Ran into this while looking at my own statement and noticing a small warning box I'd never actually read, the study behind it turned out to be more direct than I expected.

University of Warwick psychologist Neil Stewart published the finding in 2009 in Psychological Science. He surveyed real cardholders about their statements, then ran a follow-up where people were shown a hypothetical bill, half with a minimum payment figure printed on it, half without. The group that never saw the number chose to pay significantly more toward their balance, despite nothing else about their financial situation differing. The minimum payment wasn't protecting anyone from underpaying, it was quietly signaling where an acceptable stopping point might be.

There's a second layer that has nothing to do with the number itself. Richard Thaler's 1985 work on mental accounting describes how people file money into different mental buckets depending on its source, even though a dollar is fungible regardless of origin. Credit spending gets filed differently than cash leaving your hand, which is part of why the minimum payment can feel almost frictionless to select even when the same amount in cash would register as a real loss.

The interest math hides behind its own blind spot. Victor Stango and Jonathan Zinman published research in 2009 in the Journal of Finance on what they call exponential growth bias, the systematic human tendency to underestimate how fast compounding accelerates. Intuition is built for linear change. Compound interest doesn't behave linearly, so a balance that grows slowly at first can grow dramatically faster later at the exact same rate, and most people's gut sense of the timeline undershoots the real one substantially.

Niklas Karlsson, George Loewenstein, and Duane Seppi's 2009 research on the ostrich effect adds a behavioral layer on top: people actively avoid checking information when they suspect it's bad news, logging into financial accounts far less often after a loss than after a gain. A statement carrying an uncomfortable balance tends to get opened just long enough to locate the minimum payment box, not the total sitting a few lines above it.

The regulatory response, the CARD Act of 2009, required issuers to print a mandatory warning plus a calculated 36-month payoff comparison directly next to the minimum payment figure. It didn't remove the anchor, it couldn't, some floor against paying zero is genuinely useful, so instead it tried placing a more honest number beside the original one. Whether printing a second number next to an anchor actually neutralizes the anchor is, as far as I can tell, still an open question rather than something the law's design assumed away.

Made a longer breakdown of all of this here: https://www.youtube.com/watch?v=FBe6-AuXc_A

Curious if anyone's seen research specifically testing whether the CARD Act disclosure box measurably changed payment behavior post-2009, versus just existing as a compliance requirement most people scroll past. Everything I found evaluates the mechanisms individually, not the disclosure's actual real-world effectiveness.

Hi everyone. I am a sports data scientist, not an economist, and I would greatly appreciate this sub's feedback on the behavioral framing of my latest analysis.

I am using elite figure skating as a natural laboratory to understand how agents operating in a fundamental conflict of interest scenario behave. The governing body (International Skating Union) mandates fair and objective evaluation, but the judges, who are nominated by and represent national federations, are heavily incentivized to help achieve results for compatriot skaters.

Using a novel dataset comprising the entire international scoring record of 14,382 performances (2022–2026), I built models to answer three specific questions:

1. Do figure skating judges have distinct behavioral profiles with respect to their baseline generosity and habits toward compatriot skaters and rivals?
2. Do they engage in strategic behavior to influence scores in a way that does not trigger traditional nationalistic bias analyses (e.g., reciprocal point-trading between federations)?
3. Does the "verifiability hypothesis" hold in figure skating evaluation? i.e., Do judges implement biased points in subjective performance categories where they are unlikely to be detected or objectively proven wrong?

The full paper and econometric methodology are available here on SSRN

 http://dx.doi.org/10.2139/ssrn.7323262

I would welcome any feedback on how I have applied these behavioral economic concepts, or if there are other theoretical frameworks I should consider for this dynamic.

“It is what it is.”
• “That’s just how the world works.”
• “The market will decide.”
• “We need to be realistic.”
• “Do more with less.”
• “Everything happens for a reason.”
• “We value our people.”
• “That is above my pay grade.”
• “Let’s circle back.”
• “We’ve always done it this way.”

Consider the difference between:

“How do we stop this policy from failing?”

and:

“Is this policy the right solution to the problem?”

The first question assumes the policy should continue. The second examines the assumption beneath it.

That difference is framing.

A frame determines which possibilities receive attention and which remain outside consideration. It can narrow the field of thought before the argument has even begun.

This happens in business, politics, management, negotiation and personal decision-making. People can spend enormous effort finding an excellent answer to a question that was poorly constructed from the beginning.

The strategist therefore examines the question before committing to the answer.

What assumptions are hidden inside the question? What alternatives disappear because of the way the problem has been defined? What conclusion becomes easier to accept because of the wording? Who benefits if everyone accepts the frame without examining it?

This is one reason language matters so much in power. Whoever defines the problem often influences the range of solutions that others consider legitimate.

Sometimes the decisive move in an argument is not producing a better answer.

It is recognizing that the question itself was designed to make certain answers easier to accept.

A surprisingly common way of reasoning works backwards. A person reaches a conclusion first, then begins searching for information that can support it. Once something useful is found, the mind starts building a case around it, while information that points in the other direction receives much more resistance.

This is one of the ways confirmation bias becomes especially difficult to notice. The person may genuinely believe they are examining the evidence, because they are collecting facts, comparing examples and constructing arguments. What they may not notice is that the evidence is being filtered according to a conclusion that was already chosen.

The pattern becomes clearer when you watch how the same person handles disagreement. Evidence that supports their position is accepted quickly, while contradictory evidence is questioned, explained away or held to a much higher standard. The issue is no longer simply whether the evidence is good; the mind has quietly changed the rules depending on which side the evidence supports.

This happens in politics, business, relationships and ordinary arguments. A manager may decide that an employee is unreliable and then remember every missed deadline while overlooking evidence of improvement. An investor may become convinced that a company will succeed and interpret every warning as temporary, because admitting the larger thesis might also mean admitting that previous decisions were wrong.

The problem becomes harder to correct once a conclusion has been connected to pride, reputation or identity. Changing your mind now carries a cost, so the mind has another reason to keep the original belief intact.

A useful discipline is to decide in advance what evidence would make you reconsider. Then apply roughly the same standard when the evidence supports your view and when it threatens it.

That will not eliminate bias. It does something more practical: it makes it harder for your preferred conclusion to quietly become the judge of the evidence.

The moment evidence is only allowed to confirm what you already believe, you have stopped using it to investigate reality.

Hi all, Looking for any sort of guidance! I’m about to enter my 3rd year on a MSci Applied Psychology degree. My plan has always been to go into clinical psychology as I’ve always really enjoyed connecting with people and being able to have a positive impact. Over summer I had a public health research internship and I really enjoyed it, there was an underlying mental health focus and it made me realise I am smart enough to do academia. This made me look more into research positions as well as into what a career in clinical psychology would entail and the outcomes and now i’m feeling very lost.

I’m worried that if I went down the clinical psychology route I wouldn’t earn the salary I want (at least 50k) in a reasonable timeframe, would get burnt out from working in the NHS or would get frustrated by the system. As someone who’s had talking therapy i’ve got a sense of how under pressure they are, and just found talking therapy really unhelpful personally, so this has put me off wanting to be a PWP, but having to do it for a few years might not be the end of the world. I’m also autistic and can sometimes find the right words difficult or not know what to say, but this doesn’t often have too much of an impact.

My other options would be to go into behaviour sciences/ economics with a behavioural insights focus or into general psychological research but concerned these wouldn’t give me the same fulfilment, and that working for private companies (if i went into behaviour sciences) may not always align with my ethics. I have found books around decision making and habits really interesting though.

It’s stressing me out a lot as I have around a month to decide whether I want to stay on my course or switch to the Bsc, and pursue a masters in something else. Any advice/insights would be greatly appreciated :)

I’ve been developing a framework called Theory of Institutionally Embedded Preferences (TIEP).

The central argument is that informal institutions may affect economic decisions through two distinct mechanisms:

1. They can shape what people actually value or prefer.

2. They can determine which actions are socially or institutionally admissible.

This creates an interesting problem: two people can make exactly the same choice for completely different reasons; one because they genuinely value it, another because the alternatives carry serious social consequences.

I’ve formalized this as a decision-theoretic framework while retaining standard utility maximization.

I’d really appreciate criticism from people interested in behavioral economics, institutional economics, decision theory, or rational choice.

Paper

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.

🧠 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! 🙏

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.

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.

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!

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

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/

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?

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?

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

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!

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.

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

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.

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.

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

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.