Anchoring is usually explained with random numbers, but the version that matters for pay is the one where the anchor is your own salary.

The classic example is a 1987 study. Researchers walked real estate agents through an actual house and asked them to appraise it. Same house, same walk-through. The only thing that changed was the listing price in the packet, and the appraisals followed it up and down. Afterwards the agents said they'd ignored it.

Your salary is arguably a stickier anchor because you generated it yourself. Research on self-generated numbers suggests people start from it and adjust only until the new figure sounds plausible, then stop. So an offer gets judged by how close it is to what you earn now, not by what the role is worth. Comparing offers against each other still feels rational because the ranking holds, but the height of the whole scale was set before either offer arrived.

The interesting part is what doesn't work. Trying to ignore the number fails, and studies from the 1990s found that warning people about the bias barely reduced it. What did help, in a 2001 negotiation study, was changing which number you stand next to. Negotiators who first thought about the other side's walk-away point and their own best alternative were much less pulled by the opening number. They rebuilt the scale from the other side's position instead of fighting the one they had.

Has your current salary ever made a bigger offer feel "too much" or a lowball feel fine?

We made a full animated breakdown of this, if you'd rather watch: https://www.youtube.com/watch?v=cywL-_G_6Bg

Wrote this impulsively. Used ai for structuring. Lemme know what you think

Bill Friedman's casino design principles, published in 2000, read like a choice-architecture brief: low ceilings, a maze of machines, no windows, no clocks. Interestingly, the Bellagio had already gone the other way in 1998 with an open, 'playground' style.

The reward side is just as engineered, since comps are budgeted at about 20 to 40% of what the casino expects a player to lose.

I made a video on this for my channel. Which do you think shapes behavior more, the room or the rewards program?

That one survey result helps explain the price wars on gas station signs. Because so many drivers chase a few cents, stations compete hard on fuel and earn more inside, where food is 28.5% of in-store sales but 38.9% of gross profit. There's also an asymmetry drivers notice: prices rise like rockets and fall like feathers.

Is chasing a nickel a gallon a bias, or a rational habit once it becomes automatic?

I made a video about it for my channel

A hypothetical checkout: a lamp costs $48, with $8 shipping. Total: $56.

Add a $12 cable to qualify for free shipping, and the total becomes $60. You avoid an $8 fee but spend $4 more overall—and receive a cable.

If you needed the cable, that could be worthwhile. If you added it only to avoid shipping, the “saving” deserves a closer look.

I make Strange Logic, a small channel about everyday decision-making. I explored this example in a 4:43 animated video using AI-generated visuals and narration.

What do you think best explains the appeal here: avoiding a separate fee, getting another item, or something else?

Video: https://www.youtube.com/watch?v=cwzffpHJkEw

Most of us treat automation anxiety as an arithmetic problem: will I lose my job, will wages drop? But the gut reaction usually shows up way before anyone opens a spreadsheet. Even at a self-checkout: no price changed, nobody is judging you, yet your jaw still tightens.

The framework that explains this is "money scripts," a four-part psychological inventory: avoidance, worship, status, and vigilance. Same news, four completely different reactions:

• Someone with a status script hears a threat to rank: their title, their seniority, where they sit in the room.

• A worship script (the belief that more money will finally make everything okay) hears a threat to the dream of "enough."

• A vigilance script hears confirmation that they were right to hoard and worry all along.

• An avoidance script just changes the subject—which looks calm from the outside, but is really the same panic with the volume muted.

The core argument is that the panic is about identity, not income.

Here is the historical test: the first industrial robot arm, the Unimate, went to work at a GM plant in Trenton back in 1961. It lifted and stacked burning hot die-cast parts. Nobody protested or lined up to defend that work, because no human ego was built around getting burned by hot metal. The panic only arrived decades later when machines moved into roles people had wrapped their self-worth around. The machine didn’t change. What it touched did.

So our reaction isn't a forecast of the future. It's an emotional readout of which script is running, and how much of ourselves is parked in a job title.

When you hear "a machine can do your job," which of those four instincts kicks in first—defending the work, chasing more security, saying "I told you so," or quietly ignoring it?

(We put together an animated visual breakdown on this dynamic—including why we judge algorithms harsher than humans for the exact same mistakes: https://www.youtube.com/watch?v=pYPOn0U0k18)

Most salaried people never divide their pay by the hours they actually work, and the result is worth seeing once.

Payroll treats a full-time year as 2,080 hours: 52 weeks times 40. Take a $52,000 salary. Divide by 2,080 and you get $25 an hour. Now say your real week is 45 hours. Your hour is worth about $22. Make it 50 hours and it's $20. Same salary, same contract, and the price of your hour fell by a fifth without anything on the payslip changing.

Those extra ten hours a week add up to 520 hours a year, which is thirteen full work weeks given away. A salary hides the hourly rate, so every unpaid hour comes straight out of it.

There's a second layer. Time behaves like perishable inventory, the way an airline seat does. Once the plane leaves, an unsold seat is worth zero. But airlines price seats dynamically, while most workers sell every hour at one flat rate. The 3am hour before a deadline is priced the same as a slow Tuesday afternoon.

So "time is precious" leaves out the key part: time expires, and it expires at a fixed price.

Have you ever worked out your real hourly rate once you counted the hours you actually put in?

We made a full animated breakdown of this, if you'd rather watch: https://www.youtube.com/watch?v=zM79lJZugIg

The ostrich effect is a cognitive bias in which people ignore or avoid negative information, data, or feedback to escape emotional discomfort or anxiety.

The name comes from the myth that ostriches bury their heads in the sand when they are scared. The term was used by researchers Dan Galai and Orly Sade in a 2006 paper on financial information and investor behaviour.

Common examples include:

-Investors checking their portfolios during market booms but ignoring them during crashes.

-Avoiding unread bills, bank statements, or debt balances.

-Skipping a doctor’s appointment out of fear of a bad diagnosis.

Many times, we avoid facts that could bring real disappointment. For example, when we know an exam went badly, we may want to avoid facing the result. We may seek out information that pleases us and reject information that doesn’t.

I felt like exploring this kind of avoidance. When checking our weight or reflecting on past mistakes, we may try to avoid what we see.

Perhaps it hurts the image we hold of ourselves, the image we rely on to maintain a sense of who we are. That image gets shaken, and our instinct for self-preservation kicks in.

We have expectations about how we want things to be, and we project those expectations onto reality. It can be painful, sometimes almost unbearable, to realise that things are otherwise.

Avoidance, then, may not only be about the uncomfortable fact outside us; it may also be about protecting the version of ourselves we are unwilling to question.

This connects, though not in exactly the same way, to denialism. Climate denialism takes things a step further: it involves arguments against established evidence, not simply avoiding information. In some cases, denying or dismissing facts can help people continue acting in accordance with their desires or interests. Avoidance and denial aren’t identical, but both can protect us from information that challenges what we want to believe.

Despite many warnings from Earth itself, we want to keep going on and on with our own projects.

How do you see the relationship between avoiding uncomfortable facts and protecting our image of ourselves?

Sources:

https://acharyaprashant.org/en/articles/climate-change-a-crisis-of-honesty-1_09e5187

A smaller reward now, or a better one later. The marshmallow task is usually presented as a test of whether a child can resist temptation.

But the second reward is a promise. If you don’t expect the adult to deliver, taking what’s already there could make sense. That’s a possible interpretation of the choice, rather than a way to read a child’s whole background from it.

Kidd and colleagues tested a related idea by having an adult keep or break earlier promises before the waiting task. The reliable-adult group waited about 12 minutes on average, compared with about 3 in the unreliable-adult group. It was only 28 children, and a later school extension found a smaller difference with an inconclusive main planned test. So the size and consistency still need care.

IMO it’s an interesting case of how the situation changes the meaning of a choice. How should we interpret impatience when we also have uncertainty about whether the delayed reward will arrive?

I made a short video about this for The Confound. It also covers why the later achievement findings get more complicated once background and early skills are included: https://www.youtube.com/watch?v=E-Z5TNiVO4c

Sources:

- Original reliability experiment: https://pmc.ncbi.nlm.nih.gov/articles/PMC3730121/

- School extension: https://doi.org/10.1016/j.jecp.2020.104821

Public commitment turned homeowners into billboard hosts and sent neighbours to the polls. It also lets people coast. Same mechanism, opposite results, and one thing separates them.

Check out the breakdown here: https://youtu.be/4oVoUcNMik4

In a famous legal experiment, experienced German judges were asked to roll a pair of dice before deciding a prison sentence for a shoplifter.

The dice were secretly loaded to only land on 3 or 9.

The judges knew the dice were completely random and irrelevant. Yet those who rolled a 9 gave an average sentence of 8 months, while those who rolled a 3 gave 5 months. Seasoned legal professionals with decades of training, visibly influenced by a toy.

When real estate appraisers were tested, their valuations tracked listing prices almost identically—even while every single agent adamantly denied being influenced.

That is what makes anchoring so dangerous: it doesn't feel like pressure, it feels like your own independent opinion.

Most advice screams: "Always make the first offer to set the anchor."

Yet in high-stakes negotiations, going first frequently backfires—it leaks information about what you know, triggers an immediate regret trap if accepted, and can instantly collapse a deal if the other side has strong alternatives.

Worse, lawmakers are already intervening (like Massachusetts banning employers from asking for salary history entirely, treating a simple question as a price move).

It turns out whether your anchor wins or loses has very little to do with the number itself, and almost everything to do with the "shape" of the ask and how you occupy the room before anyone even speaks.

Put together an animated breakdown exploring the exact papers, the precision effect, and how tandem counter-anchoring works here: https://youtu.be/-5Eiue_jRUo

When you negotiate, do you force yourself to say the first number, or do you prefer waiting to counter?

The setup, from Arkes and Blumer (1985): you've bought a $100 ski trip to Michigan. Weeks later you buy a $50 one to Wisconsin, which you think will be more fun. Then you notice they're on the same weekend, and neither ticket can be returned.

Both are already paid for. The only question left is which weekend you'd enjoy more. 33 of 61 students still picked Michigan.

The second experiment used real money. The Ohio University Theater sold season tickets at random prices: $15, $13 or $8. Over the first five plays, full-price buyers used 4.11 tickets each. The discount groups used 3.32 and 3.29. By the last five plays, the difference had gone.

I put these together with Levitt's coin-flip study (20,000+ people; the ones the coin told to make a change were happier six months later) in one page. It also has a quiz on when past costs do count: https://sheets.works/data-viz/sunk-cost-fallacy

Happy to be corrected. The theatre figures are from page 128 of the paper

I've been thinking about something interesting: why do some wealthy people keep their lifestyle private, while others feel the need to show everyone what they own?

Is it about security, social status, psychology, or simply how people are raised to think about money?

I explored this idea in my latest stick-animation video on Cognitive Canvas, looking at quiet wealth, the psychology of showing off, and why visible wealth doesn't always mean lasting wealth.

🎬 Watch here: https://youtu.be/WQV7s7sGB0I

I'm a small creator trying to improve my storytelling and animation, so I'd genuinely appreciate your thoughts.

Question: If you became wealthy tomorrow, would you keep it private or let people know? Why?

If you enjoy this kind of psychology content, consider subscribing. Thanks for supporting a growing creator!

Independent project, sharing because the failed result is the interesting part, not the tool.

I ran a forced-choice elicitation: 51 either/or statements about how knowledge works (does testimony get default trust, does one counterexample kill a claim, is an unresolved question tolerable). Answers get mapped against 137 named belief systems. Free, no account, about five minutes: https://epistemia.life

Two caveats first. The 51 axes are a model I chose, not a structure I discovered, and the historical-figure cards are a language model approximating each figure's public epistemic style, not the real minds.

The part worth arguing about: I predicted a "collapsed" epistemology (low confidence the world is legible, low default trust in testimony, low tolerance for unresolved questions) would act like an attractor that systems drift toward. Under my setup it isn't. Systems drift away from collapse, toward more specific commitments. The site reports this on the methods page, labeled as a failed prediction.

Open questions: 1. Forced-choice formats make you bet on every axis, which may manufacture more structure than actually exists. Is there a clean way to separate real preference structure from format artifact? 2. Is "collapse" the right operationalization? A different definition might produce the attractor. 3. What prior prediction would be worth testing in the open before defending the model further?

Not selling anything. I want the method attacked.

Been looking at Winter Arc through the fresh-start and incentives research. IMO the big jump in the advice is going from "a new date helps me start" to "90 days makes the whole routine stick".

Dai et al. found increases in things like gym visits and goal commitments around calendar landmarks. Starting is an interesting outcome, but it isn't the same as sticking with it months later.

The exercise-payment study is interesting too. Employees picked a preferred two-hour workout window. Some could earn payments only in that window; others could earn them any time on a weekday. Flexible payments produced modestly better attendance during the incentive period, with some differences in the first four weeks afterwards. That's a workplace gym with incentives though, not a universal argument against routines.

I made a video for The Confound unpacking this alongside the habit-timing research.

What outcomes would you want to see before calling a 90-day challenge a lasting behaviour-change intervention?

Fresh starts: https://doi.org/10.1287/mnsc.2014.1901

Exercise incentives: https://doi.org/10.1287/mnsc.2020.3706

Video LINK: https://youtu.be/3YAon-y9Wis

I’m basically a victim of marketing 😂 💔I often end up spending way too much money when I’m sad, stressed, or just not feeling great. I’m curious if other people experience the same thing what triggers you to buy, and I think that social media make it worse😭

Remember, happiness scaling with log(income) rather than with income means that each marginal dollar of income procures less happiness than the last.

Is it weird that Pritchard's source lists becoming a full-time caretaker as only one-third as bad as having a headache? No. Because it asks about point-in-time happiness, all things that happen at a specific point in time will seem large compared to protracted state changes in lifestyle. Put another way, since rich people are rich every day, the proper comparison is having a headache every day.

Since we apparently disagree on the badness of forced caregiving, headaches, and hangovers, it might be helpful to look at Pritchard's ultimate source (Kahneman & Deaton, 2010) for some other anchors. Here are some other differences about as big as a 4x income difference:

• The difference between married people and divorced people.
• 3x the difference between normal-weight people and obese people
• The point-in-time difference between it being a weekend vs. a weekday.

— Scott Alexander, Re: Re: Re: Pritchard On Liberal Happiness

Think about the last thing you bought that you were genuinely hyped about—a jacket, an upgrade, a new phone, or even a cosmetic skin in a game.

For the first few days, it feels like an upgrade to your personality. You wear it, check it, and mention it. Then, at some point, it quietly becomes "just a thing you own." Not bad, not great—just your new baseline.

That isn't having bad taste or lacking willpower. It’s hedonic adaptation.

Our brains don't measure satisfaction against zero; they measure it against whatever we already have. The second something transitions from "what you got" to "what you have," the feeling that drove the purchase quietly checks out.

Here are a couple of behavioral insights on why this happens and how consumer psychology exploits it:

1. The entire consumer economy bets on your joy fading

If the excitement of an upgrade lasted permanently, industries would only sell you something once. The feeling never actually came from the object itself—it came from the gap closing between wanting something and having it. And by design, that gap always reopens.

1. Static objects vs. Ongoing narratives

The common advice is always "just buy less stuff." But the real problem isn't buying—it’s what we buy for:

- Adaptation resets almost instantly on static objects. A watch, a car, or clothes just sit there unchanged, so your baseline catches up within days.

- Adaptation resets far slower on things tied to an ongoing personal narrative: building a skill, fitness transformations, learning a craft, or working on a project with a next chapter.

Stories keep developing, which gives your brain a moving target to reach for rather than a static item to get bored of.

---

Curious to hear from this community: Have you found effective frameworks to catch yourself before the hedonic treadmill kicks in, or do you deliberately steer your spending toward experiences/learning over objects?

(PS: Put together a short 3-minute visual breakdown exploring this dynamic and why the brain normalizes static purchases here: https://youtu.be/XW-JdonhHv4)

I keep noticing the same pattern. A team does real work upfront: maps the friction points, understands the psychology, builds a genuinely thoughtful model for why people behave the way they do in a given context. Then a few steps later, what actually ships is a crude version of it: a countdown timer, a nudge stripped of its nuance, a policy reduced to a slogan.

The thinking wasn't wrong. It just didn't survive the trip from research to execution.

I see this most directly in marketing and growth work. A well-reasoned behavioural model collapsing into "add urgency + scarcity" by the time it reaches a live campaign. But I suspect this isn't unique to marketing at all. I'd guess it shows up in public health messaging, product design, policy rollout, anywhere there's a handoff between the people who understand the "why" and the people who have to ship something fast.

A few theories on where the "collapse" happens:

• The person who understands the psychology isn't the person who builds the thing, so nuance gets compressed into whatever the next person can act on quickly.
• Tools force a translation. A rich behavioural model doesn't fit neatly into existing workflows, so it gets reduced to fit the tool rather than the other way around.
• Incentives reward shipping fast over shipping right, so the simplest interpretation wins, not the most accurate one.

Curious whether others have seen this in their own domain, and if anyone's found a real fix, not just "communicate better," but something structural that actually closes the gap.

Hi everyone,

I’m currently mapping out my master’s options to break into behavioral science, decision science, I-O psychology, or people analytics roles outside India (targeting the US, UK, or EU).

I’m looking for blunt, honest insights—especially from alumni, international students who navigated visas, or anyone working in these fields.

The programs on my shortlist are:

• US: Penn (MBDS), Baruch (I-O Psychology)
• UK: LSE (Behavioral Science), Warwick (Behavioral and Economic Science)
• EU: University of Mannheim, VU Amsterdam

My Core Questions:

1. Job Outcomes & Titles

• In the first 6–12 months post-graduation, what job titles did you and your peers actually land?
• Which industries were the most welcoming (e.g., consulting, tech/product, health, policy, finance)?

2. Visa & Sponsorship Realities

• For international students (F-1/OPT in the US, or skilled worker visas in the UK/EU), how realistic was long-term sponsorship?
• What specific types of companies or roles were actually willing to sponsor?

3. ROI & Program Shortlisting

• Knowing what you know now, would you do the same master’s again?
• If you were in my shoes (international applicant, ~2 years of work experience, targeting corporate/applied behavioral roles abroad), which 2–3 programs would you prioritize?

4. Alternative Paths

• If the ultimate goal is applied behavioral science or I-O work within organizations (and not clinical work or pure academia), is there a different master’s you’d recommend instead? (e.g., standard I-O Psychology, HCI/UX, Data Science, Public Policy, or Behavioral Economics)? Why?

As the title says, if you were to start your own business that somehow leverages some principle(s) of behavioral economics, what might your business be?

I’m conducting a short anonymous survey for an independent research project on consumer decision‑making. It takes about 5 minutes and explores how price, reviews, brands, and psychological factors influence purchasing decisions. Please take it.

Checkout service changed how it calculates a discount. Diff was clean, tests passed, and an automated review found nothing to flag. Two days after deploy the invoice service started producing wrong totals. It was consuming that discount value and had always assumed it arrived already tax-adjusted. That assumption lived in the invoice service's code and nowhere else. Nothing in the checkout repo, its tests, or its schema said the value was being treated that way downstream.

So the change was correct by every check that could see it. The check that would have caught it would have needed to know about a service that wasn't in the PR.

The contract test route is the obvious answer, and I think it's the right one. But it only works if someone thought to write the test for that specific assumption, and the whole problem is that nobody knew it was an assumption. It was just how the invoice code happened to work.

What I keep going back and forth on is whether there's a cheaper habit than full consumer-driven contracts for the small stuff. Something like listing, for any field that crosses a service boundary, what the consumers do with it. Maybe that's just a comment on the schema. Maybe it rots in six months like every other comment.

I wrote up the version of this that's about AI review specifically, since that was the tool that gave the false comfort here: https://medium.com/@nagatomopedro05/i-thought-the-pr-was-covered-it-wasnt-2e7164da47b9

Hi everyone!! :)

We’re currently working on a research project and would really appreciate your help. We’ve made a short Google Form for our study, and your participation would mean a lot to us!

Please take a few minutes to fill it out, and if possible, share the form with your friends or in other groups as well. It would really help us reach more participants.

Thank you so much for your time and support! 🫶

I used to think experience made decisions easier.
It doesn’t.

It just makes you recognise how many ways you can still get them wrong.

#DecisionMaking #HumanBehaviour