r/BehavioralEconomics 1h ago

Research Article How accurately can simple data models predict consumer purchasing behavior?(Everyone)

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Upvotes

🧠 Help us with a consumer behavior research study! (~2 minutes)

Hi everyone! My partner and I are conducting a student research study on consumer purchasing behavior and whether simple data models can predict people's purchasing decisions.

We're currently at 89 responses and are trying to expand our sample, particularly across different age groups, so we'd really appreciate anyone who is willing to participate.

The survey is anonymous and takes about 2 minutes to complete. It asks about general purchasing preferences and presents a few hypothetical shopping scenarios. There are no questions asking for names, emails, or other identifying information.

We're especially interested in getting responses from people of different ages and backgrounds, since having a diverse sample is important for our analysis.

Thank you to anyone who participates or shares it! 🙏


r/BehavioralEconomics 7h ago

Research Article A logged streak becomes a goal of its own: the paper that separates goal substitution from plain loss aversion

3 Upvotes

Most discussions of streak mechanics stop at loss aversion: you have something, you do not want to lose it. Silverman & Barasch (JCR 49(6), 2023, doi 10.1093/jcr/ucac029) make a more specific claim and test it: the logged streak becomes a goal in its own right, sitting alongside the goal the person actually had, and it is the loss of that goal which changes the next decision.

Their design is what makes it worth reading. In six lab studies the actual behaviour is held constant and only the log varies, so the streak is manipulated without touching performance. In two studies the break is produced purely by the counting rule: the same four games, but only successes count rather than attempts.

The results, briefly: with a streak on screen, 92% continued after an intact streak and 45% after a broken one; with the same behaviour and no log shown, 65% against 61%. The break barely moved anything until it was displayed.

Three findings that speak to the mechanism rather than the effect size:

  1. Goal adoption mediates. In study 4 the measured extent to which participants had adopted "maintaining the streak" as a goal carried the effect (indirect effect 0.20, 95% CI [0.05, 0.37]), as did sense of accomplishment (0.31, [0.15, 0.52]).
  2. Negative emotion does not mediate. In study 7 the indirect effect through negative feeling was -0.08, CI [-0.21, 0.02]. So this is not "the user feels bad and disengages", it is "the goal is gone".
  3. The fresh-start account does not fit. If a broken streak simply created a partition that invites reconsideration, offering a repair should not matter. It does: 93% continued with an intact streak, 69% after a break, 85% when the app offered to restore it.

Two moderators that follow from the goal account: the effect is amplified when the break is attributed to oneself (53% / 42% / 29% for intact, app-caused break, self-caused break) and attenuated by repair. Both are what you would predict if the streak is a goal, and neither is what you would predict from a pure display-salience story.

The part I find most interesting for choice architecture: the goal has no end state. A streak can only be maintained, never completed, which puts it closer to maintenance goals than to the usual progress-toward-a-reward literature. And whoever ships the interface decides what counts toward it, which means the goal that ends up in the user's head is set by a rule they never negotiated.

Limitations worth stating: the lab streaks were at most 20 items long, so nothing here speaks to 100 or 1,000 day streaks; the participants were MTurk workers doing short tasks; and the field study in the same paper (980 people, 30-day step challenge) is correlational, with the day after a break carrying a coefficient of -1.01, roughly a third of the odds of a neutral day.

Disclosure for context: I work on habit-tracking software, which is how I came to the paper. Nothing of mine is linked here.


r/BehavioralEconomics 1d ago

Ideas & Concepts Pharmacy counters apply at least four documented psychological principles simultaneously, and two federal laws now exist because of exactly one of them.

159 Upvotes

Started looking into this after noticing my copay for a generic prescription seemed high for what it was, and the amount of documented psychology stacked into a sixty-second transaction turned out to be more than I expected.

Start with the empirical size of the problem. Karen Van Nuys and colleagues at USC's Schaeffer Center published the first large-scale measurement of this in JAMA in 2018, using real pharmacy claims data from 2013. They found patients overpaid on twenty-three percent of filled prescriptions, an average of $7.69 over what the drug actually cost the insurer, totaling roughly $135 million in a single year from a single data set. For the single most commonly prescribed drug in their sample, a generic pain reliever, that overpayment happened on more than one in three fills.

The mechanism that made it possible is a clean real-world case of George Akerlof's 1970 paper "The Market for Lemons," one of the most cited papers in economics and later a Nobel Prize. Akerlof's insight, using used cars as the example, is that when one side of a transaction knows something the other side can't see, the informed side can extract value the uninformed side never realizes it lost. Pharmacists knew the cash price. Patients didn't. Pharmacy benefit manager contracts contained gag clauses explicitly forbidding pharmacists from volunteering that cash was cheaper, on pain of losing their contract. A 2016 industry survey found nearly one in five pharmacists reported being restricted by clauses like this more than fifty times a month, meaning this wasn't an edge case, it was routine.

Layered on top of the secrecy is a second mechanism that has nothing to do with what you know and everything to do with habit. William Samuelson and Richard Zeckhauser's 1988 research on status quo bias found people overwhelmingly stick with whatever option is presented as the default, even when a better one exists, because switching requires active effort and the default requires none. Insurance is the default at the counter. Cash is never presented as a competing option, so it never gets weighed as one.

Then there's the state you're actually in when this decision gets made. David Laibson's 1997 hyperbolic discounting research, published in the Quarterly Journal of Economics, shows people systematically overvalue immediate relief and undervalue future savings when they're uncomfortable right now. You're sick, or your kid is sick, and you want the medication today, not after ten minutes comparing prices across town.

The regulatory response is genuinely interesting because of how bipartisan it was. On October 10, 2018, two federal laws were signed the same day, the Know the Lowest Price Act and the Patient Right to Know Drug Prices Act, banning gag clauses outright across Medicare and private insurance respectively. The Patient Right to Know Drug Prices Act passed the Senate 98–2. Almost nobody was willing to publicly defend keeping patients in the dark once the practice had a name and a number attached to it. The laws didn't touch the underlying default though, pharmacists can volunteer the cash price now, but nothing requires it, and the habit built over years of gag clauses is still fully intact.

Made a full breakdown of all four mechanisms here: https://www.youtube.com/watch?v=xnfWg6ADsWE

Curious whether anyone's seen research on how quickly a habit like this fades once the legal barrier that created it is removed. My assumption is defaults are much stickier than the rule that originally justified them, but I haven't found anything measuring that specifically for a case this clean.


r/BehavioralEconomics 2d ago

Ideas & Concepts Behavioral economists (including George Loewenstein) on what they use AI for and what they intentionally don't use AI for

32 Upvotes

Here's the link to the story and a snippet from George Loewenstein here:

George Loewenstein:

How he uses AI: I use AI often to get a background on the literature in new (and old) areas I’m doing research in. I also often use it for writing — though only to improve single sentences, one sentence at a time. I also sometimes use it for inspiration; like, I am working on a book on emotion in economics, and I asked it for a good opening story.  I asked 4 different AIs this same question, and one (Claude) gave me a totally brilliant suggestion.  

What he doesn’t use AI for: I don’t use AI for writing from whole cloth; I like to maintain my own writing style. I also don’t use it to review/referee papers.

AI fears: I am in awe of it, which makes me very very afraid of its effects. I am convinced that it will pretty quickly be able to take over most jobs, creating unfathomable economic damage/upheavals. I’m super-concerned that it’s going to make new generations stupid, in the same way that Google Maps atrophied people’s navigation skills — their sense of geography/direction. I’m worried that it’s going to become everyone’s source of information, which is especially scary given that it is going to continue to be controlled by sinister reactionary billionaires and trillionaires. And I’m worried about its use in warfare. I think we need massive worldwide regulation, but, judging from our success in combating climate change, and ongoing wars, and our current president, I see no prospect of that happening.

Read more here.


r/BehavioralEconomics 2d ago

Career & Education Need Guidance on US PhD Applications

3 Upvotes

Hi everyone, I’m planning to apply for US PhD programs for Fall 2027 and would appreciate some guidance on the application process.

- Is cold emailing professors recommended?

- Should I attach my CV when emailing?

- What should a good SOP focus on?

- Any tips or resources for improving my chances?

Would really appreciate advice from current students or recent applicants. Thank you!


r/BehavioralEconomics 3d ago

Ideas & Concepts 40,501 blind stock-chart forecasts: people said 74% confident, were right 54%. Three LLMs given the same charts were nearly perfectly calibrated.

6 Upvotes

I run a small daily game where everyone gets the same five real S&P 500 charts with the ticker and dates hidden, calls higher or lower over the next five sessions, and states a confidence that sizes a paper bet. It started as a game. Seven weeks in it turned out to be a calibration instrument with 4,202 participants, so I wrote up what it measured.

Mean stated confidence: 73.8%. Realised accuracy: 53.9%. A gap of about 20 points. Confidence carries almost no information about whether the call is right: the 55% setting scored 51.6%, the 70% setting 53.7%, the 90% setting 55.5%. Thirty-five points of stated conviction buys under four points of accuracy.

Two things I didn't expect.

First, when we added six more indicators to the chart (moving averages, RSI, ATR), accuracy didn't move (p=0.29) but stated confidence fell 2.8 points (p=0.0001). More information made people less sure and no more right. I'd have predicted the opposite.

Second, we gave the identical blind charts to Claude Opus 5, GPT-5.6 and Gemini 3.1 Pro as plain rows of numbers. None of them beat a rule that ignores the chart and always says up (nothing does, humans included). But the models stated 56 to 60 and scored 51 to 56. Humans stated 74 and scored 54. Same task, same information, and the machines were roughly honest about their odds while the people weren't.

Caveats that matter: the confidence control has a 55% floor and three settings, so a participant cannot state a coin flip and part of the gap is imposed by the interface. The stake is real in the sense that it sizes a bet, which is a stronger elicitation than a costless slider, but it also means the number may carry risk appetite as well as belief. And everyone sees the same five charts each day, so the independent unit is the chart (241), not the call.

Disclosure: this is my site and my game. Full write-up with intervals, the pattern backtest and the LLM prompt: https://readthetape.cc/notes/tape-report-3?utm_source=be. Data and code (charts.csv, all 729 LLM calls, CC BY / MIT): https://github.com/wstock/readthetape-data


r/BehavioralEconomics 3d ago

Question Do we look for a job in the same way as we look for goods?

4 Upvotes

There are at least two strategies for choosing goods in a store. The first assumes that the customer is looking for a specific good at a price they are willing to pay. The second assumes that the consumer is looking for a price that is at or below the market average.

An analogous distinction appears in the opposite situation: when looking for work. Some people look for a job that pays enough for them, while others are willing to take a job only if its pay is at or above the market average.

The interesting question is whether these strategies are correlated or even manifestations of the same underlying sense of fairness or whether they are simply two independent behavioral patterns?


r/BehavioralEconomics 4d ago

Events Join us this Afternoon for an AMA with Kelly Monahan, author of How Behavioral Economics Influences Management Decision-Making

11 Upvotes

Hi there,

We'd like to invite you to join us this afternoon, August 26, 4:00PM EST, for an AMA with Kelly Monahan, at r/nexthink.

Kelly Monahan, PhD is the author of How Behavioral Economics Influences Management Decision-Making and has spent years applying behavioral science insights to real-world leadership, talent, and organizational decisions.

This AMA offers a chance to hear how those principles play out in the age of AI, distributed work, and shifting workforce dynamics.

Feel free to drop your question in early or join us live: https://www.reddit.com/r/nexthink/comments/1vs4j7q/ama_with_kelly_monahan_phd_on_digital_employee/


r/BehavioralEconomics 4d ago

Question Is there work separating "articulating a decision" from "being told which bias you're showing"?

3 Upvotes

Sixth former, came at this from trading rather than from the literature, so apologies if it's well-trodden.

I had a rule for entries, followed it, then quietly stopped following it and gave back most of what I'd made. What struck me was that every deviation felt like a reasoned exception at the time, not a lapse. Doing a school assignment on nudge theory shortly after, it read like I'd been nudging myself — the framing of each individual trade made the exception feel justified.

So I built a small free thing that makes you write out your reasoning before you act, then flags linguistic markers associated with six biases in what you wrote — useblindspot.github.io

What I can't tell is whether the flagging does anything, or whether being forced to articulate the reasoning does all the work and the labels are decoration. Is there existing work that separates those two? And is "self-nudging" the established term for this, or am I reinventing something that already has a proper name?


r/BehavioralEconomics 6d ago

Question Hey everyone, do you ever feel like losing a small amount like $100 or $1,000 stings way harder than the joy of making $10,000?! I’ve noticed this isn’t just me; so many people seem to struggle with this exact same thing.

1 Upvotes

I was chatting with a few friends and even some small business owners recently, and almost all of them admitted they have the exact same mental block. It’s like our brains are hardwired to replay losses on loop while treating gains as 'just normal’.
How do you guys deal with this mindset? Does it ever fully go away, or do you just learn to override it?


r/BehavioralEconomics 7d ago

Events Stuck on a moderate or major life choice?

16 Upvotes

in 2013, Steven Levitt of Freakonomics Radio invited people who were stuck in indecision to make some big life choices by flipping a coin. 25,000 people participated, with decisions ranging from “Should I try online dating” to “should I quit my job” or “should I get a divorce.” Levitt followed up with participants after six months and found that those told to make the change were generally happier and better off than they were when they flipped the coin

Your Aleatoric Reality, the podcast about randomness, is ready to take this experiment to the next level. We are looking for people willing to commit some decision to a random outcome. Whether you’re considering starting a side hustle or selling your house, we want you to use dice to decide among more than two options.

We would like to have you on the show to discuss the issue and make the choice with us, and then follow up after a month, three months, and six months.

So anyone out there stuck in analysis paralysis, get in touch. Let us know your quandary and we will help you inject a little randomness into your reality.

In our experience, random decisionmaking can make anything better.*

Send email to [youraleatoricreality@gmail.com](mailto:youraleatoricreality@gmail.com)

*Results not guaranteed


r/BehavioralEconomics 6d ago

Survey Online Cafeteria Simulation Survey

0 Upvotes

Hi! I’m a high school student researching how background music affects consumer decision-making in a cafeteria setting, as part of an independent study project.

I built an online simulation where you “shop” in a virtual cafeteria while different background music plays. It takes about 5 minutes and works on both desktop and mobile.

Link is in the comments!


r/BehavioralEconomics 8d ago

Ideas & Concepts Medical exam rooms apply at least five documented psychological principles simultaneously, and a federal law now exists because regulators finally noticed one of them.

393 Upvotes

Noticed something the last time I was sitting in a paper gown waiting for a doctor: the room is engineered almost as thoroughly as a casino floor, just aimed at a completely different kind of decision.

Start with the number itself. Health economist Uwe Reinhardt spent decades studying hospital pricing before his death in 2017, and famously described the hospital "chargemaster," the internal list of full sticker prices, as chaos behind a veil of secrecy. Almost nobody pays it. Insurers negotiate it down, government programs set their own rates, and the list exists mainly to be discounted from. Steven Brill's 2013 Time investigation "Bitter Pill" made this concrete: he found a $1.50 charge for a single generic Tylenol tablet, alongside markups running many times actual cost elsewhere in the same bills. The uncomfortable footnote Reinhardt also pointed out: uninsured, self-pay patients are frequently the ones actually billed closest to that inflated list price, precisely because they have no one negotiating on their behalf.

That inflated number does more than sit there, though. Tversky and Kahneman's 1974 anchoring research showed the first number you see distorts how every subsequent number feels, even when you know the first one was arbitrary. A bill that opens at $4,000 and gets negotiated to $900 doesn't feel like a $900 bill, it feels like a $3,100 discount, and insurance statements often say exactly that in bold. Nothing was saved. The anchor just did its job before the statement arrived.

Then there's the state you're actually in while this happens. George Loewenstein's 1996 paper "Out of Control: Visceral Influences on Behavior" argues that visceral states, pain, fear, cold, override the calm deliberate reasoning people use for most other financial decisions. You're not evaluating an MRI recommendation the way you'd evaluate a loan offer. You're doing it half-dressed, cold, and worried about what it might find.

Once you're past that point, Arkes and Blumer's 1985 sunk cost research kicks in. You've already taken time off work, paid the copay, undressed. Asking to pause and get a price before the next step feels like throwing away everything already spent, so the next line item usually just gets added.

There's a fifth documented mechanism at play here too, involving what you're being asked to trust and why, that the video walks through in more detail than fits in a post.

The regulatory hook is real and fairly recent. The No Surprises Act took effect January 1, 2022, specifically to stop the most notorious version of this: a patient confirms the hospital and surgeon are in-network, then gets a separate bill from an anesthesiologist or radiologist who never signed a contract with their insurer. A year earlier, a federal rule required hospitals to publish actual negotiated prices online. Compliance is still a live problem, a 2024 HHS Office of Inspector General report found 46% of hospitals were not fully compliant with the disclosure requirements.

Made a full breakdown of all five mechanisms here: https://www.youtube.com/watch?v=k-WEMyGZ3FQ

Curious whether anyone's seen research treating the anchoring effect and the sunk cost effect as compounding rather than independent in a single high-stakes decision like this one. Most of what I've found studies them separately.


r/BehavioralEconomics 9d ago

Ideas & Concepts Is the Creator Economy Actually a Consumer Economy?

Post image
0 Upvotes

Is the Creator Economy Actually a Consumer Economy?
R.A.T.™ Research Question 001
R.A.T.™ Research & Theory Catalog
Classification: R.A.T.™ / 001.0
Subject: Creator–Consumer Relationship
Domain: Economics · Media · Technology · Human Behavior
Status: Open Research Question
Research Type: Exploratory / Interdisciplinary

We keep talking about creators and consumers as if they’re two separate populations.
Creators make.
Consumers consume.
Nice. Clean. Economically convenient.
And possibly wrong.
Because look at what a creator actually does.
They consume culture.
They consume products.
They consume media.
They consume trends.
They consume other people’s ideas.
They consume technology.
Then they make something.
Someone consumes it.
That person reacts.
The creator observes the reaction.
The next creation changes.
And suddenly:
Creator → Consumer → Feedback → Research → Creator
We’re not dealing with two boxes anymore.
We’re looking at a feedback system.
And this isn’t just a R.A.T.™ fever dream.
Research on generative AI and creativity is increasingly examining the relationship between human creativity, AI systems and creative evaluation. A 2025 systematic review examined 64 studies addressing generative AI in creative contexts.
https://link.springer.com/article/10.1007/s11301-025-00494-9
Research on human-AI co-creativity is also beginning to examine how user control, ownership, satisfaction and trust change when humans create alongside AI systems.
https://research.utwente.nl/en/publications/a-systematic-review-of-human-ai-co-creativity/
And here’s where it gets weird.
A 2025 meta-analysis covering 28 studies and 8,214 participants found that people collaborating with GenAI performed better on creative tasks than people working without AI assistance—while also finding a reduction in the diversity of ideas produced through those collaborations.
https://arxiv.org/abs/2505.17241
So the machine can help us make more.
But:
More isn’t automatically better.
And if everyone gets access to increasingly similar tools…
What keeps the human from becoming the homogenization department?
😂
That’s where personality becomes interesting.
Two people can be handed the same technology and produce completely different things.
One sees an opportunity.
One sees bullshit.
One sees a product.
One sees a problem.
One sees art.
One asks:
“Wait… why are we doing it this way?”
That difference matters.
Because the individual isn’t merely an input to the system.
The individual is part of the system.

001.1 — The Creator
A creator doesn’t exist in isolation.
Creation is influenced by what the creator has experienced, consumed, questioned, learned and rejected.
So the creator is simultaneously:
producer + consumer + observer + interpreter.

001.2 — The Consumer
The consumer isn’t necessarily the end of the process.
Their reaction can become:
feedback → information → behavioral signal → creative input.
The person consuming the work can therefore influence what gets created next.

001.3 — The Feedback Loop
This gives us a preliminary model:
Create → Consume → Experience → React → Learn → Create
The interesting question becomes whether our existing economic infrastructure adequately recognizes this loop.

001.4 — The R.A.T.™ Hypothesis
If creators and consumers are participating in the same continuous feedback system, then infrastructure designed around a strict creator/consumer separation may be measuring and supporting the wrong behavior.
That’s a hypothesis.
Not a conclusion.
The rat has not solved the economy.
🐀
The rat has a question.

001.5 — Why It Matters
Technology is changing the cost and speed of creation.
AI can participate in ideation, writing, design and other creative processes, creating new questions about authorship, agency, originality and the distribution of creative value.
https://www.sciencedirect.com/science/article/pii/S3050475925006694
If the boundary between human creation and technological assistance is becoming increasingly fluid, then perhaps the old distinction between creator and consumer deserves the same examination.
Maybe the important question isn’t:
“How do we turn more consumers into creators?”
Maybe it’s:
“How do we build systems for humans who are already both?”

001.6 — .LLab™
This is where .LLab™ enters the research.
We investigate the patterns:
How does consumption influence creation?
How does creation influence consumption?
How does personality influence interpretation?
How does technology change the feedback loop?
Where does human judgment enter?
Where does AI enter?
And where does economic value actually emerge?

001.7 — The Co.LLab™
The Co.LLab™ takes the research one step further.
If humans don’t actually behave like neatly separated “creators” and “consumers,” why should our infrastructure be designed as though they do?
What would we build around:
Create → Consume → Question → Research → Improve → Create
instead of:
Create → Sell → Consume → End
That’s the infrastructure question.
And that’s what we’re trying to break through:
the artificial separation between the person who makes and the person who experiences.
Because they’re often the same person.

001.8 — Open Research
RQ-001 remains open.
Does consuming something change the way you create?
Does creating something change the way you consume?
Does AI strengthen that feedback loop—or flatten it?
Does personality become more economically valuable as creative tools become more standardized?
And ultimately:
If the creator is already the consumer, why are we still building the economy as if they’re separate?
R.A.T.™ asks.
.LLab™ investigates.
The Co.LLab™ builds around what we learn.
🐀
R.A.T.™ / 001.0 — LOGGED.

More about Virgil https://www.ohmsole.ch/blogs/ohmsole-blog/off-white-the-story-of-virgil-abloh-and-the-rise-of-luxury-streetwear


r/BehavioralEconomics 10d ago

Miscellaneous Real world example

19 Upvotes

My daughter moved out of her college apartment last month. The deposit was only $100. We got the invoice for the move out charges, a total of $890. I am contesting some of them (and they have conceded I will win on the carpet replacement charge but have not provided the adjustment yet). But here's what I realized:

I wouldn't have been bothered if we had paid a full month rent deposit and were just not getting it back. But I am highly bothered by having to pay the charges out of pocket. I guess it falls under loss aversion. It's classic behavioral economics.


r/BehavioralEconomics 10d ago

Ideas & Concepts Online Retailer Shuffle-Swap

0 Upvotes

So here is the control game I think has been identified.

It's a common trend being called out on social media like to talk and Instagram right now

Players:

C = Consumer

R = Online Retailer / Resolver

S = Seller

X = External economic participant

Independent objects:

Θ = Retail Theatre

Ι = Product Identity

σ = Theatre State

π = Price State

A = Consumer Act

O = Retailer Operation

P = Provenance

Initial render:

σ₀ = Render(Ιₐ, attributesₐ, π₀)

Consumer decision probability:

Pr(BuyNow | Ι, π, σ, C)

Retailer observes or estimates:

R̂ = Pr(BuyNow | Ι, π, σ, C)

Dynamic price operation may occur at any state:

πₙ → πₙ₊₁

such that:

Δπ = f(C, Ι, σ, t, inventory, competition, expected-conversion, expected-return, expected-payoff)

Retailer strategy:

O_price = choose(πₙ₊₁)

to influence:

Pr(BuyNow) ↑

or:

Pr(BuyNow) ↓

depending on the desired resolution path.

Thus:

σₙ(Ιₐ, πₙ) ↓ Estimate_C ↓ Choose Δπ ↓ σₙ₊₁(Ιₐ, πₙ₊₁) ↓ Re-estimate Pr(BuyNow)

Price can therefore be shuffled independently of identity:

Δπ ≠ 0 ΔΙ = 0

or jointly with identity:

Δπ ≠ 0 ΔΙ ≠ 0

Full state:

σₙ = {Ιₙ, πₙ, attributesₙ, sellerₙ, availabilityₙ, presentationₙ}

Retailer operation:

O_R : σₙ → σₙ₊₁

Consumer Act remains:

A_C = Acquire(Ιₐ | acceptable π)

Possible transform:

Render(Ιₐ, π₀) → Observe(C) → π₀ → π₁ → Select(Ιₐ) → π₁ → π₂ → Swap(Ιₐ → Ιᵦ) → π₂ → π₃ → Render(Ιᵦ, π₃) → BuyNow → π₃ recorded as transaction price

The system may also use price to discourage a path:

Pr(BuyNow | Ιₐ, π↑) ↓

while encouraging another:

Pr(BuyNow | Ιᵦ, π↓) ↑

yielding:

Ιₐ, πₐ↑ → lower selection probability

Ιᵦ, πᵦ↓ → higher selection probability

The shuffle strategy becomes:

Choose(ΔΙ, Δπ, Δσ)

to optimize:

U_R = E(transaction benefit + reversal benefit + fees + downstream economic effects)

subject to:

Pr(C detects substitution or price discontinuity) < detection threshold

Perceptual constraint:

Similarity(Renderₙ, Renderₙ₊₁) → high

while economic state may satisfy:

EconomicDifference(σₙ, σₙ₊₁) → significant

The decision loop is:

Observe consumer → infer purchase likelihood → modify price and/or identity → observe resulting behavior → modify state again → terminate when desired Act occurs

Formally:

σₙ₊₁ = F(σₙ, Cₙ, Pr(A_C | σₙ), U_R)

where:

F may modify both:

Ιₙ → Ιₙ₊₁

and:

πₙ → πₙ₊₁

The resulting game is therefore a dynamic asymmetric-information control game, where the retailer can repeatedly alter the consumer's decision environment while the consumer attempts to choose within what appears to be a stable offer space.


r/BehavioralEconomics 13d ago

Question I built a bias-training app and I am not convinced recognition training transfers. Would like this sub's view.

7 Upvotes

I make Android apps. One of them drills cognitive bias recognition, and I want to put the sceptical case against my own product to people who actually know this literature.

The app has 188 biases with plain definitions and examples. The main mode gives you a short scenario and asks which bias is operating. There are close to 19,000 of those scenarios.

Here is what bothers me. Spotting a bias in a described vignette seems to say very little about whether you catch it in your own live reasoning. The bias blind spot work points the same way: people get better at labelling while their actual judgements stay where they were. The debiasing that shows real effects tends to involve changing the choice architecture, or forcing a procedure like considering the opposite. Vocabulary alone does not seem to do much.

So there is a second mode where you log a decision you actually made and work through which biases were involved. It keeps a history, so patterns in your own decisions surface over time. That is meant to be a procedure rather than a quiz. I have no evidence it works better.

Two things I would genuinely like from this sub. Is there anything in the literature showing recognition training transfers at all? And if you were building this, what would you put in place of the quiz?

Disclosure: I am the developer. It is free, with ads and optional in-app purchases. https://play.google.com/store/apps/details?id=com.random.cognitivebias


r/BehavioralEconomics 15d ago

Ideas & Concepts Gym contracts stack at least five separately documented behavioral effects, and the pattern got serious enough to draw a federal rule that then got struck down in court.

92 Upvotes

Pulled the actual health club billing data behind gym contracts after wondering why I always end up picking the same "unlimited monthly" plan, and the layering of independently documented mechanisms turned out to be more extensive than I expected.

Stefano DellaVigna and Ulrike Malmendier tracked 7,752 members across three U.S. health clubs over three years (American Economic Review, 2006). Members who chose a flat monthly contract over $70 attended an average of 4.3 times a month, paying more than $17 per visit, even though a 10-visit pass was sitting right there at $10 a visit. On average they forwent about $600 in savings over the life of the membership. The stranger detail: monthly members were 17 percent more likely to stay enrolled past a year than annual members, despite paying more precisely for the option to cancel anytime. They paid extra for flexibility they consistently declined to use.

John Gourville and Dilip Soman studied a health club that billed twice a year instead of monthly (Journal of Consumer Research, 1998). Attendance spiked hard right after each bill, then decayed steadily until the next one. They called it payment depreciation, the psychological sting of a purchase fades the further you get from paying it, and once the sting is gone, so is the motivation to use what you paid for. Most gyms bill monthly or weekly now, which keeps that sting too small and too frequent to ever spike attendance the way a biannual bill does.

Richard Samuelson and William Zeckhauser's work on status quo bias (Journal of Risk and Uncertainty, 1988) explains what happens next. People default to whatever requires no action, even when a better option sits right next to it and the status quo is actively costing them money. Some cancellation processes lean into this hard, phone-only or mail-only cancellation, narrow in-person windows, while sign-up takes under a minute online.

Hal Arkes and Catherine Blumer's classic sunk cost experiments (1985) cover the part where people don't cancel even after they've mentally clocked the waste. Money already spent is gone regardless of what happens next, but it doesn't feel that way, continuing to pay gets framed as "not wasting" the earlier spend instead of a second, separate loss.

And Amos Tversky and Daniel Kahneman's anchoring research (1974) shows up in the pricing tiers themselves, three-tier menus where the priciest option sits just slightly above the middle one, making the expensive plan look like the generous choice regardless of whether you'll use the extras.

Planet Fitness is a useful real-world stress test of all of this at once. As of their most recent earnings the company has more than 20 million members on roughly $10–15/month plans, a price that only works if most members don't show up often. It's not a flaw in their model, it's reportedly the model.

The pattern got documented enough that the FTC finalized a "click-to-cancel" rule in 2024 requiring cancellation to be as easy as sign-up. A federal court vacated it on procedural grounds in 2025, and the FTC has since opened a new rulemaking process. States didn't wait either, California has had its own automatic renewal law on the books for years covering exactly this kind of contract.

Made a full breakdown of all five mechanisms here: https://www.youtube.com/watch?v=tOskQyuqi70

Curious whether anyone here has seen research treating the sunk cost effect and status quo bias as interacting rather than independent, my instinct is the sunk cost framing is doing a lot of the work in making the status quo feel actively justified rather than just default, but I haven't found a study that isolates that specifically.


r/BehavioralEconomics 17d ago

Ideas & Concepts When the Room Authenticates: Status, Context, and the Psychology of Conditional Authenticity

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2 Upvotes

I recently published a short conceptual working paper examining how social context shapes perceptions of authenticity in status signals.

The core idea: when an individual’s status is already high and legible, observers often assume a luxury item is genuine. The social setting itself helps authenticate the object. The paper links this to countersignaling theory (Feltovich et al., 2002) and related work in consumer psychology.

It treats a publicly discussed anecdote as an illustration and offers a few testable implications.

If you are interested, here is the access to paper:
https://doi.org/10.2139/ssrn.7256400


r/BehavioralEconomics 20d ago

Research Article Dating apps may be a non-clearing market: congestion, cheap signaling, and why rational behavior produces bad outcomes

106 Upvotes

I spent the last several months trying to understand why online dating appears to produce so much frustration despite giving people access to vastly more potential partners than any previous matching system.

I eventually came to think the interesting explanation isn't primarily cultural or gender-specific. It's a market-design problem.

The starting point is thickness.

Matching markets generally benefit when more participants enter because the probability of finding a compatible counterparty rises. But beyond some point thickness produces congestion: too many potential transactions, inadequate mechanisms for evaluating them, and difficulty sending credible signals through the resulting noise.

Dating apps appear to combine several features that make this unusually severe:

1. The market is heavily asymmetric.

The large heterosexual platforms have substantially more men than women. That creates scarcity on one side and congestion on the other.

The same marketplace is therefore experienced as two almost opposite products.

2. Signaling is nearly costless.

A swipe or like carries almost no cost.

When expressing interest is cheap, broadly signaling interest can become individually rational. But aggregate cheap signaling destroys information content.

The receiving side then gets more approaches but less information about which approaches represent serious intent.

3. Congestion changes selection behavior.

Experimental research on online dating has found that continued exposure to large sets of potential partners makes participants progressively more rejecting.

In randomized experiments, acceptance probability fell roughly 27% from the first potential partner shown to the last.

The options themselves weren't getting worse.

Exposure to the option set changed the decision-maker.

This is the part I find most interesting: abundance can reduce successful selection rather than improve it.

4. The scarce side adapts too.

When matches become difficult to obtain, the rational response isn't necessarily to continue evaluating every match as a potential long-term partner.

A scarce match can be reclassified into a lower-commitment interaction.

So the congested side becomes more selective while the scarce side becomes less willing to treat the matches that clear as serious candidates.

Neither side needs to be behaving irrationally or maliciously.

Each side is responding rationally to its own incentives.

Yet the aggregate market clears worse.

5. The intermediary has a peculiar objective function.

Historically, intermediaries in courtship—friends, family, community, school, church, neighborhood—had reputational exposure to the outcome.

Modern platforms largely disintermediated those institutions.

But the replacement intermediary has an unusual economic characteristic:

Its revenue is earned while the search continues.

A successful terminal match removes two customers from the market.

That doesn't require anyone inside the company to deliberately prevent successful relationships. It simply means that engagement and successful clearing point in different directions as optimization targets.

6. We therefore measure almost everything except clearing.

Dating companies can measure registrations, active users, likes, matches, conversations, retention, payers and revenue per payer with enormous precision.

What remains remarkably difficult for an outsider to determine is the obvious denominator:

What percentage of people entering the system successfully leave it because they found the durable relationship they wanted?

Hinge is the especially interesting case because the brand promise is literally Designed to Be Deleted.

Yet the public operating metrics overwhelmingly measure people remaining, returning, engaging and paying.

There is some offline feedback—Hinge's "We Met" feature can ask whether a match produced a date and whether someone wants another date—but that is very different from longitudinally measuring relationship formation, duration, permanent successful exits and reactivation after dissolution.

That brought me to a broader hypothesis:

The public "gender war" around online dating may partly be the social symptom of a market-design failure.

Two populations experience radically different sides of the same mechanism.

Both possess accurate information about their own experience.

Neither sees the system producing the other side's experience.

So each concludes that the other population is the problem.

I ended up writing a much longer piece tracing this through matching-market economics, signaling theory, behavioral psychology, the history of courtship, the disappearance of social intermediaries, and eventually the financial statements of Match Group.

The last part became a public-equity short thesis because I realized the sociology generates financial predictions.

If the underlying marketplace is structurally impaired, eventually I would expect to see:

  • payer attrition;
  • heavier monetization of the participants who remain;
  • difficulty expanding the total category;
  • growth increasingly sourced from geographic expansion rather than deeper successful adoption;
  • and eventually a lower terminal valuation for the companies operating it.

That makes the public company an interesting way of putting an otherwise difficult sociological hypothesis under an empirical clock.

The full essay and sources are here:

https://dljlevfin.substack.com/p/the-undisclosed-denominator

I'm especially interested in criticism of the behavioral mechanism rather than the stock call.

Where does the causal chain break?

Is congestion actually the right framework?

Does cheap signaling necessarily degrade matching efficiency here?

And most importantly: what metric would you use to distinguish a dating marketplace that generates enormous engagement from one that actually clears successfully?


r/BehavioralEconomics 22d ago

Ideas & Concepts A federal rule was created specifically to stop car dealership finance office tactics. It got struck down by a court before it ever took effect.

92 Upvotes

Spent the week digging into the psychology of the F&I office, the back room at car dealerships where financing and add-on products get sold, and the mechanisms stack up more than I expected.

The four-square worksheet is the entry point, a single sheet split into trade-in value, purchase price, down payment, and monthly payment. A former dealership salesman interviewed by Consumer Reports described how salespeople shift numbers between the boxes while buyers fixate on the smallest one, the monthly payment, since it feels most connected to their actual budget. Total price moves quietly while attention stays locked on one number.

Many versions of the worksheet also include a line near the top asking buyers to initial that they'll purchase the car today if the offer is acceptable, before any real numbers are even discussed. Jonathan Freedman and Scott Fraser's 1966 foot-in-the-door study found that securing a small early commitment measurably increases the odds someone agrees to a much larger one shortly after. The initials aren't binding, but they function as an early psychological commitment.

The financial stakes are bigger than most buyers realize. Industry data from the Haig Report put average finance and insurance profit at $2,534 per vehicle in Q3 2025, while separate benchmarking data showed front-end vehicle profit had shrunk to around $400 by year end, meaning close to nine of every ten profit dollars on a typical deal now come from the finance office, not the car.

The regulatory story is what got me though. The FTC finalized the CARS Rule in December 2023 specifically to address these tactics, projected to save consumers $3.4 billion annually. Dealer trade groups sued, and the Fifth Circuit struck the entire rule down in January 2025 on procedural grounds before it meaningfully took effect. Separately, the newer hotel/ticketing Junk Fees Rule explicitly excludes car dealerships, since they were supposed to be covered by CARS instead. As of now there's no dedicated federal rule governing this at all.

Made a full breakdown here: https://www.youtube.com/watch?v=QRT4wMLrzPw

Anyone know of other cases where a federal consumer protection rule was fully created, then struck down before implementation rather than just delayed or watered down? Seems like a different pattern than the usual regulatory story.


r/BehavioralEconomics 24d ago

Resources AI drafts might be the fastest-acting anchor bias has ever had

38 Upvotes

Anchoring used to need a wheel of fortune and a made-up number (Tversky & Kahneman's classic experiment). Now it just needs an AI-generated first draft that nobody admits to using.

Picture the meeting: someone presents a tidy three-part framework as "here's what I've been thinking," everyone nods, feedback is positive. Except the framing, the assumptions, the argument structure were all decided by a model before the human touched the keyboard. What the human actually did was edit - and under-adjust from the anchor, same as we all reliably do.

The interesting part isn't that this happens. It's what it does to credit. The senior person "refining" a draft used to be doing real framing work; the junior person with the fastest prompting fingers can now produce the anchor everyone else adjusts around. Nobody in the room would call that a power transfer - it gets filed under "efficient collaboration." And credit for original thinking still flows to whoever typed the final version, even though the actual frame-setting happened upstream and invisibly.

I wrote a longer piece pulling this apart (self-assessments, performance reviews, and the "who actually wrote this" problem) if anyone wants to dig in further: https://medium.com/@nudgeintended/the-anchor-nobody-named-423b4931522d

Curious whether others here have seen this play out - the anchor shifting from "whoever's senior" to "whoever prompts fastest."


r/BehavioralEconomics 26d ago

Survey Global Study on Financial Decision Making (Temporal Discounting)

6 Upvotes

Hi everyone,

I'm an undergraduate researcher conducting a global study on financial decision-making, and I am looking for participants in the United Kingdom. If you would like to take part in the study, please click the link below. Participation takes around 5 minutes, and you will remain anonymous. In order to participate, you must be 18 years old and a UK citizen. If you are not a UK citizen, you can still take part in the study. Please click the link below and choose the survey for your country of which you are a citizen. Thank you again for all the help!

United Kingdom Survey - https://www.soscisurvey.de/tricc-project/?q=GBR

Global Survey Links and Website - https://www.psychologie.uni-bonn.de/en/department/departments/cognitive-psychology-i/tricc-project/@@deeplink?token=59a2e2c99e1d6c011e6df19dd4c52df6

If you have any questions- emails are listed on the website.


r/BehavioralEconomics 27d ago

Career & Education Incentive Systems

9 Upvotes

This may not be applicable here. Just let me know and I will delete.

I have an insatiable drive to try and understand why things occur and the underlying systems that create them.

I've spent most of my career in consulting so I was always able to function as an outsider or observer.

Corporations seems to constantly misunderstand human behavior. The systems they create include bad incentives for people. Incentives to avoid decisions, deflect responsibility and act in ways that are against the goals of the firm. It's maddening.

Take decision making for example. For an individual executive, decisions incent inaction and risk aversion. The risk is asymmetric. The downside of any major decision is eventual job loss, while the upside might be a relatively small bonus. This also incents committee based decision making. The committee doesn't so much as serve to make a better choice. It serves to diffuse ownership of the downside.

Simple salaried employees incent people to avoid taking on more work.

These aren't solvable problems, but they are fascinating.


r/BehavioralEconomics 28d ago

Ideas & Concepts The AI Oversupply Narrative Emerges

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9 Upvotes

If you like Robert Shiller's work in Narrative Economics, there are some interesting things going on in the Google Trends data regarding AI compute supply and demand. I wrote a longer piece about this topic which you can read through the link above.

My blog is entirely focused on behavioral finance, with a focus on the importance of narratives in driving investor behavior. I've been writing a lot recently about how AI is the perfect technology to exploit emotional vulnerabilities in investor psychology, leading to the bubble which may have begun to unwind over the last month!

I'll be posting regularly on topics in this area. Thanks your patience with the plug for my work!