I think my current favorite is “Thinking Fast and Slow” by Daniel Kahneman.

The asymmetry in Edmans, García and Norli (2007, JF) baffles me. Across about 1100 international matches in 39 countries, a World Cup elimination loss is followed by a roughly 0.49% abnormal decline in the losing country's own index the next trading day, net of world market moves. Wins, however, produce no comparable effect. Taken at face value that is loss aversion, or negative affect driven pessimism, priced by the most incentivised participants we have, who have every reason not to.

For one, I am unsure of the robustness. It is an old, famous result, which these days is closer to a yellow flag than a green one, and the headline number rests on only about 56 World Cup elimination games, which sharpens the fragility worry rather than softening it. A 2026 working paper (Gatto, "The reach of the World Cup distraction effect"), as I have seen it summarised, argues the broader World Cup market effect barely registers in the deep, liquid venues that carry most of the world's money, that a couple of ordinary measurement choices can conjure it out of noise, and that the durable bite concentrates among retail investors trading on the result. Worth flagging that Gatto works the distraction and inattention channel rather than re-testing the loss result head on, so it is adjacent evidence, not a direct replication, and I am going off the write-up, not the paper itself. Either way it reframes the question from "markets are irrational" to "a thin slice of participants is, sometimes." Does the original survive modern specification-curve and multiple-testing scrutiny, or is this a well-dressed green jelly bean?

Second, a confound that cannot be ignored. The original sample runs only to the early 2000s, so this next case sits out of sample, but it is the one Edmans himself later used to stress-test the finding against the 2014 tournament. Brazil's 7-1 semi-final loss should, on the mood story, have been about the cleanest negative affect shock going. The Bovespa rose about 1.8%. Edmans' own reading is political, that the defeat was taken as raising the odds the incumbent president lost October's election to a more market friendly rival, and at least one other account puts the move down to macro tailwinds instead. National mood and the market moved in opposite directions, and the fact that two credible explanations compete for the same print is the point: sentiment is not one variable, and any single-event reading is underidentified.

Full piece linked in the comments if useful, but mostly I want the pushback: affect pricing that is real if small, or artifact?

Hey everyone,

I’ve been trying to model a common real-world relationship dilemma using behavioral economics frameworks, and I’d love to get this community's take on how to map the utility functions and strategic equilibria here.

The Scenario:

A girl operates under a tight liquid budget constraint of £100 cash (she earns a wage of £10/hour).

• Action A (High WTP): She willingly drops £30 cash (30% of her net worth) plus 1 hour of leisure time to play padel with two friends.
• Action B (High WTA / Avoidance): For 9 months, her boyfriend has consistently requested a small, low-cost signal of effort: for her to bake him a simple batch of cookies. The objective market cost is negligible (£5 for ingredients + 30 mins of labour, representing a £5 opportunity cost).

Despite his constant requests—and despite the fact that the boyfriend aggressively over-supplies effort by fulfilling every single micro-request she has—she completely refuses to do it.

When the boyfriend challenges her on the math (spending 8 hours of labour equivalent on an £80 pair of jeans vs. 30 minutes on him), she uses a few classic behavioral defence mechanisms:

1. Projection Bias: She claims that verbal praise and physical affection are "enough" for the relationship, projecting her own utility weights onto his.
2. The Phantom Promise: If he offers to shift to market norms by saying, "I will literally bank transfer you £20 to do it," she suffers from social shame and issues a time-inconsistent promise: "No, don't pay me, I'll do it myself." Then, she defaults right back to the baseline and never executes.

My Behavioral Breakdown:

• 1. Hyperbolic Discounting & Present Bias: The immediate transaction utility of padel (instant dopamine, social status, peer bonding) heavily outweighs the delayed, abstract utility of relationship investment, which she already views as a "fully funded account" due to the boyfriend's over-supply.
• 2. The Endowment Effect & Asymmetric Loss Aversion: Because her budget is low (£100), she is highly loss-averse regarding her cash and immediate free time. Giving up £5 and 30 minutes feels like a painful, visceral loss of her current endowment. The boyfriend is looking at the gain (relationship harmony), while she is looking strictly at the loss of autonomy.
• 3. Market vs. Social Norms (The Ariely Effect): Introducing a cash incentive (£20) crowds out the social norm, triggering an ego-preservation mechanism. She uses a "Phantom Promise" to buy immediate relief from the argument, heavily discounting the future cognitive cost of actually having to bake.
• 4. A Monopoly Equilibrium: From a cold, profit-maximizing perspective, why would a rational consumer change this setup? Her input cost is £0, and her output is a doting boyfriend who gives infinite effort. Her ROI is mathematically infinite. By making his effort a zero-priced good, the boyfriend has accidentally lowered its subjective value to her to zero.

Questions for the Sub:

1. How would you formally write out her utility function to include this massive "psychic cost/dread tax" ($D_b$) for domestic relationship labour vs. her self-image mental account (£80 jeans)?
2. If the boyfriend wants to break this Nash Equilibrium, what is the most efficient "Nudge" or structural choice architecture change he can implement? Should he introduce strategic scarcity of his own effort to reset her baseline reference point?

Been noticing something strange while reading about chargebacks, payment processing, issuer workflows, and alternative payment setups lately.

Modern payment systems can detect fraud patterns in milliseconds, score user behavior using AI models, route transactions globally, and monitor risk across millions of transactions.

But when a customer sees:
“PAYXYZ INC - $187”

the system still somehow struggles to answer the most human question:
“What exactly was this payment for?”

And that gap seems to create an entire industry around disputes, friendly fraud, token systems, alternative processors, reserves, representments, and compliance layers.

What’s interesting is that the money itself is usually not the real issue.

Customers willingly let Netflix, Amazon, Uber, Apple, and food apps pull money automatically every month without thinking much.

So maybe trust in payments is less about banking infrastructure and more about:

• recognition
• context
• interface clarity
• behavioral conditioning
• and emotional confidence at the moment of seeing the charge

Which makes me wonder:

Are chargebacks fundamentally a fraud problem?

Or are many of them actually a “human understanding” problem happening inside highly fragmented financial systems?

https://forms.gle/vqE1PBW7wNAQkq189

Hi everyone! I’ve been interested in behavioral economics since taking a Psychology of Finance course in college.

I studied Finance and Financial Planning, started my career at Merrill, spent several years in investment-bank compliance, and now work at an independent RIA as an Associate. I hold the SIE and Series 66, have completed my CFP coursework, and am currently studying to sit for the CFP exam in November.

Over time, I’ve kept finding myself drawn back to behavioral finance. The question I keep coming back to is: How do you actually build a successful career in this space?

I know behavioral economics can overlap with finance, fintech, consumer research, product, marketing, consulting, public policy, and benefits, but I’m unclear on the most realistic entry points for someone with my background.

For those working in or adjacent to behavioral economics or something similar:
- What job titles or career paths should I research?
- How did you get started?
- Are there any specific skills, programs, or companies worth exploring? Or type of experience that matters most?
- Is there a place for someone coming from wealth management/financial planning, or are there adjacent roles that would make more sense first?

I would genuinely greatly appreciate any advice, resources, or honest perspectives from anyone who has found their way into this field or works near it. Thank you so much!

I've been thinking about an interesting design problem.

Imagine you're building a system that helps people make decisions under uncertainty. Sometimes it has plenty of useful information. Sometimes it has almost none.

Now imagine the only information available is something like: time of day, day of the week, a person's own historical behavior, previous outcomes from similar situations. None of these variables should be strong predictors by themselves. Any signal they contain is likely to be weak, noisy, and unstable.

So what should the system do?

One philosophy is: "If the evidence isn't strong enough, don't recommend anything." Another is: "Present weak signals transparently, explain their uncertainty, and let people decide how much weight to give them."

Personally, I find the second approach fascinating. Humans already rely on weak signals all the time: intuition, routines, superstitions, "today feels like a good day", recent experiences, emotional state. Those signals may not be objectively reliable, but they clearly influence decisions.

So why shouldn't a decision-support system expose weak statistical signals ... as long as it makes their limitations explicit?

I've been prototyping an experimental decision-support system around this question. It doesn't try to predict future outcomes or outperform probability. Instead, it records repeated decisions, tracks outcomes over time, and explores whether weak behavioral signals become more informative as data accumulates.

I'm genuinely interested in where people here would draw the line. At what point does a weak signal become useful enough to present? Or should decision-support systems remain completely silent until they have statistically compelling evidence?

If anyone here works on behavioral decision-making, choice architecture, or uncertainty, I'd genuinely appreciate your perspective. And if you'd like to participate in the experiment itself, I'd be happy to share how it works.

For a long time, economics assumed that human beings are perfectly rational. The idea was that people always make logical decisions to maximize their personal utility. This concept was known as Homo Economicus (the Rational Man).

But real life tells a different story.

Humans are not rational all the time. Our decisions are influenced by emotions, relationships, social pressure, propaganda, cognitive biases, and incomplete information. We are not machines calculating utility—we are emotional beings with instincts and psychological limitations.

A simple example illustrates this.

Suppose I don't have much money beyond my necessary expenses, but my wife or son asks me for a birthday gift.

Pure logic suggests I should wait until my next salary, reduce unnecessary spending, save money, and then buy the gift.

In reality, many people still buy the gift immediately because the decision is driven by love and emotion rather than financial optimization.

Traditional economic models struggle to explain such behavior. This is where behavioral economics emerged—the meeting point of psychology and economics. Instead of assuming people are perfectly rational, it studies how people actually think and make decisions.

Famous Examples That Challenged Traditional Economics

1. The Ultimatum Game

Player A receives $100 and decides how to divide it with Player B.

Player B can either accept the offer (both receive the proposed amounts) or reject it (both receive nothing).

Traditional economics predicted that Player A would offer the smallest possible amount, perhaps $1, and Player B would accept because $1 is better than nothing.

In reality, people frequently reject unfair offers below $20–30, even though doing so leaves them with nothing. They are willing to sacrifice money simply to punish unfair behavior.

This demonstrated that fairness and emotions matter alongside self-interest.

2. The Allais Paradox

Expected Utility Theory claimed that people evaluate risky choices consistently.

Maurice Allais demonstrated that they do not.

Most people prefer:

• $1 million for certain
• over an 89% chance of $1 million, a 10% chance of $5 million, and a 1% chance of nothing.

But when the same probabilities are adjusted, many of those same people suddenly prefer the riskier option with the larger reward.

This inconsistency revealed the Certainty Effect—people value guaranteed outcomes much more than mathematics predicts.

3. Stock Market Bubbles

The Efficient Market Hypothesis argued that stock prices always reflect all available information because investors are rational.

Reality proved otherwise.

Events such as the Dot-Com Bubble (2000) and the Global Financial Crisis (2008) showed that markets can become wildly disconnected from their true value.

Behavioral economists such as Robert Shiller demonstrated that markets are heavily influenced by herd behavior, overconfidence, speculation, fear, and irrational exuberance.

4. Loss Aversion

Daniel Kahneman and Amos Tversky discovered that losing something hurts much more than gaining the same thing feels good.

Losing $100 typically causes about twice as much psychological pain as the happiness gained from winning $100.

This explains why investors often refuse to sell losing stocks, hoping they'll recover, even when logic suggests selling.

The Bonus Experiment

Three groups were asked to perform the same task.

The first group received no bonus.

The second group was promised a bonus after completing the work.

The third group received the bonus before starting but was told it would be taken back if they performed poorly.

Traditional economics suggested that bonuses should not greatly affect performance because rational workers simply respond to total pay.

Instead, the third group performed the best. The fear of losing something they already possessed motivated them far more than the promise of gaining it.

This is another demonstration of loss aversion.

The Dutch Tulip Mania (1636–1637)

During the Dutch Golden Age, tulip bulbs became objects of intense speculation.

At the peak of the bubble, a single rare tulip bulb could sell for more than ten times the annual income of a skilled craftsman.

People weren't buying tulips because they needed flowers. They bought them because they believed someone else would pay an even higher price later.

This illustrates several behavioral concepts:

• The Greater Fool Theory
• Herd mentality
• Fear of Missing Out (FOMO)

Speculation became so extreme that people traded contracts for tulips that hadn't even been harvested yet. When buyers suddenly disappeared, prices collapsed almost overnight.

The Formula Behind the 2008 Financial Crisis

One of the mathematical models heavily relied upon before the 2008 crisis was David X. Li's Gaussian Copula Function.

It attempted to estimate the probability that different borrowers would default on their mortgages at the same time.

The model assumed that relationships between defaults remained relatively stable based on historical data.

The mathematics itself was elegant.

The assumption was not.

When panic spread through the housing market, human behavior changed dramatically. Mortgage defaults became highly correlated because fear spread through the entire financial system.

The model ignored psychology and herd behavior, leading institutions to underestimate risk on a massive scale.

The Major Models of Behavioral Economics

Prospect Theory (Daniel Kahneman & Amos Tversky)

People evaluate gains and losses relative to their current situation rather than their total wealth.

Losses hurt much more than equivalent gains feel good, and people consistently misjudge probabilities.

This replaced Expected Utility Theory for many real-world applications.

Dual Process Theory

Human thinking operates through two systems.

System 1 is fast, emotional, intuitive, and automatic.

System 2 is slow, analytical, logical, and effortful.

Most everyday decisions are made using System 1, which explains why people rely on shortcuts and cognitive biases instead of careful reasoning.

Mental Accounting (Richard Thaler)

Economics traditionally treated all money as identical.

Behavioral economics showed that people mentally separate money into different "accounts."

For example, someone may keep ₹50,000 in a savings account earning very little interest while simultaneously paying high interest on credit card debt because psychologically they see them as different pools of money.

Hyperbolic Discounting

People consistently prefer immediate rewards over larger future rewards.

This present bias explains procrastination, poor saving habits, unhealthy lifestyles, and many failures of long-term planning.

Bounded Rationality (Herbert Simon)

Humans do not always maximize utility because they lack unlimited time, information, and mental capacity.

Instead of searching endlessly for the perfect option, most people settle for an option that is simply "good enough."

Simon called this behavior satisficing.

Conclusion

Behavioral economics did not reject mathematics or economics.

Instead, it made economics more realistic by incorporating psychology into economic models.

Rather than assuming perfectly rational individuals, behavioral economics recognizes that humans are emotional, biased, socially influenced, and cognitively limited.

By understanding these predictable irrationalities, economists developed models such as Prospect Theory, Mental Accounting, Hyperbolic Discounting, and Bounded Rationality that explain and predict real human behavior far better than traditional theories ever could.

Our brain does not always think in a fully rational way. It works through two systems. System 1 is fast, automatic, and cheap. System 2 is slower, more careful, and rational, but it needs more energy. Because of that, the brain usually prefers System 1 and takes the easiest path if it can. It is not exactly laziness, more like energy saving.

That is why a discount on Flipkart or Amazon works so well. A product has one price, then the old price is crossed out with a red line, and the final price looks lower. Even if we do not really need the product, the brain feels like it is a gain and wants to buy it. On the other hand, we can fight over ₹10 with a local vendor because the same amount feels very different in a different situation. The brain is not comparing properly all the time. It is reacting to the frame.

This also connects to loss aversion. Losing something hurts more than gaining the same thing feels good. So losing ₹500 hurts more than getting ₹500 feels good. That is also one reason people stay in toxic or uncomfortable relationships. Leaving feels like a bigger loss than the possible gain of moving on, so the brain stays stuck even when the situation is bad.

What makes this topic interesting is that it does not mean the brain is broken. It is just trying to use less energy. System 2 is there, but it does not run all the time because it is expensive. So a lot of the time, people are not making decisions by deep calculation. They are making decisions through shortcuts, frames, and quick reactions.