever.

The next anecdote was basically like Biff goading Marty McFly to do anything he wants by calling him a chicken.

And the next one sounded similarly like naked manipulation: “I don’t blame you for being scared. It’s a tough spot. It’ll take a big person to do [thing I want you to do].”

The end of the chapter talked about how the biggest motivator for people at work is the work itself, whether it’s exciting and interesting and a chance for self-expression. People are motived to excel and to win. And to feel important.

Principle 12: Throw down a challenge.

Recap of the principles from Part 3 in my own words:

You don’t win arguments; first just listen and reflect. Scout mindset.
Respect the other’s opinions and never say “you’re wrong”.
When you’re wrong, be quick to admit it.
Start with lots of friendliness.
Get the person saying “yes” off the bat.
Let the person do most of the talking.
Let the person feel the idea is their own. Inception.
Honestly see things from the other’s perspective.
Say “if I were you I’d feel the same way you do”.
Appeal to their nobler motives; assume they’re fair, honest, etc.
Add some drama to make your point memorable.
Stimulate competition, goad them.

Part Four

Be a leader: How to change people without giving offense or arousing resentment

Chapter 1:

If you must find fault, this is the way to begin

This introduces the idea of the compliment sandwich. But Carnegie is making it open face. All the good stuff right out there on top, with some healthy whole grain bread underneath.

A barber lathers a man before he shaves him.

lol…

He’s got examples of doing it very baldly: Calvin Coolidge says to one of his secretaries “That’s a pretty dress you’re wearing this morning and you are a very attractive woman. Now, don’t get stuck up. I just said that to make you feel good. From now on, I wish you to be more careful with your punctuation.”

… the quite slytherin: A guy’s subcontractor is holding up the whole contract’s delivery, so he goes to visit the subcontractor’s factory, and gets a tour of the whole factory, praising the whole way. Then they go out to lunch, and chat like friends. So the guy still hasn’t brought up the thing that brought him out to the factory in the first place. Then after lunch the subcontractor brings it up and is like “well obviously i know why you’re here, but i didn’t think it would be so pleasant.” and then he fulfills the subscontract on time.

And finally, this gem of insight within an anecdote about an underperforming bank teller: “Once she realized I had confidence in her, she easily followed my suggestions and mastered this function.” I suspect this is a great way to think of it, when applicable; demonstrating that you have confidence in, or think well of your subordinate puts you on their side, so the criticism is less harshly felt, or more collaborative or something.

Principle 1: Begin with praise and honest appreciation.

Part 4, Chapter 2: How to Criticize – and Not Be Hated for It

First anecdote is people in a steel mill smoking under a no-smoking sign. Big bossman walks up, hands them each a cigar, and says “I’ll appreciate it, boys, if you will smoke these on the outside”

This one sounds like playing martyr: Bossman checks on a retail store and the clerks are chitchatting amongst themselves, ignoring a customer at the counter. Bossman waits on the customer himself and then nonchalantly hands the purchase to the clerks to wrap up.

Then there’s one about a mayor in Florida who claimed he had an open-door policy but his staff kept trying to helpfully shield him from people trying to get time with him. Rather than try to explain to his staff that they should do that less, he physically removed the door to his office.

Here’s one that seems obvious enough now: Simply replace BUTs with ANDs. Don’t say “you did awesome but [criticism]”. Say “you did awesome and [criticism framed as a way to be even awesomer]”.

The next one is philosophically interesting. Someone had contractors remodeling her house. They were good but they left a mess in the yard each night. So she and her kids clean up after after them, stack up all the materials, whatever, and then she lies through her teeth to the foreman: “I’m really pleased with the way the front lawn was left last night; it’s nice and clean and doesn’t offend the neighbors.” So of course they get the message and leave it that way from then on.

(It’s an exaggeration (technically a lie?) to say she lied through her teeth. What she said was technically true. She liked the way the yard was left. Passive voice. But by Gricean implicature, she was communicating that the workers left it that way. So I claim it technically counts as deception. Justifiable deception? Discuss.)

Penultimate anecdote is an Army Reserve sergeant getting reservists to cut their hair military-style even though they maybe don’t technically have to until they’re called to duty or something. Basically he guilts them into it. “You’re leaders, representing the Army Reserves, blah blah blah.”

Last one: Pastor writes a terrible sermon, spouse says “this would make an excellent article for The North American Review”. I.e., explicitly praising him but implicitly saying it doesn’t work as a sermon. Smart.

Principle 2: Call attention to people’s mistakes indirectly.

Part 4, Chapter 3

Principle 3: talk about your own mistakes before criticizing others.

It humbles you and stuff, and makes the other person like you more, and seem less like you’re lording stuff over them.

None of the anecdotes here seemed particularly profound and shrug. Pretty straightforward and common sense, right?

“Lord knows I’ve messed up this word a thousand times, you misspelled it too.”

All the better if your self criticism is sincere and relatable and relevant.

Part 4, Chapter 4: No One Likes To Take Orders

Tiny chapter with just 2 anecdotes.

Make suggestions and ask questions instead of giving orders.

“You might consider…”
“Do you think XYZ would work?”
“Maybe if we…”

Save the person’s pride, give them a feeling of importance, encourage cooperation rather than rebellion. Also stimulate their creativity. And the thing from before about making them feel like the decision was theirs.

(I think there’s a space-heatering danger though. [long story, maybe a future blog post] If you never give directives then your subordinates try to read your mind and infer them.)

Anecdote 1: Person parked their car illegally and idiotically. Demanding they move their car worked in the short term but turned the person into an enemy basically. Correct approach is like “if you moved your car I think other cars would be able to get in an out”.

Anecdote 2: Machine shop gets a huge order that’s going to require overtime and stuff. Instead of cracking the whip, bossman gathers all the employees and describes the big opportunity they have in accepting the huge order but it sadly doesn’t seem realistic. Then the employees rally and have lots of ideas for how they can pull it off. Clever clever.

Principle 4: Ask questions instead of giving direct orders.

Part 4, Chapter 5

Principle 5: Let the other person save face.

Don’t attack people in front of their peers. I mean, don’t attack people in general, but demonstrating respect and appreciation are good.

Anecdote 1: an accounting firm has to let go workers seasonally after the tax rush. when they just say “hey, we don’t need you any more. tax rush is over.” the person leaves kinda bitter, and then you have to find a whole new set of people next time. if instead you give them an honest and kind evaluation of their work and say you appreciate them etc, then they are more understanding, feel they’ve been a good worker, and are likely to remember you more fondly even though they’ve just been downsized. better working relations going forward, and if they are available next tax season, they will want to work with you again.

Anecdote 2: a boss dresses down a manager for a mistake in front of all his peers, and he soon after quits and goes to get a job with their competitor where he’s doing just fine, thanks. a different person in a different job has the experience of admitting a mistake in front of their peers, and their boss giving them space to save face “people often make mistakes on new projects, i’m sure you only messed up because of inexperience, and not incompetence. i’m sure you won’t make mistakes in the future once you’re more experienced.”, and the person goes away feeling confident and determined to not let the boss down in the future.

So yeah, don’t be a jerk. Give people an opportunity to save face.

Part 4, Chapter 6: How to Spur People On to Success

The secret is to treat them like dogs. Specifically, dogs in obedience school. You lavish praise on every tiny improvement.

Five anecdotes: First, a famous opera singer whose teacher said he couldn’t sing as a student but his mother (really?) said he was so good, so he became world famous, QED. Next is Charles Dickens who sent his manuscripts to a million publishers and they all hated him but then one of them published him and that turned him into Charles Motherfucking Dickens because, you guessed it, praise. (Interlude in which Carnegie emphasizes his own sheer credulity by citing debunked ideas of B.F. Skinner. Except I think Carnegie predated most of Skinner’s work so this may have been added later.) Anyway, similar anecdote about H.G. Wells, who was ready to kill himself so his old schoolteacher said he was talented, et voila. Penultimate anecdote: praise your kids for small improvements instead of yelling at them. Ok, this one sounds fair enough. Finally, a talented employee in a print shop was a hothead about to get fired but the boss explained in heartfelt terms what exactly made him talented. Then the employee was great.

Dale emphasizes that this isn’t about flattery. You have to prove you really mean it. And that’s true of the whole book. It’s not a bag of tricks, it’s a way of life. It works when it comes from the heart.

But just to bring the credibility back down, it basically cites the canard about humans only using 10% of their brains. Except not literally so maybe it gets a pass. The point is there’s a lot of unrealized potential in people that you can draw out by praising and inspiring them.

Principle 6: Praise the slightest improvement and praise every improvement. Be “hearty in your approbation and lavish in your praise.”

Part 4, Chapter 7: Give a Dog a Good Name

Is that a reference to some saying? Anyway, this chapter is about praising a person for something you want them to do more of, like, instead of yelling at them to do more of it I guess? If you have someone’s respect, and then you praise them for having a certain ability, whether or not they actually do, then they’ll try to live up to that praise, and get better at the thing you praised them for. This is a lot like the dog-obedience school thing, is it not?

Anecdotes:

A mechanic’s work started going downhill after being real good for many years. The boss praises his past work and is like “Because you’re such a good mechanic, I thought you’d want to know that lately your work has been subpar.” and then the mechanic goes back to their old level of performance.
Shakespeare quote: “Assume a virtue if you have it not.”
Some old rich woman praises a servant girl from the hotel, and the girl believes her and starts taking better care of her appearance, and having more confidence and within a couple months she has a marriage proposal from the cook’s son.
“Hey, I know you’re so good at changing your mind, so I’m sure you’ll hear me out when I make these arguments to you about xyz.”
A dentist notices that his office cleaner hasn’t been getting the small details done, so he writes her a note thanking her for being so great, and saying “I know it’s a lot to get done in the short time you have, so if you ever want to take an extra half hour to really scrub the corners and polish the furniture up to your standards, I’ll understand and even pay you for the extra time.” and then she does the detail work without even using any overtime.
A 4th grade teacher knows she’s got a “problem kid” in her class this year, his reputation precedes him. So she starts out the year saying “I know you’re such a good leader, and I’m counting on your help to [do things].” and he eagerly falls into line and is a model student in her classroom.

So basically, people generally want to please, so Principle 7: Give people a fine reputation to live up to.

Give a Dog a Good Name — Is that a reference to some saying?

Apparently it’s referencing “give a dog a bad name and hang him”.

This is a lot like the dog-obedience school thing, is it not?

The dog obedience school thing was chapter 6. This chapter seems a little advanced for literal dogs.

Part 4, Chapter 8: Make the Fault Seem Easy to Correct

This one seems thoroughly in the water supply already but we can run through the four anecdotes.

Dancing

Dance teacher flattered and praised the guy and it made him want to be better.

Dale plays bridge

Same story. “Bridge is just memory and judgment so it will be so easy for you.”

Bridge teacher

Guy’s girlfriend is a bridge teacher and she tells him what a genius he is at bridge. He goes on to become a best-selling author of books on the game of bridge.

Flashcards for times tables

Kid was in a nasty car accident and was deemed to have brain damage and performed poorly at school. Dad did mathghost I mean flashcards with the kid and every time he set a PR for getting through the flashcards they’d hug and dance a jig. The boy grew up to win a Nobel prize in particle physics. Or at least started making honor roll or something.

Principle 8: Use encouragement. Make the fault seem easy to correct.

Part 4, Chapter 9: Making the other person happy to do what you suggest

Ways to make the other person happy about doing what you suggest involve considering what benefit they will get from doing it, and suggesting the thing in a way that is non-confrontational. Possibly also adding incentives to make it more attractive? This chapter feels less cohesive than a lot of the others.

Anecdotes

Telling someone they don’t get to do a job they were hoping to do, but implying that they’re too important to do it, so that they don’t feel put out or passed over.
Framing it such that “you’d be doing me a favor if…”
Assigning titles; Napoleon did this with special military titles, and medals, and calling his army “Le Grande Arme”
Adding incentives: kid doesn’t do his chore of picking up pears before the mower mows the lawn. Dad offers an incentive – I’ll give you a $1 a bushel – but also a negative incentive – and charge a $1 for every pear I find out in the yard.

Keep in mind:

Be sincere. Don’t promise something you can’t deliver. Forget about the benefits to yourself and concentrate on the benefits to the other person.
Know exactly what it is you want the other person to do.
Be empathetic. Ask yourself what it is the other person really wants.
Consider the benefits the other person will receive from doing what you suggest.
Match those benefits with their wants.
When you make your request, put it in a form that will convey to the other person the idea that they will personally benefit, and how.

Principle 9: Make the other person happy about doing the thing you suggest.

In A Nutshell

Be a leader

A leader’s job often includes changing your people’s attitudes and behavior. Some suggestions to accomplish this:

Begin with praise and honest appreciation
Call attention to people’s mistakes indirectly
Talk about your own mistakes before criticizing the other person
Ask questions instead of giving direct orders
Let the other person save face
Praise the slightest improvement and praise every improvement.
Give the other person a fine reputation to live up to
Use encouragement. Make the fault seem easy to correct
Make the other person happy about doing the thing you suggest

PS: Soon after we finished this book brigade, CGP Grey posted a podcast about this book.

Postminder: Beeminder ♥ The Beeminder Forum (And Other Discourse-powered Fora)

dreev — Wed, 26 Mar 2025 00:00:00 GMT

Tada! You can now beemind posting to the Beeminder forum, or any forum that uses the same forum software. Which is a lot of other fora. All the cool kids use it. It’s called Discourse. Not to be confused with Discord (more on this below), the instant messaging platform. Which, we suppose, is what the very coolest kids use exclusively. But they are objectively wrong. Having a community that uses chat exclusively is terrible. When people in a community help each other, you want that preserved for posterity, not scrolling away into the abyss.

Ok, but, confession: this integration adds zero additional functionality beyond what RSSminder already provides. The difference is that, try as we did to make “feed Beeminder graphs directly from RSS feeds” normal-person-friendly, approximately zero normal people were persuaded. And even for übernerds, friction matters.

So now it is dirt simple to beemind posting more often. The most technically challenging part is knowing your username: [1]

Why would I want to do this?

We have eight reasons! Seven of them are covered in our previous blog post, “The Power of Rearticulating Insights in Your Own Words,” about why, in general, it’s surprisingly valuable to write forum posts (or blog posts). The eighth reason is that beeminding your forum posts is the price of admission to the Beeminder community Discord.

Just link us to your Beeminder forum-posting goal in a new forum thread I just created — Postminder Thread — and you’re in. Probably. We also have to personally like your forum posts. The Beeminder Discord is highly exclusive, ok?

Why do you hate your users this much?

It’s not like that! But we do have two reasons for being so restrictive about who we let into the Discord. First, it’s very cozy and high-quality and non-overwhelming, and we hope to keep it that way. Second, as we alluded to above, it’s a safeguard to keep the Beeminder community Discord from cannibalizing the amazing Beeminder Forum.

Or ignore the bit about the Discord (again, not to be confused with Discourse, the forum software Beeminder and many others use) and focus on the forum. If you’re looking for other ideas for forum posts, here are some recommendations:

The Introduction Thread
The Gratitude Journaling Thread (thousands of entries over 5 years)
Start a journal in the Life category

If you’re sold, head to beeminder.com/new, pick the Discourse/Postminder icon from the list of integrations, and answer the questions about how much you want to commit to posting. Or, here, have a button:

PrePOSTerousness!

Footnotes

[1] If knowing your username is too much of a technical hurdle, then, realistically, this isn’t going to work out.

Let's See If OpenAI's Deep Research Can Tell Us about the Science of Beeminder

dreev — Sun, 02 Mar 2025 00:00:00 GMT

This is a slightly more polished version of something I posted in the Beeminder forum. The forum is also the place for ongoing discussion about this.

OpenAI’s Deep Research tool was made available to premium users the other day and I tried it out with the following prompt:

“What does the science say about Beeminder’s efficacy?”

It asked a couple followup questions (Beeminder specifically? No. Any specific outcome measures in mind? Also no.) and then it thought for 6 minutes and churned out a report. I’m quoting it in its entirety (for Science!) with my reactions interspersed. But I’m mercifully collapsing most of it so you can mostly just read my comments and reactions. (The only edits I’ve made to the AI’s words is replacing repeat citations with “[ibid.]”.)

Here we go!

Efficacy of Commitment Devices (Beeminder and Similar Tools) in Goal Achievement

Commitment Devices and Self-Binding Strategies

“blah blah blah Odysseus had himself tied to the mast and Cortés burned his ships”

Commitment devices are voluntary arrangements that people use to bind themselves to a future course of action, typically by introducing immediate consequences for failure. Beeminder, StickK, and similar services fall into this category – they let individuals set goals and attach stakes (often monetary or social) to staying on track. This concept of self-binding has a long history: for example, the mythical Odysseus had himself tied to the mast to resist the Sirens, and historical figures like Cortés burned his ships

My first nitpick! I thought he scuttled them rather than burned them, and Claude is well-actuallying harder and saying he dismantled them. Ok, not important!

“commitment contracts and distracting website blockers yada yada”

to eliminate the option of retreat (Commitment contracts as a way to health). In modern life, commitment devices can take many forms – from apps that block distracting websites at work, to “Ulysses contracts” for personal behavior change, to formal commitment contracts where you pledge money that you forfeit if you don’t meet your target.

I’m vaguely crinkling my nose at this without having any concrete objection. Maybe the writing is just too LLM-y.

“[…] locking in today’s good intentions, curbing vices”

These tools create external constraints or incentives that make it harder for our future selves to give in to temptation, essentially locking in today’s good intentions. Notably, many people do choose to use such self-imposed contracts to help achieve goals: evidence shows individuals have used commitment contracts to save money, complete assignments on time, and curb vices like smoking [ibid.]. The premise is that by pre-committing and putting something on the line, we can overcome procrastination and impulsiveness that might derail our goals.

Citing the same 2012 paper twice, I see. So far I’m gratified by how much better the Beeminder blog is than this stuff.

“blah blah present bias … (sometimes termed ‘akrasia’ in psychology)”

Behavioral Economics Basis: Present Bias and Loss Aversion

From a behavioral economics perspective, commitment devices target well-known biases in decision-making. One key issue is present bias (or hyperbolic discounting): we tend to favor immediate gratification over long-term benefits, which leads to procrastination and inconsistency in pursuing goals (sometimes termed “akrasia” in psychology).

It’s philosophers (and rationalists) who use that term. I guess we’re still in nitpick territory.

A commitment contract tries to counteract present bias by making the long-term goal salient in the present – often by attaching an immediate cost to slacking off.

Getting a little repetitive here.

For instance, Beeminder will charge you a preset amount if you fall off your “goal road,”

Severe nose-crinkle.

thus bringing a future penalty forward to the present moment each day. This leverages loss aversion: people are generally more motivated to avoid a loss than to achieve an equivalent gain [ibid.].

No no no. Or, well, it’s complicated. I mean, we got this wrong for years ourselves so I don’t know how stridently to complain. But at least a little stridently because we did put the right answer on the internet for anybotty to see: “Loss Aversion Aversion”.

Research underscores this principle – putting one’s own money at risk (a potential loss) tends to motivate better than offering a reward of the same size for success (Association Between Monetary Deposits and Weight Loss in Online Commitment Contracts - PMC). In other words, the fear of losing $100 can drive behavior more strongly than the hope of winning $100, in line with prospect theory.

This much is true. Anyway, see our Loss Aversion Aversion post and the post right before it for the rest of the story.

Commitment platforms harness this by using financial stakes or penalties as motivators. For example, StickK allows users to stake money which they forfeit to a charity (or an “anti-charity” they hate, for extra motivation)

I just have to mention again for the record how much we hate that.

if they fail – a design aimed at exploiting loss aversion and even moral stakes to push adherence [ibid.]. By imposing a cost for failure, these tools change the cost–benefit

Just to turn the nitpickery up to infinity for a second, because I’m honestly surprised to see an LLM do this, why on earth did it hyphenate “cost-benefit” with an em dash? (Claude, and ChatGPT itself, agree that this is wrong when I ask about it in isolation.)

“[…] tools rely on what is essentially negative reinforcement or punishment”

calculation: skipping a workout or study session no longer just means slower progress; it means an immediate hit to your wallet or reputation. This helps align our short-term actions with our long-term goals by increasing the immediate consequences of procrastination.

Reinforcement and Accountability Mechanisms

Commitment devices operate via reinforcement mechanisms that alter the incentives around behavior. Many of these tools rely on what is essentially negative reinforcement or punishment:

Technically not the same thing, as we helpfully explain in a classic guest post by an actual psychologist (Beeminder user @tierrabluebird): “Negative Reinforcement ≠ Punishment”.

avoiding aversive outcomes, Beeminder’s sting, immediate feedback, social accountability

if you do the desired behavior (e.g. stick to your daily goal), you avoid an aversive outcome (losing money or facing an embarrassing consequence). Conversely, failing to do the task triggers a penalty, which is akin to a punishment intended to reduce future lapses. For instance, with Beeminder you “pay the price” when you go off track – this sting is supposed to reinforce better habits going forward. From a psychology standpoint, immediate consequences are crucial for behavior change, and commitment contracts ensure immediate feedback (often aversive) for not following the plan. In addition to monetary penalties, these services often incorporate accountability and social reinforcement. Some allow or encourage goals to be made public or involve a referee to verify outcomes. Research suggests public commitments can enhance success – making a commitment in front of others or to a partner adds social pressure and positive accountability, which has been linked to higher adherence (The effect of commitment-making on weight loss and behaviour change in adults with obesity/overweight; a systematic review - PMC)

I’m falling asleep but nothing wrong exactly here.

“[…] core idea of tools like Beeminder and StickK is to harness our stronger sensitivity to negative outcomes”

In practice, a user might appoint a friend as a referee who must confirm they actually went to the gym, or they might share their Beeminder graph publicly for encouragement and a bit of healthy shame if they falter. This adds a layer of social reinforcement (praise or encouragement when you succeed, concern or disappointment if you don’t). Some commitment platforms and productivity apps also use positive reinforcement strategies – for example, Habitica (a gamified habit app) rewards good behavior with points and treats failure as a mild in-game loss. However, the core idea of tools like Beeminder and StickK is to harness our stronger sensitivity to negative outcomes.

Ok when it’s put like that I really have to object. The gains-vs-losses asymmetry in the Kahneman and Tversky sense is emphatically not fundamental to how Beeminder and StickK work.

Notably, even simple tech interventions that remind and reward behavior can have an effect: for instance, apps that send reminders or provide social feedback for good habits have shown some effectiveness in improving health behaviors (Association Between Monetary Deposits and Weight Loss in Online Commitment Contracts - PMC).

So repetitive with these citations.

so much blah blah blah with specific claims about weight loss RCTs and then, “yet the magnitude of weight loss achieved is moderate, and maintaining the weight loss after the commitment period remains challenging”

By combining immediate penalties for failure with real-time progress tracking (e.g. Beeminder’s graphs) and sometimes rewards or positive feedback for success, commitment devices create a reinforcing loop that keeps one’s focus on the goal. The behaviorist notion at work is that consistently enforcing consequences (good or bad) for behavior will gradually build habit strength and self-control.

Evidence of Effectiveness from Research

A growing body of research in behavioral economics, health psychology, and productivity science has investigated how well commitment devices actually work. Here are some key findings from studies on their efficacy:

Weight Loss and Health: Several randomized controlled trials (RCTs) have tested commitment contracts for weight loss. In one RCT, roughly 50% of obese participants assigned to a 16-week commitment-contract program (with financial stakes for meeting weekly weight targets) succeeded in achieving their weekly weight loss goals, compared to only 10% of those in a usual-care control group (Commitment contracts as a way to health - PMC). A follow-up 32-week trial found similar initial advantages for the commitment group [ibid.]. However, in both cases the difference waned after the contracts ended – the commitment device spurred more weight loss during the intervention, but the benefits largely disappeared a few months after the program, as participants often regained weight once the financial stakes were removed [ibid.]. This suggests commitment contracts can powerfully jump-start behavior change in the short term, though maintaining long-term change may require either extending the commitment or transitioning to other motivators. A 2019 systematic review of weight-loss interventions using commitment strategies (mostly non-monetary behavioral contracts) likewise found modest short-term benefits. On average, adding a commitment contract to a diet/exercise program increased weight loss by about 1.5 kg more than the program alone in the short term (few months) (The effect of commitment-making on weight loss and behaviour change in adults with obesity/overweight; a systematic review - PMC). Some effects persisted at 12 months (about 1.7 kg greater loss than controls) [ibid.], but evidence was mixed and quality varied. Interestingly, that review noted commitments were most effective when they were made publicly and focused on speci fic diet changes, rather than private or solely exercise commitments [ibid.]. Overall, the research indicates that for weight management, commitment contracts do improve adherence and outcomes in many cases – people stick to diets or exercise regimens better when they’ve put skin in the game. Yet the magnitude of weight loss achieved is moderate, and maintaining the weight loss after the commitment period remains challenging.

Yes, obviously you can’t let the commitment contract end! See also our review of Katy Milkman’s research.

(Also I’m not taking the time to verify all the specific claims above about commitment devices for weight loss. We did that once for smoking cessation.)

“contracts with […] stakes led to […] more weight loss”

Online Commitment Platforms (Naturalistic Data): Real-world data from commitment websites also support their efficacy, while highlighting some caveats. One study analyzed over 10,000 weight-loss contracts on StickK.com, where users set a weight goal with optional monetary stakes. The findings showed that users who put down a monetary deposit (choosing contracts that penalized them for not losing weight) achieved greater weight loss than those who made no financial commitment (Association Between Monetary Deposits and Weight Loss in Online Commitment Contracts - PMC). Specifically, contracts with an anti-charity clause (money goes to a hated cause if you fail) or other stakes led to about 0.25–0.33% more weight loss per week compared to contracts with no money at stake [ibid.]. [/details]

🚨 This is wrong awful bad science. Hopelessly confounded, not at all establishing causation. (record scratch sound) Wait, sorry, I freaked out too soon, ChatGPT acknowledges this a little further on. So this is merely bad writing.

Over a few months, that adds up to a meaningful extra loss. This aligns with the idea that adding a loss incentive increases motivation. The same analysis found that participants without any verification (no third-party or photo weigh-in) reported larger weight losses than those with verification [ibid.] – suggesting a few may have been cheating the system when no one was watching.

Ooh, I’m not sure I knew about that result. That’s important.

Importantly, because this was not a randomized experiment, there’s a potential selection effect: it could be that more motivated individuals are the ones choosing to put down money, which might partly explain their better outcomes. The authors indeed cautioned that while voluntary commitment contracts were associated with greater weight loss, it’s unclear how much is due to the incentive itself versus the user’s pre-existing motivation [ibid.]. In other words, these tools attract people who are already determined, so success rates from user data may look high. Nonetheless, the fact that thousands willingly use these platforms and that those who raise the stakes tend to do better is consistent with the experimental evidence that commitment devices can be effective.

Yes, thank you. (Also eternal shame on StickK for intentional deception about this in their marketing copy back in the day.)

blah blah about how much commitment devices reduce smoking

Smoking Cessation: Commitment devices have also been applied to quitting smoking. A notable field experiment in the Philippines offered smokers a bank account (nicknamed CARES) where they could deposit money that would be forfeited if they failed a nicotine test after 6 months. Only about 11% of eligible smokers actually opted into this rigid contract (showing that many are wary of self-imposed penalties), but those who did showed significantly higher success in quitting (Put Your Money Where Your Butts Are). In fact, smokers randomly given the opportunity to use the commitment account were 3.4–5.7 percentage points more likely to pass a nicotine urine test at 12 months than those in a control group with no such option [ibid.]. This was a statistically significant increase in quit rates attributable to the commitment device. Although the overall uptake was low, this study provided direct causal evidence that a voluntary commitment contract can improve behavior change outcomes for those who choose to enroll.

It’s a bit more complicated than that but … UPDATE: never mind, our blog post is about a different smoking cessation study. I don’t have an opinion about the claims about this earlier study.

Similar results have been seen in other incentive-based smoking studies – people offered a refundable deposit or loss incentive to quit often achieve higher short-term cessation rates than those given no incentives (or even those given equivalent reward-only incentives). As with weight loss, a pattern emerges: while the commitment is in force, success rates improve, but maintaining the behavior long-term can require sustained efforts or repeat commitments.

Productivity and Other Behaviors: In domains like work or academics, formal research is a bit sparser, but existing studies and observations support the efficacy of self-imposed commitments. For example, a classic study by Ariely

🤮

and Wertenbroch (2002) showed that when students were allowed to set their own deadlines for assignments (a form of commitment device), many students did set spaced deadlines rather than one last-minute deadline, and those who spaced out and self-imposed earlier deadlines performed better than those who did not (1333).

Despite Ariely’s involvement, this study still seems like solid research to me. Just has to be taken with a massive grain of salt now, obviously.

The catch was that self-imposed deadlines, while helpful, were not always fully optimal – students who had externally imposed evenly spaced deadlines did best of all [ibid.]. This suggests people understand their procrastination problems and will use commitments to improve performance, though they might not choose the perfect commitment structure without guidance. In the workplace, commitment techniques are often recommended by productivity experts: for instance, publicly committing to a goal or staking money on completing a project by a due date can combat procrastination. While rigorous studies in general productivity (like coding, writing, etc.) are fewer, the same principles apply. Some field research in personal finance found that offering a commitment savings account (where people couldn’t withdraw until reaching a savings goal or date) significantly boosted savings rates for those who took it up – again indicating that self-control tools can work when adopted. Additionally, anecdotal evidence from user communities (such as Beeminder’s forum

Oh hi. (I originally wrote all this in the forum.)

and habit-tracking communities) often reports success in using these tools for things like studying for exams, writing daily, or even managing ADHD-related behaviors. In summary, across various behavioral domains – health, finance, work or study – commitment devices tend to improve goal attainment relative to having no such binding mechanism in place. The strongest evidence comes from health behavior trials, but theory and smaller studies support that the effects are transferable to other self-control challenges.

Limitations and Considerations of Commitment Tools

Despite their promise, commitment devices are not a one-size-fits-all solution, and research has identified several limitations and potential pitfalls:

Limited Uptake and Acceptability: Many people choose not to use commitment contracts, even when they have the option,

See the Type Bee personality.

because the idea of self-imposing penalties can be aversive. Studies find that the higher the stakes required, the fewer people are willing to sign up (Commitment contracts as a way to health - PMC). For example, if a program asks participants to put down a large deposit as a commitment, enrollment rates drop off markedly. This means these tools often attract a self-selected group who are particularly motivated or confident. Low uptake can limit the population-level impact of commitment interventions – they help those who use them, but many individuals opt out.

Short-Term Efficacy vs. Long-Term Behavior: A consistent theme is that the benefits of commitment devices tend to be temporary.

Again, not if you don’t stop using them! Katy Milkman’s analogy is it’s like insulin for diabetes. You have to keep taking it indefinitely. (Though plenty of Beeminder users aver that they’ve successfully habituated certain behaviors and no longer needed Beeminder for them.)

“[…] these tools might be best used as a kickstarter or a bridge to longer-term habit change, rather than a permanent crutch”

While the contract is active, people stick to their goals more (e.g. losing weight, not smoking, meeting deadlines), but once it ends, old habits can resurface [ibid.]. Unless the commitment is renewed or transitioned into intrinsic motivation, the behavior change may not fully “stick.” Researchers have noted that when the external enforcement is removed, there is often regression, much like stopping a medication leads symptoms to return [ibid.]. This raises the question of how to maintain progress after the commitment period – an area for further innovation (such as tapering stakes, building habit formation alongside the contract, etc.). It also implies that these tools might be best used as a kickstarter or a bridge to longer-term habit change, rather than a permanent crutch.

Crutch you say?

Selection Bias in Outcomes: As mentioned, there is a self-selection factor – those who voluntarily use apps like Beeminder or sign commitment contracts might be inherently more disciplined or motivated. So their high success rates aren’t wholly generalizable to everyone. For instance, data showed users who risk money lost more weight, but it’s uncertain whether the incentive caused the difference or if those users were already more driven (Association Between Monetary Deposits and Weight Loss in Online Commitment Contracts - PMC). This means that if someone is very averse to the idea of a commitment device, forcing them to use one might not magically change their behavior; these tools work partly because the user buys into the concept.

Yes, good to reiterate this, and well said.

Gaming and Compliance Issues: A commitment device is only effective if the user plays by the rules. There is potential for gaming the system or cheating to avoid penalties. An example is reporting false progress: in an online study, those without verification of outcomes claimed more success, suggesting some may have lied about their results to escape losing money [ibid.]. Similarly, someone could set an easy goal and achieve it with minimal change, or find loopholes (for instance, if you commit to “no desserts” but then redefine what counts as dessert). Robust commitment platforms try to mitigate this (Beeminder, for example, often requires automatic data tracking or makes it tedious to cheat), but the risk remains that a determined person might undermine the contract, limiting its effectiveness.

We have so much to say about this. Mostly that’s in our blog post on ice cream truck loopholes and Goodhart’s law.

“Commitment Overload” and Stress: Using too many commitment devices or overly harsh contracts can backfire. There is evidence that piling on too many commitments can overwhelm individuals, a phenomenon one researcher termed “commitment overload” (The effectiveness of commitment devices: field experiments on health behaviour change - UCL Discovery).

Amen. See also beating Beeminder burnout and beeminding easy things.

“Designing commitments that are stringent enough to matter but not so punitive that they induce despair is a delicate balance”

If someone binds themselves in numerous ways or sets the stakes too high, the pressure and cognitive load might become counterproductive. Likewise, the stress of facing constant penalties can potentially reduce enjoyment or intrinsic motivation for the activity. In qualitative follow-ups, some participants report feeling anxiety about the consequences, which in excess could harm engagement. The key is finding the “right dose of commitment” that motivates without causing burnout [ibid.]. Designing commitments that are stringent enough to matter but not so punitive that they induce despair is a delicate balance.

Fair. This is part of what’s behind our thinking in our series of posts about “derailing is not failing” / “derailing it is nailing it”.

“These tools are most effective when tailored to the individual’s preferences and when the person is genuinely committed to the goal itself (the contract is then just a nudge)”

Individual Differences: Not everyone responds equally well to commitment contracts. Some people are extremely loss-averse and will rigorously stick to avoid paying a penalty, while others might respond better to positive reinforcement or different strategies. The impact can vary across sub-groups, suggesting a need for careful targeting and personalization [ibid.]. For instance, an extrovert who cares about reputation might thrive under a public pledge with social accountability, whereas someone else might prefer a private, financial contract. If misaligned with personality or values, a commitment device might just frustrate the user. Therefore, these tools are most effective when tailored to the individual’s preferences and when the person is genuinely committed to the goal itself (the contract is then just a nudge).

Fair again.

“[…] to potentially lose money or be punished can be emotionally challenging etc etc”

Ethical and Psychological Considerations: Finally, there are considerations about reliance on external motivators. Some critics note that if one’s goals are only achieved because of a financial threat, the behavior change could be shallow – what happens when the threat is gone? Ideally, the period under contract helps build skills and habits (e.g. learning you can run daily or write regularly) which then continue. But there’s a risk that people don’t internalize the motivation. Additionally, setting yourself up to potentially lose money or be punished can be emotionally challenging, and not everyone wants to introduce that kind of adversarial relationship with their future self.

See again the derailing-is-good-actually series.

Ensuring that commitment contracts are entered into freely, and that users understand they are a tool for their own benefit (not a punishment for being “weak”), is important for their ethical use. Encouraging a mindset that paying a penalty is not failure but part of the learning process (as Beeminder’s founders often stress (What is Beeminder?)) can help mitigate negative feelings.

Finally, an actual citation to our own writing on this. But that particular citation doesn’t seem to make sense.

Conclusion

In summary, commitment devices like Beeminder, StickK, and related tools leverage powerful behavioral principles – by utilizing commitment contracts, loss aversion,

Again, not exactly.

“[…] quintupling the success rate in a weight-loss program compared to no commitment”

and immediate reinforcement, they help bridge the gap between our intentions and actions. Scientific research from behavioral economics and psychology generally finds that these tools can be effective: they have been shown to improve adherence to goals ranging from weight loss and smoking cessation to meeting deadlines and saving money. The use of loss-based incentives and self-imposed constraints often does improve the odds of success, sometimes dramatically so in the short run (for example, quintupling the success rate in a weight-loss program compared to no commitment (Commitment contracts as a way to health - PMC)).

That sounds wrong but I’m not taking the time to fact-check all the specific claims like this.

That said, the effectiveness comes with qualifications. Commitment devices are most helpful for those who are willing to use them properly, and their impact may diminish over time if not coupled with longer-term habit strategies. They are not magic bullets that guarantee success for everyone, but rather tools that give a willing individual an edge over their weaker impulses. In practice, many find them useful to break through procrastination or sticking points – the psychology of “knowing I’ll face a consequence if I fail” can indeed keep one on task. The evidence to date supports the idea that these devices help achieve goals, especially when carefully designed (and chosen by the person) to suit their motivational style. Future research is exploring how to optimize commitment contracts – for example, determining ideal incentive amounts, durations, and how to maintain behavior change after the contract – as well as how to increase their acceptability and use among people who could benefit. In the meantime, commitment devices remain a compelling option grounded in behavioral science, and when used judiciously, they are a proven strategy to reinforce goal tracking and behavior change (The effectiveness of commitment devices: field experiments on health behaviour change - UCL Discovery) (Association Between Monetary Deposits and Weight Loss in Online Commitment Contracts - PMC). They effectively help many individuals achieve their goals – turning intentions into action by making sure that tomorrow’s you faces the promises today’s you made.

Alright, ChatGPT’s Deep Research, thank you for the vote of confidence. I guess for my thoughts on what this means in terms of how long till the robot apocalypse you can see my new AGI Friday newsletter.

Gauges and Dials

dreev — Tue, 25 Feb 2025 00:00:00 GMT

This is part 4 of our Melzaminder Series. Previously we introduced the Mindful Munching with Metrics program, talked about scale weight as a metric, and talked about proxy metrics more generally. Here we talk about how the Metrics part of Mindful Munching with Metrics work.

Alright, any non-nerds who may have wandered in, we do have a normal-person-friendly way to explain this but for our core audience I think it’ll be clearest to jump straight to a graph and an equation.

Consider a simple proportional feedback control system with the variable you want to control on the x-axis and the action you take to control it on the y-axis:

In general, we need two daily metrics: one to measure your objective and one to measure what you’re doing to achieve it. The first we call your gauge and the second we call your dial. To keep things concrete, scale weight is the canonical gauge and daily net calories the canonical dial. You adjust your net calories in order to change your scale weight.

Other examples of possible gauges include bodyfat percent, waist circumference, maybe even athletic benchmarks. As we talk about in Proxy Metrics, the true underlying objective is being strong and healthy and living a long time and things like that. Just that we need a proxy for that that we can put a number on and monitor daily.

Your dial is some very rough measure of your net caloric intake. If you want to literally count calories you can but you can pick something easier or more fun to track. If you like intermittent fasting, the length of your daily eating window can be a dial. Or number of servings, number of total grams, or bites, or maybe probability of it being a fasting day — lots of interesting possibilities.

Before we say more about gauges and dials, here’s the equation for the above graph:

$$ d = \text{clip}_{[a,b]}\left(b-(b-a)\frac{g-(r-m)}{m}\right) $$

where:

$d$ is the dial setting (control metric on the y-axis)
$[a,b]$ is the domain of $d$
$g$ is the gauge (feedback metric on the x-axis)
$r$ is today’s value of the bright red line
$m$ is maxflux, how much below the red line you ideally want to be.

If you’re on or below maxflux ($r-m$), the dial is shoved all the way to easy ($b$) and you’re golden. If you’re on or above the red line ($r$), the dial is shoved all the way to hard ($a$); that’s you being in the red. For anything in between (the middle of the graph in white), lerp it.

This is all a generalization/simplification of the classic Hacker’s Diet. (See also the Bang-Bang Servo Diet. I’d be inclined to call this one “the feedback control diet” or the “thermostat diet” but Melanie says we really want to keep the d-word out of this.)

In the first post of this series, Melanie talked about the mindfulness side of Mindful Munching with Metrics. The ideal case for the program is that you can master mindful munching and make all the metrics madness moot. The gauge and dial can be thought of as guardrails — rules that go into effect when/if mindfulness alone isn’t sufficing.

Picking your Dial’s domain

Whatever your dial, you need to pick its domain by naming (a) the strictest possible setting, that you hope to never need but that, if so set, would unambiguously improve your gauge, and (b) the laxest possible setting — what you’d do without thinking about it. For example, if your control metric were daily calories consumed the range could be 0 to 3000 calories. If it were length of eating window it could be 0 to 16 hours.

As part of Melzaminder, we put all that in a spreadsheet (only participants can access it) which encodes the above formula. Intuitively, if you’re at or below the maxflux line on your Beeminder graph, hooray, your dial is turned to max laxity (b). If you’re on or above the bright red line, oh no, the dial’s laxity goes all the way down (a). If you’re halfway between maxflux and the red line, the dial will point to the midpoint of (a) and (b).

You don’t have to keep track of any of that math and can treat it as a black box. Enter the value of your gauge each day, see the computed dial value to know how much to eat/burn that day. The summary tab of the spreadsheet looks like this:

(More participants have gotten their gauges and dials set up since that screenshot.) Each participant also has their own tab of the spreadsheet with all their data. Again, if you’re mindfully munching successfully, the black box will peg the dial to its upper bound (shown as all green above), which means doing what comes naturally and listening to your body (and to Melanie).

Seeing How You Eat

Tied with net calories for most popular choice of dial in Melzaminder is plates of food, as measured by the number of squares in the See How You Eat app. Here’s a typical day for me, with 6 squares filled in:

(Ok, fine, I scrolled back a week till I found a non-embarrassing one.)

Using number of meals, i.e., squares in that app, is a nice compromise between simplicity and accuracy. Sure, there’s the ice cream truck loophole of eating a single plate stacked two feet high with fudge but (a) there’s the social pressure in the group to not abuse it like that, and (b) even if you did, that would just back you into the corner of eventually getting your dial pushed to zero plates/squares and that will move your gauge tout de suite.

I can feel Melanie grimacing at me here. Emphasis on part (a) above. Follow the spirit of the dial!

But I do also recommend picking the “impossibly strict” extreme to be something that makes failure kind of theoretically impossible, up to and including full-on fasting. Just work hard on the mindfulness side of things to avoid ever letting the dial get pushed that far. That makes it a true guardrail — something painful to crash into but still better than the alternative of careening off a precipice.

Exercise as a Dial?

Sensible as this sounds, we don’t recommend this. It’s surprisingly hard to control weight via exercise and movement. The lopsidedness of calories in vs out is downright discouraging. I’ll spend half a day building up a calorie deficit and then undo it all in five minutes of mindlessly eating cookies. So “keep dialing up your workout minutes or steps until your weight goes down” doesn’t tend to work by itself. “Keep dialing down your calories until your weight goes down” does. As does pretty much any other measure of the amount of food you’re consuming, even very coarse ones, like the example above of squares in your food collage.

Net calories

Other than the onus of counting them, I feel like net calories is the best of all worlds. This is what I’m personally using (and Bee as well). A Fitbit or smartwatch tracks calories burned pretty similarly to steps. So if you did want to focus on exercise for your dial you could hold calories consumed constant and do as much walking as dictated by your gauge.

The extrema I’ve chosen are -2000 (“impossibly strict”) to +2000 (“maximally lax”). That -2000 could be achieved, for example, by a fasting day with 500 calories burned on top of my basal metabolic rate (BMR) of 1500. And “maximally lax” would allow sitting perfectly still all day while eating 3500 calories, since I’d always get my -1500 BMR. Again, we don’t expect to hit the extremes, we just need to define theoretical bounds to make the math work.

Servings of veggies

This is the one Melanie’s using. If her weight is in the danger zone she ups her daily fistfuls of veggies as high as 10. But Melanie’s kept her dial fully green almost every day so this one is still fairly untested as a guardrail. My gut (which, admittedly, is a lying fiend) tells me that there should be a more explicit clause about the veggies displacing other food. I’m imaging my dial deep in the red, I’m stuffed full of ice cream, and still choking down another 7 handfuls of veggies in order to hew to the dial.

Maybe at the very least a rule that all the veggies have to get eaten first, before any other food? [UPDATE: Melanie confirms that this was in fact implied!] That still allows for the Dessert Stomach loophole but maybe Mindfulness can keep you from exploiting it. And at least you can’t totally back yourself into a corner if you commit to getting all the prescribed veggies in first.

Do mindfulness and metrics mesh?

I’m more and more excited and optimistic about this Mindful Munching + Metrics hybrid. My sense/prediction is that, long term, mindfulness lapses are natural, that mindfulness takes at least a trickle of ongoing mental energy, and that without guardrails mindfulness would curb but not eliminate gradual unbounded weight gain / loss of fitness. (Mindfulness is still very worth it, to be clear!) Mindful Munching without metrics might keep one’s weight steady or going down for a long period but then holidays or vacations or work deadlines happen. My impression from all of Melanie’s reflections on her own mindfulness progress is that this is true even for her. Her weight sometimes creeps up when not running a mindful munching group and comes back down during it.

Interestingly, you could think of the responsibility of running the group as itself a kind of guardrail. Melanie has to model all the best behavior! For a participant who wants this to work lifelong, setting up guardrails might be a necessary component. Metrics aren’t the only way to do that — you could rejoin the program a couple times a year? — but I think the metrics (gauge + dial) might be hard to beat in the long term.

But I’m very biased and extreme and bright-line-y in my own thinking. Also I’m finding that I’m not super susceptible to many of the pitfalls of diet mentality. I don’t relate to feelings of guilt, for example. So for me, hewing to my dial and practicing mindfulness go hand in hand and support each other rather than hinder each other. Or at worst it’ll be a bit less of a chance to practice assessing satiety and such if my dial dictates only one meal, say. But then the next day or the day after probably the dial will be back to mostly green so no real harm.

That’s all how I’m experiencing things personally. We’ll see how other participants assess this. And if you want that to include you next time, fill out the interest form.

PS: This all works fine for weight gain as well as weight loss. We have one participant keeping their weight above their bright red line.

Beeminder ♥ Fatebook

dreev — Tue, 11 Feb 2025 00:00:00 GMT

That’s Fatebook with a T. [1] Made by the impressive folks at Sage.

What is Fatebook for? It’s for predicting things. Specifically, it’s for publicly or privately logging your estimates of the likelihood of certain future events. Which events? It’s totally up to you. You can predict the course of history, or predict things about your own life. Or register disagreements you have with friends or colleagues to see who turns out to be right. It’s kind of like Manifold, but for less betting-minded people.

The real value to doing this is to improve your calibration. Being calibrated means that, for example, of all the times you claim there’s a 90% chance of a thing happening, nine times out of ten it does. Here’s one of our own calibration graphs (specifically, Oliver Mayor’s) from Fatebook:

If all the blue dots were on the green line, you’d be perfectly calibrated. It’s common for untrained humans to be overconfident. That shows up on a calibration graph as the blue dots being below the green line. Like, as the above graph reveals, when you say something is 90% likely to happen and only 55% of the time does it actually happen.

If you’re curious, here’s what it looks like to be underconfident, as Scott Alexander was for his predictions for 2019:

(He’s is still uncannily well-calibrated there.)

So that’s what Fatebook’s good for: training yourself to make better predictions. To quantify your uncertainty.

And how can I use Fatebook with Beeminder?

Well, when you set up a Beeminder goal to track your Fatebook predictions, we’ll encourage you to keep making more forecasts.

To create a goal, head to beeminder.com/new, pick the Fatebook icon from the list of integrations, and answer the questions about how much you want to commit to predicting. Or, here, have a button:

Get Guessing

Footnote

[1] Relatedly, when we talk about our Meta integration we’re obviously using the Greek word (meta as in beeminding your beeminding), not the confusing new name for Facebook-with-a-C. (We’re waiting for some company to name itself “The” next.)

Proxy Metrics

dreev — Wed, 29 Jan 2025 00:00:00 GMT

This is part 3 of our Melzaminder Series. Previously we introduced the Mindful Munching with Metrics program followed by To Weigh Or Not To Weigh, in which we argue in favor of poor maligned scale weight as a useful getting-in-shape metric. Here, we generalize the idea of metrics that aren’t quite the true thing you care about and yet still make for good Beeminder graphs.

Pop quiz! What is a metric?

(a) It’s short for the metric system.
(b) A fancy way to say “a lot”.
(c) A set plus a distance function satisfying the triangle inequality.
(d) Something you can measure numerically and make a graph of.

Got your answers locked in? If you said (a) then no, that “metric” is an adjective; ours is a noun. If you said (b) then no, you’re thinking of “a metric f***-ton”. If you said (c) then you probably thought you were winning all the nerd points but, alas, that’s a metric space. Nice try.

So, yes, the correct answer is (d). We’d be kind of surprised if anyone reading this blog didn’t know that, since we’ve been talking about metrics for a decade and a half. (See, for example, a classic one from over ten years ago on how to pick a good metric.) But if you’ve been reading along in confusion this whole time, now you know.

Alright, and a proxy metric? We talked about a classic one last time: scale weight is a proxy for fitness (or, less indirectly, is a proxy for bodyfat).

A lot of our own beeminding is proxy beeminding. Like how our User-Visible Improvements goal is a proxy for inexorable forward progress and never giving up. When what you really care about isn’t straightforward to measure and track, you can often find another metric that is and beemind that instead. Like Danny O’Brien’s browser hack. He wrote some code to automatically beemind how many websites he’d visited in a day as he found it correlates well with his distraction. More websites = more distraction.

Here’s another example: I track my vegetarian days, and in the past have tracked sugar-free days. What I really want is to reduce my overall meat and sugar intake. But Do Less goals can be fussy, with the Pessimistic Presumptive Reports and such (plus they’re a premium feature). Also keeping track of number of servings of sugar or meat becomes too easy to get vague about. If I don’t remember to update the moment I eat a cookie, it’s very easy to forget later in the day if I ate 1 cookie this afternoon, or was it 10? But sugar-free or meat-free days are easy and binary. Either I ate of the forbidden food or I didn’t.

In theory I could eat twice as much sugar on sugar-allowed days and defeat the whole point. But that would be gross, so in practice this goal succeeds in reducing my sugar consumption just fine.

(UPDATE: Since I wrote that, I switched to a Do Less goal for sugar, set quite generously, since I love sugar, and just have my “vegetarian days” goal.)

Alright, let’s engage listicle mode and enumerate examples, starting with those above, that we’ve collected with the help of the Discord and daily beemail subscribers:

Scale weight as a proxy for bodyfat or fitness.
Sugar-free or meat-free days as a proxy for eating less sugar or meat.
Automatic tally via browser extension of number of websites visited as a proxy for distractedness.
Timestamp of first Withings weigh-in as a proxy for wake-up time.
User-Visible Improvements as a proxy for inexorable forward progress.
Number of gym visits as a proxy for exercise.
Words written about a nebulous goal.
Time spent playing piano as a proxy for getting better at playing.
Time spent on a project as a proxy for finishing it.
Focusmate minutes as a proxy for focused work generally.
Number of homemade meals as a proxy for healthy eating.
Number of comment actions in Google docs as a proxy for progress on a jointly authored document.
Strength training reps as a proxy for strength.
Triangular beeminding as an elaborate proxy metric for quantifying the binge-iness of one’s alcohol consumption.
Hours in bed as a proxy for getting enough sleep.
Handfuls of veggies, or servings of protein, as a proxy for healthy eating.
Time spent reading as a proxy for mental health (if you’re Nicky).
Micro habits like setting foot in the gym as a proxy for working out (reasoning: lower activation energy).

“All metrics are wrong, some are useful” — Theo Spears

We realize there’s a fuzzy line on what counts as a proxy metric vs just a metric. Some goals are very direct, others are very roundabout, others are in between. Some proxies are just about making a tradeoff in favor of easier data collection. Some goals are inherently qualitative but we find creative ways to operationalize them.

As Beeminder user Anita put it, this is central to Beeminder (and life!). “A lot of what we want is pretty fuzzy — be healthy, be happy, be loved. Choosing what actions to take (what metrics to track) in pursuit of those goals is central to a life well-lived.”

This is also the key to choosing SMART(ER) goals. You have an underlying goal that you distill into something Specific that you can Measure and Attain — also it should be Relevant — in a Time-bound way. (And since that’s merely a proxy for the underlying goal, be sure to Evaluate and Reevaluate it.)

Beeminder Blog

Beeminder ♥ BoardGameGeek

Um, I just wandered in from BGG; what is Beeminder?

The Sheeple Strategy

Generalizations

Related Concepts

Gresham's Law of Beeminder Deadlines: Or Death to Deadline Snoozing

“Noooooo, I am unmoved, you can take deadline snoozing from my cold, dead hands”

The real best of all worlds

Footnotes

Neology

Related Reading

Footnotes

Feature Announcement: Dashboard Hotkeys

Refetch

Super-collapse

Anything else?

Footnotes

Beeminder ♥ Steam: Announcing the Steam Autodata Integration

Detail-ey-doodles

Against Pseudovernacular Jargon

The Worst of All Worlds

Suitcases of knowledge

Footnotes

Call Them "Quals"

What about “Specs”?

Uphill battles

Footnotes

Writing About Writing: Highlights from Inkhaven

The Principle of Delayed Commitment

“People commonly overgeneralize the virtue of prudence and preparedness to cases where it’s blatantly irrational”

Toilet seat toggling

The kids’ carseats

Bridge crossing

Premature optimization

Pre- or over-planning

Lazy evaluation

Delaying those ticket purchases

Inkhaven

Goodharting?

DIY Inkhavens?

Footnotes

The Protein Dial

Nitty gritty

“When your [weight] is not near the bright red line then you just ignore all of this”

“Your total calories for the day, C, must always satisfy C ≤ 4P/D”

Couldn’t you game this by…

The Snackwell effect and the so-called Big Fat Lie

Trying this in real life

The Five Theses of Dials & Gauges

Protein in various foods and food-adjacent products

Related Reading

Footnotes

Feature Announcement: Tare Tags

Background

Quick version if you don’t want to learn any (more) new things

The wonderful generality (and explicitness)

“We’re relieved that you now have to be less of a supergenius to make Beeminder behave as you want it to”

Whittle-Down use case

Oh my goodness the sheer number of things this fixes

Footnotes

Magic Strings In Datapoint Comments

#SELFDESTRUCT (or #THISWILLSELFDESTRUCT)

#DERAIL

#RESTART

Coming soon: #TARE

More Fine Print, The Hashtag Feature, and Alternate Syntax

Magic Strings are Anti-Magical

The worst anti-magic violation in all of Beeminder

Footnotes

Time Bridges: Beeminder on The Future of Life Institute Podcast

Downtime and DEFCON Levels

Related Reading

Why Danny Hates the Beemium Plan

1. It’s unbeeminder-y

2. We might kill it eventually

3. It’s expensive for us

4. Pledge short-circuiting is a honey trap

In conclusion

Deprecation Warning: Using “Deprecated” To Mean “No Longer Supported”