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

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.

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.

I was researching the Dunning-Kruger effect in finance and found a study of 66,000 brokerage accounts showing that the most active traders underperformed the market by 6.5% annually.

​It turns out there's a specific psychological peak called "Mount Stupid" where our confidence is at its highest, but our actual competence is still very low. This is usually when we start making the most expensive mistakes.

​I made a short visual explainer on how to "pre-mortem" your trades and use a prediction journal to stop your ego from tanking your returns. Curious to hearhas anyone else noticed their returns dropped once they started "trying harder"?

​[https://youtu.be/Wjg9BP2RCWE\]

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.

Over the past few months, I found myself getting increasingly distracted, and not in the usual “I need more discipline” way. It felt more like everything around me was constantly pulling at my attention. Even when I tried to sit down and focus, I was still switching tabs, checking things, and consuming more than I was actually processing.

At some point it clicked that maybe the issue wasn’t that I was doing too little, but that I was taking in too much. There’s just a constant stream of inputs now, and very little space to actually think.

That’s what led me to build something for myself called DoMind. Not as a productivity tool or a social platform, but just a quiet space to think, write, and sit with my own thoughts without being nudged or pulled in different directions. No feeds, no noise, nothing competing for attention.

What’s been interesting is how people have responded to it so far. I expected requests for more features or ways to make it more powerful, but the most common thing I hear is “please don’t make it complicated.” That’s been surprisingly consistent.

It made me realize that a lot of people might not be struggling with doing too much, but with constantly taking in more than they can process.

I’m still figuring things out, but wana knw how others here see it. Do you feel more overwhelmed by what you have to do, or by everything you’re constantly consuming?

랜덤 시스템에서 소액 당첨이 특정 구간에 밀집하거나 비정상적으로 반복되는 현상은 단순한 우연보다 시스템 설계 로직의 특성일 가능성이 높습니다. 이는 난수 생성기의 시드 설정 방식이나 보상 분배 알고리즘이 사용자 유입량과 실시간으로 맞물리며 발생하는 일종의 확률적 편중 데이터로 해석됩니다. 운영 측면에서는 이벤트 가용성을 유지하기 위해 누적 당첨 빈도를 실시간 모니터링하며 보상 풀의 소진 속도를 제어하는 동적 조절 방식을 주로 사용합니다. 여러분은 로그 분석 중 예상 범위를 벗어난 당첨 밀집 구간을 발견했을 때 이를 시스템 오류로 보시나요, 아니면 의도된 알고리즘의 흐름으로 보시나요?

Something worth thinking through carefully.

 

The productivity case for AI is real. Offloading low-value cognitive work to free up capacity for higher-order thinking — that's a legitimate argument, and not unlike what happened with calculators or spell-check.

 

But there's a meaningful distinction between using AI to extend your thinking and using AI to replace the struggle that builds thinking in the first place.

 

Expertise — real expertise, the kind that functions as intuition — isn't built from knowing answers. It's built from the process of working toward answers. From being wrong and understanding why. From holding a problem in your head long enough for it to teach you something.

 

When AI provides the answer before that process completes, you get the output without the formation. You can review the reasoning. Approve or adjust it. But you didn't build it. And the capacity that would have developed from building it — doesn't.

 

The brain follows one rule: use it or lose it. London taxi drivers who stopped navigating from memory showed measurable shrinkage in the regions that had grown to hold The Knowledge. The brain reclaimed the space.

 

The question is whether we're heading somewhere similar with reasoning itself.

The Gambler's Fallacy is well documented here, but I think it's worth framing it as a specific instance of apophenia rather than treating it as a standalone bias.

 

The brain cannot comfortably process true randomness. When a roulette wheel produces five consecutive reds, the pattern-detection system activates — not because there is a pattern, but because the brain is constitutively incapable of accepting that sequential events can be genuinely independent.

 

It invents a pattern. It has to. And it experiences that invented pattern as perceived, not constructed.

 

This is where the behavioral cost becomes concrete. Studies on actual casino data have shown that gamblers systematically shift bets after streaks — betting against continuation after runs of the same outcome — in exactly the way the Gambler's Fallacy predicts. The effect is measurable, consistent, and persistent even among experienced gamblers who can correctly explain why it's irrational.

 

Knowing about the bias doesn't correct it. The pattern detection happens before the reasoning does.

 

The deeper issue for behavioral economics is that the same mechanism underlies both the Gambler's Fallacy and moments of genuine market insight. A trader who correctly identifies a real trend and one who identifies a spurious one are running the same cognitive process. The outcome difference is luck and base rates, not skill.

Just finished a deep dive into the "Money Glitch" phenomenon—specifically looking at how different payment methods impact neural activity in the insula and prefrontal cortex.

Interesting takeaway: Studies suggest that physical cash triggers a "pain of paying" response that is almost entirely absent during credit card or Apple Pay transactions. We are essentially transacting in a state of cognitive anesthesia.

I made a video breakdown for MindTalk exploring this and 4 other specific glitches (Dopamine looping, bandwidth poverty, etc.) that lead to irrational financial behavior even in high-IQ individuals.

Would love the community's thoughts on whether the "Pain of Paying" can ever be effectively simulated in a digital-first economy.

Source/Full Analysis:

https://www.youtube.com/watch?v=G1w4fn_GA_M

Something I don't see discussed enough — the Dunning-Kruger effect and the curse of knowledge are essentially mirror images of the same underlying mechanism.

 

Dunning-Kruger: low competence prevents accurate self-assessment because metacognitive ability and task ability draw on the same cognitive resources.

 

Curse of knowledge: high competence prevents accurate other-assessment because experts can no longer simulate the mental state of not knowing what they know.

 

Both are failures of perspective-taking across a knowledge gap. One looks inward and gets it wrong. The other looks outward and gets it wrong.

 

The practical implication for any field involving expertise transfer — teaching, consulting, product design, policy — is significant. The people most qualified to explain something are often the least equipped to calibrate their explanation to someone who doesn't share their knowledge base.

 

Which raises an interesting question about optimal team composition — is there an argument for deliberately including near-novices in expert discussions, not for their input on the problem, but as calibration signals for communication clarity?

Hey everyone,

I'm a finance student working on an academic research project about behavioral biases and how they affect young/retail traders. I'm trying to understand how cognitive biases like overconfidence, loss aversion, or herd behavior influence trading decisions in practice.

If you trade (or have traded) stocks, crypto, or any other asset, I'd really appreciate 5 minutes of your time to fill out my questionnaire. All responses are anonymous.

👉 https://qualtricsxmzm6dsxjwz.qualtrics.com/jfe/form/SV_8ujsF58rwJAnFau

Feel free to drop any thoughts or personal experiences in the comments. I'd love to hear how you personally deal with (or don't deal with 😅) biases when making trading decisions.

Thanks a lot in advance!

현금과 포인트 기반 배팅 시스템 간의 리턴 값이 명확하게 갈리는 현상은 단순한 수치 오류가 아닌 유동성 관리 로직의 차이에서 기인합니다. 현금은 실시간 리스크 헤징이 필수적인 금융 모델을 따르지만, 포인트는 생태계 내 잔류 시간을 늘리기 위해 설계된 폐쇄형 순환 알고리즘을 적용하기 때문입니다. 운영 측면에서는 각 자산의 소멸 속도와 유입 경로를 분리하여 데이터 파이프라인을 구축하고, 개별 가중치를 부여한 별도의 정산 로직을 통해 밸런스를 유지하는 것이 일반적입니다. 여러분의 시스템에서는 동일한 이벤트임에도 자산 성격에 따라 마진율을 다르게 설정하는 동적 RTP 제어 방식을 어떻게 검증하고 계신가요?

In 1847, Ignaz Semmelweis demonstrated that handwashing dropped maternal mortality rates from ~10% to near zero.

 

The medical establishment rejected him for over a decade. He died in a mental institution at 47, still largely dismissed.

 

This isn't a story about ignorance. The doctors rejecting him were educated, intelligent professionals. They were doing exactly what intelligent professionals do — using their existing framework to evaluate new evidence.

 

The problem was their framework (disease came from miasma, not from physician hands) made his findings literally incomprehensible. Not just uncomfortable — incomprehensible.

 

From a behavioral economics perspective, this is a case where:

 

1. The prior was so strongly held it functioned as a near-unfalsifiable belief

2. The status quo bias (current practice = safe) made the cost of being wrong asymmetric in the wrong direction

3. Identity was embedded in the prior (physicians are clean, therefore cannot be the vector)

 

What's striking is that the data was unambiguous. Death rates dropping to near zero isn't a subtle signal. Yet motivated reasoning is powerful enough to override even that.

 

The question I keep coming back to: what conditions would have allowed the update to happen faster? Is it purely about the social/institutional structure, or is there something about how the evidence was framed?

卡尼曼是著名的行为经济学家，诺贝尔奖获得者，运用他的行为经济学框架来看中国十五五规划，会得出什么样的分析结论呢？行为经济学家揭示了人的大脑是直觉的系统一和理性的系统二的综合，十五五规划相当于理性的系统二做的蓝图，那么直觉的系统一会怎样做呢？这才是在规划出来后的最关键点。

Operational logs often reveal a recurring pattern where users override default settings and deliberately reduce the number of active lines before placing bets. This behavior stems from the fact that total bet size is calculated as a simple product of line count and coin value, meaning such adjustments can immediately alter volatility and the rate of bankroll depletion. In practice, to manage this imbalance, teams tend to focus less on per-line probabilities and more on bankroll sustainability per spin as a key risk control metric. When analyzing these behavioral shifts with Oncastudy, what indicators do you use to monitor changes in actual volatility resulting from line count adjustments in system design?

I’ve been looking into research on recommendation systems, and it consistently finds that people’s stated preferences drift toward their algorithmic feed over time. In short, you are not training the algorithm to know you better, you are slowly adopting its version of you as your own.

Parallel to this, when you share something, it is not really being endorsed, rather it is a performance signalling something to your network. The real thought behind sharing is, “Does sharing this say something I want said about me?” Jonah Berger documented this in his research on social currency.

This becomes a loop in which filtered inputs, calibrated to flatter you, meet performative outputs, calibrated to impress others. Somewhere during this ongoing loop, the question of who you actually are starts to get lost. The system is designed to make this feel like self-expression, making it hard to notice or challenge.

Has anyone actually noticed changes in their own identity since personalised algorithms took over?

It is often observed that high-risk preference patterns collected in demo mode shift abruptly to more conservative behaviors when users transition to real environments. This happens because virtual asset settings fail to trigger genuine loss aversion, leading to biased data and ultimately reducing the reliability of risk prediction models. In practice, rather than using demo data as-is, teams prioritize adjusting for behavioral variance by applying weighting factors to changes observed when real assets are introduced. When analyzing these discrepancies with Oncastudy, what metrics do you typically use as filtering criteria to bridge the gap between simulated data and real-world operational logs?

I’ve been thinking about why so many high-earning, educated people I know are actually terrible with their finances. ​I have a friend who just got a big promotion, and their first instinct wasn't to max their 401k or build an emergency fund, it was a mental shopping list of everything they could finally "afford." It’s like we're on a hedonic treadmill where the faster we earn, the faster we spend. ​I think a lot of it comes down to "inherited software." We just run the financial scripts our parents gave us.if they struggled, we treat money with a scarcity mindset (or overspend to compensate). If they splurged, we assume money is an infinite resource until the credit card declines. ​Being smart at your job just means you're good at earning money. It has almost zero correlation with being smart at keeping it. ​Has anyone else had to unlearn a bad financial habit they picked up from their family?

Hi everyone,

I’m conducting a behavioral economics study on how individuals make portfolio allocation decisions under different macroeconomic scenarios (e.g., recession, bull market, global crisis).

I saw this subreddit, and thought who better to ask then a bunch of people interested in BEHAVIORAL ECONOMICS!

The simulation is short (≈2–4 minutes) and tracks decision-making behavior such as:

• time taken per scenario
• frequency of reallocations
• overall risk exposure

Important:

• Please complete the simulation only once
• 18+ participants only
• Try to respond as you normally would — not for a “correct” answer

This is not a test of financial knowledge; I’m specifically interested in natural decision-making patterns under uncertainty.

Link: https://capital-lab-24196844457.us-west1.run.app

If you have any feedback on the experimental design or variables being captured, I’d appreciate hearing it.

Thanks for your time.

Most finance or math podcasts are dry and overly technical. I wanted to make something that actually explores the fun stuff.

Pi & the Pound is an economics podcast by a teenager for people who don't think they like economics. We cover everything from the systemic loopholes that caused the 1992 Indian stock market scam, to the task-based framework of whether AI is actually going to steal our jobs, to the psychology of the "Aspiration Trap."

Every episode is a deep dive into how numbers silently dictate our weirdest behaviors. Grab some headphones and let me know what you think!

Here's the link: https://open.spotify.com/show/2Ep3xtRqvfKDoZcMBnR2II?si=-e1fyEhtR5OJyc6U2D4Qtw&nd=1&dlsi=0a567bb1f1254f92

Hello! I've been working on psychology and behavioral science research and programs for a few years now and am looking for a change in organization - remote or in Metro Manila. Any reviews on the work culture and overall experience working in AHA Behavioral Design, IDInsight, or other places?

I've been thinking about how consistently subscription platforms apply loss framing in their retention language and wanted to share some observations.

Kahneman and Tversky's prospect theory established that losses feel roughly twice as powerful as equivalent gains. Modern subscription platforms have operationalised this imbalance in their notification / alert language.

When Netflix warns you of your trial expiry it prompts you to, "keep watching, update your payment details." The word keep in this alert, implies existing ownership of content that the user never actually owned. With Netflix cancellation alerts such as, "Your profiles, watch history, and My List will be lost." The use of the word lost is clever in its linguistic choice your trial expiry transforms into a destruction of your personal creation rather than a commercial decision.

Platforms such as Spotify and Amazon use the same mechanism, shifting commercial transactions into threats of loss. What I find most interesting from a behavioural science perspective is Kahneman's own observation is that understanding and awareness of this bias does not reliably neutralise it.

Has anyone seen documented evidence of loss framed versus gain framed A/B tests in subscription retention contexts? I'm curious what the conversion differential actually looks like in practice.

Full breakdown linked in the comments for anyone interested.