r/BehavioralEconomics

In 1999 Dunning and Kruger ran a study at Cornell. Found that the people who performed worst on a test were the most confident they'd aced it.

The mechanism is simple — to know you're bad at something, you need the same skills required to be good at it.The robber didn't wear a mask because he genuinely believed lemon juice made him invisible.

He wasn't lying. He lacked the ability to recognise his own incompetence. This is the same reason your worst colleagues are always the most confident in meetings.

Most choice architecture operates within existing market structures — nudging people toward better decisions inside systems that assume rational actors. But what if the system itself was redesigned from the ground up to account for how humans actually think?

I've been working on a paper that takes eight well-documented neurological constraints and treats them as design parameters rather than problems to fix:

1. Dual-process cognition — System 1 handles ~95% of decisions. The architecture assumes autopilot as the default state.
2. Dominance hierarchies — Power literally changes the brain within days (Keltner's research). The architecture uses mandatory rotation as neurological hygiene.
3. Tribal bias — The amygdala fires in 30ms on in-group/out-group detection. The architecture uses tribal loyalty for auditing-group cohesion, with cross-group rotation to prevent calcification.
4. Temporal discounting — Limbic beats prefrontal in nearly all time-preference conflicts. The architecture hardcodes long-term constraints at the protocol level where no human decision-maker can override them.
5. Status addiction — Same dopamine circuit as cocaine (Zink et al.). The architecture redirects status-seeking toward verified impact via multidimensional reputation — no single leaderboard to game.
6. Cognitive load limits — 4 plus or minus 1 items (Cowan). Interfaces are designed for bounded attention.
7. Conformity pressure — Dissent registers as physical pain (Eisenberger). The architecture mandates anonymous preliminary filing and devil's advocate roles.
8. Meditation ceiling — Population-level effect sizes of d = 0.2-0.3. The architecture doesn't bet on training people to think better.

The core move is what I'm calling "neurological judo" — redirecting primate drives rather than suppressing them. Loss aversion becomes a corruption deterrent (symmetric stakes in oversight). Status addiction becomes a quality incentive (reputation tied to verified outcomes). Tribal loyalty becomes institutional resilience (auditing groups compete to catch fraud).

The claim: you don't need 100% rational participants. You need 5% vigilance within a well-constrained 95% autopilot, at Dunbar-compatible scale.

This is part of a larger paper proposing a protocol-based economic architecture that separates funding (algorithmic), measurement (supermajority-updated), and execution (competing non-profits) — but the neurological design layer is the part I think this community would find most interesting.

Full paper (formal models, adversarial scenarios, pharmaceutical walkthrough, 16 system limits): https://stuk88.github.io/post-scarcity-architecture/

1,000-word summary: https://stuk88.github.io/post-scarcity-architecture/pitch.html

Curious whether anyone has seen other attempts to design institutional architecture specifically around bounded rationality constraints rather than just nudging within existing institutions. Thaler and Sunstein's work opened the door, but it feels like most applied behavioral economics still treats the market structure as given. What would it look like to not take that as given?

When deposit or withdrawal failures occur on a platform, most operators tend to rely on liability exemption clauses in their terms of service to avoid direct compensation for user assets. This can be interpreted less as a flaw in system design and more as a structural defense mechanism that shifts legal risk onto users—particularly evident in incidents involving stablecoin transfers.

From an operational standpoint, effective risk management begins with proactively identifying unfavorable clauses in the terms and implementing technical safeguards such as distributing assets across personal wallets. Within the analytical framework of Oncastudy, how do you bridge the gap between a platform’s legal liability boundaries and its actual response to security or transaction-related incidents?

https://preview.redd.it/mlowbc0mwawg1.png?width=1080&format=png&auto=webp&s=60c7041262ab238ea1f6a242cd5a695ca30cefa4

Completion means that someone came. It doesn't say whether they understood anything, whether they can use it, or whether anything will really change after that.

That's why a lot of workplace learning looks good on paper but doesn't work in real life.

A lot of digital training would be very different if we focused on changing behavior instead of completion rates.

This is when an interactive learning platform that focuses on decisions instead of just tracking becomes a lot more important.

Automation bias: favoring automated system suggestions over contradictory information from other sources. This shows up constantly in financial behavior. The investor who follows their robo-advisor despite knowing their situation has changed. The trader who overrides their own read because the model says otherwise.

The extreme version played out in August 2012. Knight Capital deployed misconfigured trading software and within 45 minutes had executed millions of unintended orders. $440 million gone. The kill switch existed the entire time. Nobody used it because the system was supposed to be the authority.

What I find underexplored in the literature: automation bias doesn’t just cause people to trust flawed systems. It atrophies the independent judgment that would have caught the error. The more reliable a system is 99% of the time, the more catastrophic that 1% becomes, because you’ve stopped watching.

I think this is even more relevant as AI automation moves into this space.

Has anyone come across research on automation reliance and erosion of judgment over time, specifically in investor behavior? The Parasuraman and Manzey work is the most cited but it’s aviation-focused.

I'm a student who has started to learn economics, and I found my studies in behavioural economics very interesting, and I've created a survey on various BE concepts that I learned about. Please could you answer my form? It will only take 5 minutes, and it will help me collect data for my school project. Economics Survey  – Fill in form

A New York Federal Reserve report published last week found that in states where sports betting is legal, credit delinquencies rose roughly 0.3% overall. But when you isolate the 3% of the population who actually took up betting after legalization, delinquencies spiked over 10%. Online access specifically correlated with a 10% increase in bankruptcy likelihood and an 8% increase in debt collection amounts outcomes that typically appeared two years after legalization.

The data is striking, but the mechanism behind it is what I find more interesting. Most coverage frames this as a willpower problem or an addiction story. I think it’s more accurately an illusion of control problem.

Ellen Langer’s original 1975 research showed that people systematically overestimate their ability to influence random outcomes when skill-adjacent cues are present, choosing your own lottery numbers, rolling dice yourself, being given information before placing a bet. Sports betting is essentially a masterclass in manufacturing those cues. You’re not just picking a number. You’re analyzing stats. You’re watching film. You’re tracking injury reports. The interface is designed to feel like research.

The brain interprets effort as influence, influence as control, and control as reduced risk.

It isn’t. The house edge doesn’t move because you spent three hours studying the spreads. But the felt sense of competence makes the bet feel fundamentally different from a coin flip, even when the underlying probability is structurally similar.

What I find most interesting about the Fed data is the two-year lag before bankruptcy and collections appear. That’s consistent with the illusion of control driving escalating commitment, each loss interpreted not as evidence that the system is random, but as a signal to refine the strategy. The skill frame makes quitting feel like giving up rather than accepting reality.

Curious whether anyone has looked at this through the lens of the hot-hand fallacy in tandem with illusion of control — they seem to compound each other in sports betting specifically in ways I haven’t seen well documented.

I have been thinking recently how much chatgpt has changed our thought process and remember where any ideas for any papers started off with a simple google search, from which you would start expanding your knowledge in that area and because you left your imagination open you learned and retained more info. Well here I am, limiting my use of chat and starting with those simple google searches, and let me tell you, every idea seems so dull, kind of like my brain was fried. Chat created a space where every one of its output seemed "original" or the next best thing and now in hopes of writing something original I have come to conclusion it has to be niche and even so, it is taking a lot to come up with that niche area and bring existing ideas to it to revolutionize that area. Welp this is more of a rant if anything. Good day :)

Hi all - I am considering enrolling in the Graduate Certificate in BI from UTAS starting next year and I am keen to hear from anyone who has done it before. Looking to hear what the papers are like, the time commitment, the Behavioural Lab and general experience.

For context, I would be doing it remotely from New Zealand while working full-time. I did a Bachelor's degree about 10 years ago (Eco, Finance and Accounting) and have done some behavioural papers in the past. Always wanted to do some post-grad study into BE but haven't found a course that aligns with my budget and capacity until now.

Thanks in advance.

Completion means that someone showed up. It doesn't say if they understood anything, if they can use it, or if anything will really change after that.

That's why a lot of learning at work looks good on paper but doesn't work in real life.

If we focused on changing behavior instead of completion rates, a lot of digital training would be very different.

This is when an interactive learning platform that focuses on making decisions instead of just keeping track of things becomes much more important.

"Evolution doesn’t wait for comprehension—it builds fast responses to signals of possible advantage or danger."

Can expound this to many facets of life; personally, it’s helped me predict behavior in those I interact with when there’s an incentive on the line that requires taking risks I’m more comfortable with than the other party.

The beauty premium shows up consistently across hiring, wages, courtroom outcomes, and everyday service interactions. Attractive people get faster service, more benefit of the doubt in interviews, lighter sentences for equivalent crimes, and measurably higher wages in equivalent roles.

What's less often discussed is the mechanism: it's not conscious favoritism in most cases. It's the halo effect — the brain forms a rapid global impression from appearance and then uses that impression as a filter for all subsequent judgments. Intelligence, trustworthiness, competence — all get rated higher when the initial physical impression is positive.

The compounding problem is that the halo effect is partially self-fulfilling. Attractive people receive more opportunities, more mentorship, more positive feedback loops. Over time they often do become more competent — not because the initial judgment was accurate, but because the world behaved as if it was.

This makes the beauty premium almost impossible to audit retrospectively. The evidence has been contaminated by the bias that shaped it.

모바일 환경에서 가로 모드로 전환하면 릴의 병렬 배치가 넓어지며 당첨 라인을 포착하는 유저의 반응 속도가 눈에 띄게 빨라집니다. 이는 시야각 확대에 따라 정보를 한눈에 처리하는 인지 부하가 줄어들며 발생하는 설계상의 물리적 변화로 해석됩니다. 레이아웃 최적화를 통해 심볼 간의 간격을 조절하고 시각적 대비를 강화하면 정보 전달의 왜곡을 최소화할 수 있습니다. 여러분의 플랫폼에서는 화면 방향에 따른 데이터 밀도 차이를 UI/UX 측면에서 어떻게 보정하고 계신가요?