Heuristics, Biases and Risk Preference

1. Cognitive Psychology and Heuristics

Cognitive psychology is the science of representing and processing information in the human mind. Heuristics are informal mental shortcuts – “rules of thumb” – that reduce the cognitive load of complex decisions. Kahneman’s dual‑system thinking frames heuristics as System 1 (reflexive, fast) thinking, as opposed to System 2 (deliberate, analytical).

Why do we rely on heuristics?

• They speed up cognition – decisions are made quickly.
• They lower cognitive load – the brain conserves energy.

The world grows increasingly complex, yet the human mind has not evolved appreciably in 10 000 years. Hence we are forced to use fast, reflexive thinking more often. The downside: heuristics can produce systematic errors called biases.

Key terms: Cognitive psychology, heuristics, dual‑system thinking (System 1 vs System 2), biases.

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2. The Four Key Judgment Heuristics (Tversky & Kahneman)

1. Representativeness – judging probability by how similar something is to a mental model.
2. Availability – judging frequency by the ease with which examples come to mind.
3. Anchoring – relying too heavily on an initial piece of information (the “anchor”).
4. Affect – making decisions based on emotional reactions.

The remainder of this sub‑section focuses on Representativeness and its associated biases.

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3. Representativeness Heuristic

Representativeness is the heuristic a decision‑maker uses when she takes similarity as a proxy for probability. We ask: “Does this case resemble my mental model?” If it does, we assume a high probability – even when logical or statistical reasoning would contradict.

3.1 Conjunction Fallacy (The Linda Problem)

Linda is 31, single, outspoken, very bright. She majored in philosophy. As a student she was deeply concerned with issues of discrimination, social justice, and participated in antinuclear demonstrations.
Which is more probable?
A. Linda is a bank teller.
B. Linda is a bank teller and active in the feminist movement.

Intuition: Most people (89% of undergraduates, and even Stanford PhD students) choose B – the conjunction – because Linda’s description fits the stereotype of a feminist bank teller better than a plain bank teller.

Formal logic: The set of feminist bank tellers is a subset of bank tellers. Therefore:

$P(\text{bank teller and feminist}) \leq P(\text{bank teller})$

The conjunction fallacy occurs when a specific conjunction is judged more probable than a more general condition – a direct conflict between intuitive (System 1) thinking and probability theory.

Exam tip: A subset can never be more likely than the set that contains it. The Linda problem is the classic demonstration of the conjunction fallacy.

3.2 Insensitivity to Base Rates

Base‑rate neglect – ignoring the prior probability of a category when evaluating a specific case.

Example 1 – Earthquake:

A. A massive earthquake somewhere in India next year killing >1000 people.
B. A massive earthquake in Uttarakhand next year causing avalanches, killing >1000 people.

Most people judge B more likely, even though Uttarakhand is a subset of India. The added detail (“causing avalanches”) makes the scenario more representative of an Uttarakhand earthquake, so it feels more plausible – but:

$P(\text{earthquake in India}) \geq P(\text{earthquake in Uttarakhand})$

Example 2 – Dick the engineer/lawyer:

In treatments 1 and 2, participants ignore the completely different base rates and rely on the representativeness of the description. In treatment 3, they also ignore base rates (should be 70% or 30%), reverting to an uninformed 50‑50 guess.

3.3 Insensitivity to Sample Size

People fail to account for the fact that smaller samples produce more variable outcomes.

Hospital problem:

Large hospital: 45 births/day; Small hospital: 15 births/day.
For a year, each recorded days with >60% boys. Which hospital recorded more such days?
A. Larger B. Smaller C. About the same

Most choose C. The correct answer is B – the smaller hospital. Reason: the variance of the sample proportion is larger when $n$ is small:

$\text{Var}(\hat{p}) = \frac{p(1-p)}{n}$

Worked analogy: Compare the chance of ≥60% heads in coin flips.

In a small sample, large deviations from the population parameter are far more likely. Because people rely on representativeness – 60% feels “random” regardless of sample size – they ignore sample size.

Real‑world application: Ad agencies use “4 out of 5 dentists recommend…” knowing consumers are insensitive to the (often tiny) sample size.

3.4 Misconceptions of Chance

A mistaken belief that random sequences must “look random” (i.e., have no runs), leading to the gambler’s fallacy: the expectation that after a streak of one outcome, the opposite outcome is due.

Coin toss examples:

• After 5 consecutive tails, people predict a head on the 6th toss. (Correct: each toss is independent; $P(H)=0.5$.)
• Which sequence is more likely?
  $\text{Red: } H\,T\,H\,T\,H\,T$
  $\text{Blue: } H\,H\,H\,H\,H\,T$
  Most choose red, because it “looks random”. In truth, both sequences are equally likely ($(0.5)^6$).

Again, the root is representativeness: the red sequence resembles a typical random outcome, so it is judged more probable.

Consequential real‑world applications:

• Fertility decisions: A household with two daughters, desiring a son, may try for another child because they believe a boy is “due”. Seema Jayachandran and Rohini Pande show this affects child health and sex ratios, contributing to Amartya Sen’s “missing women” in South Asia. The mathematician Laplace noted the same fallacy among French parents 150 years ago.
• Gambler’s fallacy: After ten losing poker hands, a player believes a win is imminent. After winning a lottery, a player changes their “lucky numbers” because they think the same numbers are unlikely to come up again – ignoring that the lottery draws with replacement.
• Sports commentary: “The law of averages will catch up” – e.g., a cricketer who has scored a 50 in four consecutive matches is said to be less likely to do so in the fifth.

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Key Takeaways

• Heuristics are System 1 shortcuts (fast, low effort); biases are mistakes that arise from them.
• Representativeness judges probability by similarity: “seems like” = “is likely”.
• Conjunction fallacy: $P(A \cap B) \leq P(A)$, but representativeness can violate this (Linda problem).
• Base‑rate neglect: detailed, representative scenarios cause people to ignore prior probabilities (Dick example).
• Sample‑size neglect: small samples are more variable; people treat all sample sizes as equally informative (hospital problem).
• Misconception of chance leads to the gambler’s fallacy – expecting reversals after streaks – with serious consequences in gambling, fertility decisions, and sports commentary.

Exam tip: For any question involving probability judgments, ask: Is the person being fooled by representativeness? Look for “similarity” overriding logical or statistical reasoning – that is the signature of this family of biases.

Regression to the Mean

Regression to the mean is the statistical phenomenon that after an extreme outcome, the next outcome tends to be closer to the average. Intuitively: when performance depends partly on luck, the best and worst performances are often flukes — and subsequent attempts will "regress" toward the person’s true ability level.

Kahneman’s flight-trainer story illustrates how easily regression is misinterpreted as causation. Flight instructors observed that:

• Praise after an exceptionally smooth landing was typically followed by a poorer landing.
• Harsh criticism after a rough landing was typically followed by improvement.

The instructor’s conclusion: punishment works, praise doesn’t. Kahneman recognised this as regression to the mean. Landing a fighter jet is highly variable. Those who performed extremely well were simply lucky; the next attempt was likely to be closer to their average (worse). Those who performed poorly were unlucky; the next attempt was likely to be closer to their average (better). The instructor’s praise or punishment had nothing to do with the change.

Common examples of regression to the mean

Exam tip: The key insight is that the instructor assigned a causal interpretation to a statistical pattern. Any time you see praise or punishment followed by a change, check whether regression to the mean is the real explanation.

Relation to representativeness heuristic

The representativeness heuristic assumes future outcomes are directly predictable from past outcomes (perfect correlation). Regression to the mean shows that extreme past outcomes are often not representative of future ones — especially in early stages of a process, outcomes tend to shift toward the mean over time.

Key takeaways

• Regression to the mean: extreme outcomes are likely followed by outcomes closer to the average.
• It often appears as a causal effect when it is purely statistical.
• Common in sports, business, education, and any domain with variable performance.
• Confusing it with cause-effect is a classic bias.

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Conjunction Fallacy

The conjunction fallacy occurs when people judge the joint occurrence of two events (A and B) as more probable than one of the individual events alone — violating the laws of probability. Intuitively: a specific, detailed story can feel more representative and therefore more likely than a generic one, even though the generic one must be at least as probable.

In a rational world, for any two events A and B: $\Pr(A \cap B) \leq \Pr(A) \quad\text{and}\quad \Pr(A \cap B) \leq \Pr(B)$

The Linda problem demonstrates this systematically. Subjects were given a description: “Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was very concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.”

Then asked to rank the probability of several statements. Two key options:

• Linda is a bank teller.
• Linda is a bank teller and active in the feminist movement.

The second (conjunction) was judged more likely than the first by a large majority — even though it is a subset of the first. The conjunction “feminist bank teller” appears more representative of the description than “bank teller” alone.

Key takeaways

• Conjunction fallacy: $\Pr(A \cap B) > \Pr(A)$ is impossible, but people commit it when the conjunction is more representative.
• Driven by the representativeness heuristic.
• Classic demonstration: the Linda problem.

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Confirmation Bias

Confirmation bias is the tendency to seek, interpret, and remember evidence in a way that confirms one’s pre-existing beliefs. Intuitively: once you decide something, you look for reasons you are right — not reasons you might be wrong.

The car-buying contrast

• Scientific approach: Gather reasons, then decide.
• Confirmation bias approach: Decide first, then find reasons to justify that decision.

Lord, Ross & Lepper’s death-penalty experiment

Subjects were given two sets of studies — one supporting, one opposing the effectiveness of capital punishment. Both proponents and opponents of the death penalty evaluated the evidence:

• Proponents found the anti-death-penalty evidence methodologically flawed and unpersuasive.
• Opponents found the pro-death-penalty evidence methodologically flawed and the anti-penalty evidence persuasive.

After reading, both groups became more convinced of their original positions.

Why does confirmation bias exist?

The mental cost of processing a contrarian viewpoint is higher than processing a confirmatory one. Fischer et al. (2008) showed that mentally fatigued individuals were more likely to choose articles consistent with their viewpoint.

Yes Man vs. Devil’s Advocate (Thaler)

Exam tip: The Yes-Man/Devil’s-Advocate tradeoff is a classic application. Think about real organisations: do their internal cultures reward honest critics or loyal cheerleaders?

Key takeaways

• Confirmation bias: seeking and favouring evidence that supports existing beliefs.
• Mentally easier to process confirming than disconfirming information.
• Fuels polarisation and poor decision-making in organisations.
• Devil’s advocates may add more value but struggle to survive when leaders suffer from confirmation bias.

Availability Heuristic

Availability heuristic is the tendency to judge the probability or frequency of an event by the ease with which instances come to mind. Easily recalled events feel more common, even if they are not.

Evidence from experiments

• Causes of death ranking: Participants rank war and starvation as leading causes of death, but WHO data shows cancer, lung diseases, and respiratory infections are actually more common. The vivid, media‑heavy coverage of war and drought makes them retrievable → overestimated.
• Famous names list: A list with equal numbers of men and women, but one version contains more famous men, the other more famous women. Participants rate whichever gender had the more famous names as more numerous – because famous names are easier to recall.

Salience

Salience – the property of drawing attention – drives availability. Something becomes salient when it is:

• Familiar
• Important to the observer
• Part of personal experience
• Recent

Performance evaluation bias: Managers evaluating employees tend to give disproportionate weight to vivid, recent incidents (positive or negative) because those are easiest to recall. An employee who “goofed up in August” may be penalised heavily in an October review, even if overall performance was good.

Retrievability bias

A classic demonstration: Are there more English words with r as the first letter (e.g., road) or as the third letter (e.g., car)? Most people say first‑letter words are more numerous, but letters like r and k actually appear more often in the third position. The bias arises because our mental search engine retrieves words starting with a given letter far more easily than words with it in the third position.

Exam tip: Availability is easily confused with representativeness. Remember: availability is about ease of recall (influenced by salience, recency, vividness), not about similarity to a prototype.

Key takeaways – Availability

• Availability heuristic: frequency/probability judged by ease of recall.
• Driven by salience – familiarity, personal experience, recency, vividness.
• Leads to overestimating vivid causes of death and underestimating common diseases.
• Causes performance evaluation to overweight recent or vivid episodes.
• Retrievability bias (e.g., first‑letter vs. third‑letter words) is a direct consequence.

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Anchoring Heuristic

Anchoring heuristic is the tendency to make estimates by starting from an initial value (the anchor) and then adjusting insufficiently to reach a final answer.

Famous demonstration: product of 1–8

Participants were asked to quickly estimate the product of $1 \times 2 \times 3 \times \cdots \times 8$ (correct answer = 40,320). One group saw the ascending sequence (1,2,3,…); their average guess was 512. Another group saw the descending sequence (8,7,6,…); their average guess was 2,250. The initial few numbers acted as an anchor, and adjustments were far from the true value.

Multiple anchoring experiments

In every case, the anchor pulled the average guess toward itself, even though anchors were assigned randomly.

Why anchoring works

1. Lack of information – in ambiguous situations, the anchor provides a natural starting point; adjustments away from it are typically insufficient.
2. Confirmatory thinking – the anchor activates information that is consistent with it (similar to confirmation bias); people find it easier to think of reasons why the anchor might be right than why it might be wrong.

Real‑world implications

• Consumer choice (Mussweiler & Strack): After guessing the average new‑car price in Germany – some were given a high anchor (40,000 DM), others a low anchor (20,000 DM) – participants were asked to name four car brands. The high‑anchor group listed Mercedes, BMW; the low‑anchor group listed Golf, Volkswagen. Exposing consumers to high prices can raise willingness to pay.
• MBA salary negotiations: Companies often ask for pre‑MBA salary as an anchor. Students who bargain aggressively create a higher anchor for themselves. Research shows women bargain less than men, leading to a lower starting‑salary anchor – a possible contributor to the gender wage gap.

Exam tip: Anchoring affects even domain experts. The effect is robust and may operate subconsciously. To counter it, deliberately consider an opposite anchor before making a final estimate.

Key takeaways – Anchoring

• Anchoring: start with an initial value (anchor), adjust (insufficiently) to a final answer.
• Common in numerical estimation tasks (product of numbers, historical dates, heights).
• Works because of lack of information and confirmatory search.
• Powerful in consumer behaviour – extreme price anchors shift willingness to pay.
• Applies to salary negotiations, performance appraisals, and any context with ambiguous values.

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Affect Heuristic

Affect heuristic refers to the quick “good” or “bad” emotional response (affect) that arises before higher‑order reasoning engages. Many judgments are actually driven by this gut feeling.

Everyday examples

• Recruiter sorting CVs: Positive affect may arise if a candidate shares the same university, subject, or hostel block. Negative affect can come from unrelated features. The emotional tag influences the sorting decision.
• Affective polarization: In modern politics, people not only hold opposing views but also actively dislike supporters of the opposite party. This emotional response (affect) colours all subsequent political judgments.

The smoking paradox – from positive to negative affect

• 1960s – Positive affect: The tobacco industry used the Marlboro Man campaign to associate smoking with masculinity, freedom, and a rugged lifestyle. The product was sold via lifestyle imagery, bypassing risk information. Positive affect made people underestimate the health dangers.
• 2000s – Negative affect: Policy makers reversed the technique. Cigarette packets now carry gory health‑warning images that evoke disgust and fear. Video campaigns create negative emotional triggers. The goal is to make the product feel bad before any rational cost‑benefit analysis.

The affect heuristic is a core part of System 1 thinking: fast, automatic, emotional. It explains why factual information alone often fails to change behaviour – emotions must be engaged.

Exam tip: Affect is the newest of the four heuristics. It often overlaps with availability (vivid images are both affect‑laden and easy to recall). Examiners may ask you to distinguish them: affect is the emotional evaluation, availability is the ease of recall.

Key takeaways – Affect

• Affect heuristic: judgments driven by an immediate emotional “good/bad” response.
• Occurs automatically, before reasoning (System 1).
• Examples: recruiter bias from shared background, affective political polarization.
• Demonstrated powerfully in smoking – positive affect in Marlboro ads reduced perceived risk; negative affect in health warnings increased perceived risk.
• Policy makers now intentionally use negative affect to counteract harmful behaviours.

Overconfidence

Overconfidence – called the "mother of all biases" – is the systematic tendency for a person's subjective confidence in their judgments to exceed the objective accuracy of those judgments. It is considered the most robust finding in the psychology of judgment (Taylor). Its consequences are severe: wars, stock-market bubbles, strikes, unnecessary lawsuits, and major accidents (Challenger, Chernobyl) have all been linked to overconfidence.

Three distinct forms of overconfidence are distinguished:

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Overprecision

Intuition: When you are 98% sure a number lies inside a range, you should be wrong only 2% of the time. In reality, people are wrong far more often – their "confidence bands" are too tight.

Key studies

• Alpert & Raiffa (1982): Participants gave best estimates and 98% confidence intervals for unknown quantities (e.g., Walmart's 2010 revenue, world population in 2012). Of about 100 participants making 10 judgments each, most correctly bracketed only 3–7 of the true values – far fewer than the 98% they claimed. This shows systematic overprecision: people are far surer of their knowledge than they should be.

• McKenzie et al. (UCSD): To test whether unfamiliarity drives the effect, students (experts on UCSD campus) and computer programmers (experts on IT industry) were asked questions about both domains. Overprecision persisted in both groups, but was reduced when the context was familiar. Familiarity helps but does not eliminate the bias.

• Ben-David et al. (2013, QJE): Over 13,000 business executives were asked for 80% confidence intervals for future stock market returns (10-year panel). The realized return fell inside their 80% bands only 36% of the time. This miscalibration was worst during periods of high market uncertainty and also predicted miscalibration about the executives' own firm performance.

Real-life implications of overprecision

• A legal counsel 98% confident of winning will push for litigation instead of settlement.
• A marketing plan based on a sales forecast with an overly narrow confidence band will lack a Plan B – leaving the firm exposed.

Debiasing strategies

1. The "crowd within": Herzog & Hertwig (2009) asked people to generate multiple estimates of the same outcome and then average them. The averaged estimate was more accurate – a form of "wisdom of the crowd" applied within one person.

   Exam tip: Actively seeking others' perspectives gives a wider sense of where the truth lies – a practical antidote to overprecision.

2. Explicitly consider alternative outcomes: Harran et al. showed that asking people to state the chance of failure (e.g., "what is the percentage chance of failure?") after they have stated a 98% chance of success forces recalibration and improves accuracy.

Key takeaways (overprecision)

• Overprecision = confidence intervals that are too narrow for the true level of knowledge.
• Robustly observed even among experts in familiar domains, though familiarity reduces the magnitude.
• Real economic costs: bad legal advice, missing Plan B, misjudged firm performance.
• Debiasing: use multiple estimates (crowd within) and explicitly prompt the probability of failure.

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Overestimation

Intuition: We tend to think we are better across many domains than we really are – not relative to others, but relative to our own actual ability.

Manifestations

• Self-enhancement: Viewing oneself positively rather than accurately – leads to inflated perceptions of own performance, ability, and talent.
• Planning fallacy: Believing one will deliver a project on time when one consistently fails to do so. In work teams, this leads to unrealistic deadlines.
• Optimism bias: Overestimation of how positive the future will be. Culturally captured in phrases like Akash Kusum (Bengali) and Khayali Pulav (Hindi) – "imaginary rice". Self-help books often reinforce this bias.

Economic consequences of overestimation

• Effort: A person who overestimates their ability is less likely to put in effort for an upcoming exam (because they believe they already know enough).
• Savings: Overestimation leads to under-saving – the person believes they can generate sufficient resources in the future, so they save too little today.

Exam tip: Note the contrast: overestimation can reduce effort (because you think you don't need to try) while overplacement can increase risk-taking (because you think you are better than rivals). Both distort economic decisions.

Key takeaways (overestimation)

• Overestimation = inflated view of one's own absolute ability or future outcomes.
• Leads to planning fallacy, self-enhancement, and optimism bias.
• Real consequences: under-saving, insufficient effort, unrealistic deadlines.

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Overplacement

Intuition: The "better-than-average effect" – most people think they are above average at skills like driving, leading, or social competence.

Classic evidence

• 93% of American drivers rated themselves as more skillful than the median driver.
• CAT (MBA entrance) students: 60% rated themselves in the top 10% of ability to get along with others; 25% placed themselves in the top 1% – a statistical impossibility.

Real-world consequences

• Litigation: Thompson & Loewenstein (1992) – because parties overplace their chances of winning, they fight too hard, hold on too long, and overpay lawyers.
• Entrepreneurship: Entrepreneurs who believe they are more capable than competitors enter new markets even when objective success chances are low.
• Mergers & Acquisitions: Malmendier & Tate found that CEOs who overplace their ability relative to other managers pursue M&A deals that destroy shareholder value – decisions driven by overplacement, not rational calculation.

Key takeaways (overplacement)

• Overplacement = the "better-than-average" effect.
• Ubiquitous in self-reports of skill, driving, academic ability.
• Drives excessive litigation, inefficient market entry, and value-destroying M&A.

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Summary: Overconfidence – the three forms

Overarching key takeaways

• Overconfidence is pervasive and has severe real-world consequences across wars, markets, litigation, and corporate decisions.
• The three forms (overprecision, overestimation, overplacement) are distinct but often co-occur.
• Debiasing tools exist: generating multiple estimates, explicitly considering failure probabilities, and seeking external perspectives.
• Overprecision persists even among experts and in familiar domains – it is not merely a lack of knowledge.
• Overestimation can reduce effort and savings; overplacement fuels excessive competition and risk-taking.

Definition and Intuition

Exponential growth bias (EGB) is the pervasive tendency to linearize exponential functions when assessing them intuitively. People mentally replace the true exponential curve with a straight line, leading to a systematic underestimation of future values given a present value, or overestimation of present values given a future value.

EGB is not a simple arithmetic mistake — it reflects a deep cognitive failure to grasp how repeated multiplication (compounding) behaves. In daily life it explains why milk suddenly boils over, why cities like Bangalore gridlock, and why water hyacinth can choke a pond overnight: the growth rate appears slow at first, then accelerates faster than intuition can track.

Classic Example: Sissa and the Chessboard

The story of Sissa ibn Dahir (legendary inventor of chaturanga, a precursor to chess) captures EGB perfectly. Asked for a reward, Sissa requested:
1 grain on the first square, 2 on the second, 4 on the third, 8 on the fourth … doubling each time. The king, thinking only of the early small numbers, agreed. By the 64th square the required grains totalled $2^{63}$ — more than the entire empire’s wealth. The king’s intuition linearised the doubling; EGB made him bankrupt.

Formal representation:
Number of grains on square $n$: $2^{(n-1)}$.
Total after 64 squares: $\sum_{i=0}^{63} 2^{i} = 2^{64} - 1$ — astronomically large.

Consequences of EGB

The underlying causal chain:

Exam tip: EGB is a cornerstone behaviour that links financial decisions (borrowing/saving) to health decisions (compliance). The chessboard story is a classic exam starter.

Key takeaways

• EGB = tendency to linearise exponential growth → systematic underestimation.
• Drives over-borrowing, under-saving, and poor risk anticipation.
• Evident in everyday phenomena (boiling milk, traffic) and formal puzzles (chessboard).
• Foundational for understanding how people misjudge compound interest and epidemic spread.

Study 1: EGB and COVID-19 Compliance

Design. Participants saw the confirmed COVID-19 case count for weeks 1, 2, and 3 (from Germany, USA, France, Spain) during both early and later phases of the pandemic. They predicted the total cases for weeks 4 and 5. Incentive: small payment if prediction was within 5% of the actual number. After prediction, participants completed a survey measuring three compliance indices:

• Actual realised compliance
• Appropriateness of norm violation
• Agreeableness with government’s performance

Results.

1. The median prediction for week 4 and week 5 was significantly lower than the actual number.
2. A bias measure was computed:
   $bias_{t|3} = \text{prediction error relative to week 3 (log-scale used)}$
   Averaged across weeks 4 and 5, the bias was positive and significantly different from zero — confirming EGB.
3. EGB was negatively correlated with compliance behaviour: participants with higher EGB were less likely to wear masks, social distance, etc., and were more likely to view norm violations as acceptable.

Interpretation. EGB reduces perceived future risk → lower motivation to comply with public health measures.

Study 2: Reducing EGB with Behavioural Nudges

This follow-up experiment tested whether simple, behaviourally informed interventions could reduce or eliminate EGB and thereby adjust economic expectations (optimism about future macroeconomy).

Experimental design.

All treatments were identical in stage 2: participants invested a hypothetical amount in a stock market of the fictitious country, with returns based on the actual Day‑35 market performance.

Outcome variables.

• Bias (prediction error) and EGB (derived from bias)
• Economic expectations (measured via RBI’s current situation index, future expectation index, and Michigan Consumer Survey items)

Key results.

1. EGB present. Baseline predictions were far from actual — large bias.
2. Step reduced EGB; Feedback (both number and graph) and Forecast virtually eliminated EGB (bias not significantly different from zero).
3. Economic expectations were lower (more realistic / less optimistic) in Step, Feedback, and Forecast treatments compared to Baseline.

Caveat for comparison. Only Day‑35 predictions are comparable across all treatments because Baseline lacks intermediate predictions. For comparisons among Step‑based treatments, the average bias over Days 15–35 can be used.

Exam tip: Behavioural nudges that force step‑by‑step prediction or provide feedback (especially graphical) can correct EGB. This is a key example of “debiasing” through choice architecture.

Key takeaways

• EGB is robust (observed in COVID‑19 case predictions across countries).
• Step‑by‑step prediction reduces EGB; adding feedback or a model forecast can eliminate it.
• Reducing EGB makes economic expectations less optimistic and improves compliance intentions.
• Behaviourally informed policy design: simple changes in how information is presented can correct systematic bias.

Hindsight Bias

Hindsight bias is the tendency to perceive past events as having been more predictable than they actually were before they occurred. In plain language: after something happens, people readily believe “I knew it all along.” This bias distorts how we evaluate decisions and limits genuine learning.

Classic examples from everyday life

• Financial crisis 2008 — Many policymakers later claimed they “knew the crisis was inevitable,” yet the majority of macroeconomists did not see it coming. The question: was it truly knowable?
• Cricket, 1986 Sharjah Cup final — India vs. Pakistan; last ball, four runs needed. Captain gave the ball to Chetan Sharma; Javed Miandad hit a six. Retrospectively, “everyone” knew Chetan Sharma should not have bowled that ball.
• Driving with a spouse — At an unmarked fork your spouse turns right, you end up on the wrong route. You say, “I knew you should have turned left.” Did you really know?
• Hiring disagreement (Ali and Rani) — After Shyam’s poor performance, Ali says “there was a lot of evidence Shyam would be lousy,” though he had previously agreed to go with Rani’s view.
• Marketing presentation — A senior VP says, “I could have told you these results,” dismissing a six‑month consumer study.

Evidence: Fischhoff (1975)

Fischhoff experimentally demonstrated the gap between hindsight and foresight.

• Participants read a passage about the British vs. Gurkha war of 1814.
• They were divided into five groups, each given a different “result” of the war:
  • British won
  • Gurkhas won
  • Military stalemate with no peace settlement
  • Military stalemate with peace settlement
  • Baseline: result not shared
• All groups were then asked to assess the probability of each outcome without the benefit of that knowledge.
• Result: Participants in every group claimed they would have reached the same conclusion regardless of which outcome they were shown. In a rational world, this is impossible — it’s driven by hindsight bias.

Why it matters: pros and cons

Exam tip: Hindsight bias is often tested with the Fischhoff experiment. Remember the key finding: different groups given different “outcomes” still believed they would have predicted that same outcome. The bias arises partly from imperfect recall – we misremember our earlier predictions.

Key takeaways

• Hindsight bias = “I knew it all along” phenomenon; events seem more predictable after the fact.
• It limits learning and prevents fair evaluation of past decisions.
• Fischhoff’s war study is the classic empirical demonstration.
• The bias is flattering but harmful for decision‑making.

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Curse of Knowledge

Curse of knowledge is the difficulty of un knowing something once you know it — and the resulting inability to imagine how others, who lack that knowledge, perceive a situation. It causes experts to overestimate what novices understand.

Everyday examples

• Fresh PhDs becoming assistant professors struggle to explain concepts simply to undergraduates — they cannot “unlearn” five years of deep expertise.
• Product designers often include advanced features that the average user finds too complex, underestimating the learning curve.

Empirical evidence: Kysar (cognitive psychology)

Participants read: “David had dinner at a restaurant based on a friend’s recommendation.”

• Treatment 1: David enjoyed the meal.
• Treatment 2: David disliked the meal.
• Both groups then read: “David wrote to his friend: about the restaurant it was marvellous just marvellous.”
• Question: Would the friend read the comment as sincere or sarcastic?
• Result:
  • Treatment 1 (David enjoyed) → “sincere”
  • Treatment 2 (David disliked) → “sarcastic”
• Why is this interesting? Because David’s friend did not know how David’s meal went — but the participants did. They could not ignore that information when predicting the friend’s interpretation. This is the curse of knowledge in action.

Organizational implications

A great deal of disappointment in organisations arises from poor communication — we mistakenly believe that our ambiguous messages are clear to others. A known remedy is to adopt a mindset of focusing on differences (between people and objects) rather than similarities. People who focus on differences are better at taking others’ visual perspective and less likely to project their own private information onto others.

Key takeaways

• Curse of knowledge = inability to forget what you know when judging what others know.
• It leads to over‑complex products, poor teaching, and communication failures.
• Kysar’s restaurant study shows how knowledge of an outcome biases interpretation of a message.
• A practical fix: focus on differences, not similarities, to reduce projection.

Changes vs. Levels

Intuition: Standard neoclassical economics says satisfaction depends on absolute levels — the number of mangoes you eat or the money you earn. But real human happiness responds to changes from a reference point, not just final levels.

• Thought experiment: Same final score (95/100) from (a) 95 on both midterm and final, or (b) 65 on midterm and 95 on final. Most people feel happier in (b) — the change matters.
• Ali vs. Rani: Ali (poor, ₹500/month) suddenly earns ₹1 crore; Rani (rich, ₹1 crore) earns ₹500. The final income is nearly the same, but Ali’s happiness is far greater. Humans compare to a reference point; “econs” (rational agents) do not.
• Perception parallel: Hot water / cold water experiment — left hand in hot, right hand in cold, then both in lukewarm. Sensation depends on the change from prior state.
• Pain management: Cancer patients are told to accept pain as the “new normal” — the reference point shifts to reduce emotional trauma.

Reference Points

The change is computed from some reference point. Candidates:

• Expected outcome (what one anticipates)
• Fair outcome / Status quo / Legal entitlement
• Average outcome in society
• Personal goal / Social hero’s outcome

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Key takeaways

• People care about changes, not absolute levels.
• Satisfaction is measured relative to a reference point.
• Reference points are shaped by expectations, fairness, status quo, etc.
• This idea underlies the first key principle of prospect theory: changes matter more than levels.

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Losses and Gains

The second key principle: losses matter more than gains. In standard utility theory, the pleasure of a gain equals the pain of an equal loss (symmetry). Prospect theory rejects symmetry.

Mug Experiment (Kahneman, Knetsch, Thaler)

• Sellers (given a mug) see selling as a loss — reference point = possession.
• Buyers (no mug) see buying as a gain — reference point = non-possession.
• Results: Willingness to Accept (WTA) ≈ $7, **Willingness to Pay (WTP)** ≈$3.
• Implication: the value function is steeper in the loss domain than in the gain domain.

Duke Basketball Tickets (Carmon & Ariely)

• Lottery-based distribution (eliminates selection effect — those who get tickets aren’t inherently higher-valuing).
• Sellers’ WTA: ~$2,400; buyers' WTP: ~$170.
• Massive gap again — losses loom larger than gains.

Loss Aversion & Endowment Effect

Loss aversion: the pain of a loss is about twice the pleasure of an equivalent gain. Researchers estimate the loss aversion parameter (slope ratio of loss domain to gain domain) ≈ 2.

Endowment effect: people value an object more once they own it — driven by loss aversion.

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Key takeaways

• Losses hurt more than gains please (asymmetry).
• Value function: flatter for gains, steeper for losses.
• Loss aversion parameter ≈ 2 (empirical estimate).
• The endowment effect (e.g., mug, tickets) is a direct consequence of loss aversion.

Exam tip: The mug experiment and Duke tickets are classic demonstrations of endowment effect. Be able to explain why lottery distribution is important (controls for selection bias).

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St. Petersburg Paradox

The paradox highlights that people do not simply maximise expected value — they care about expected utility.

The Gamble

• Start with $1. Flip a coin.
• If heads → double the money and flip again.
• If tails → game ends, take current money.
• You can play until first tail.

Expected Value

$EV = 1 + \frac{1}{2}\times 2 + \frac{1}{4}\times 4 + \frac{1}{8}\times 8 + \cdots = \sum_{k=0}^{\infty} 1 = \infty$

Despite infinite expected value, people pay very little to play.

Resolution via Expected Utility

Assume utility function $U(x) = \log(x)$. Then:

$EU = \log(1) + \frac{1}{2}\log(2) + \frac{1}{4}\log(4) + \frac{1}{8}\log(8) + \cdots$

This series converges to a finite value (≈ 0.602 utility units). People are risk-averse — concave utility reduces the appeal of the gamble.

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Key takeaways

• Expected value alone cannot explain risk-taking behaviour.
• The St. Petersburg Paradox shows why expected utility framework is needed.
• Concave utility functions (diminishing marginal utility) make infinite-expected-value gambles unattractive.

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Definition

A risk-averse individual prefers a certain income over a risky lottery with the same expected value.

Graphical Explanation

Consider a gamble: 50% chance of ₹10,000, 50% chance of ₹30,000. Expected value = ₹20,000. A risk-averse person with a concave utility function will choose the certain ₹20,000.

• Utility at ₹10,000 = 10, at ₹30,000 = 18. Expected utility = $0.5\times 10 + 0.5\times 18 = 14$.
• Utility of certain ₹20,000 = 16.
• Since $16 > 14$, the sure thing is preferred.

For a concave utility function: $U(EV) > E[U(x)]$ (Jensen’s inequality).

Risk Premium

Risk premium = the maximum amount an individual is willing to pay to avoid risk. It is the difference between the expected value of the gamble and the certainty equivalent (the sure amount that gives the same utility as the gamble).

From the graph: certainty equivalent ≈ ₹16,000. Risk premium = ₹20,000 – ₹16,000 = ₹4,000.

Exam tip: Risk premium = $EV - \text{certainty equivalent}$. Insurance companies price premiums based on this concept.

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Key takeaways

• Risk aversion: prefer sure thing over equally valued risky gamble.
• Concave utility → $U(EV) > E[U(x)]$.
• Risk premium = amount paid to avoid risk; equals $EV - \text{certainty equivalent}$.
• Insurance exploits risk premium.

Sensitivity to Gains and Losses

When humans choose between risky gains, they behave differently than when choosing between risky losses. This pattern – risk aversion in the gain domain, risk seeking in the loss domain – is a core finding of prospect theory and cannot be explained by expected utility theory.

Reference Points and Choice Patterns

The key is that people evaluate outcomes relative to a reference point, not as final wealth. The following choice problems (from Kahneman & Tversky) demonstrate the effect.

Problem 1 (Gain frame)

• Option A: Get $900 for sure.
• Option B: 90% chance of $1000, 10\% chance of$0.
• Most choose A – risk averse.

Problem 2 (Loss frame)

• Option A: Lose $900 for sure.
• Option B: 90% chance of losing $1000, 10\% chance of losing$0.
• Most choose B – risk seeking.

The same pattern appears when an endowment is added:

Preference reversal: The utility inequalities from Problem 3 and Problem 4 are identical except for the inequality sign – a violation of expected utility.

Exam tip: The same expected value can be framed as a gain or a loss. The frame determines whether people are risk averse or risk seeking.

The Value Function (Prospect Theory)

Prospect theory replaces the utility function with a value function $v(x)$ defined over gains and losses (deviations from the reference point). Three principles define its shape:

1. Changes matter more than levels – The reference point is the status quo; outcomes are coded as gains or losses.
2. Losses matter more than gains – The value function is steeper in the loss domain; the loss aversion coefficient $\lambda \approx 2$ means the pain of losing $X$ feels about twice the pleasure of gaining $X$.
3. Risk averse in gains, risk seeking in losses – $v$ is concave for gains ($v''(x)<0$) and convex for losses ($v''(x)>0$).

Mathematically:

$$v(x) = \begin{cases} x^\alpha & x \ge 0 \\ -\lambda (-x)^\beta & x < 0 \end{cases}$$

with $0<\alpha,\beta<1$ and $\lambda>1$ (typically $\lambda \approx 2$).

Framing: The Asian/American Disease Problem

Problem 1 (lives saved – gain domain)

• Program A: 200 people saved for sure.
• Program B: 1/3 chance 600 saved, 2/3 chance 0 saved.
• Most choose A – risk averse.

Problem 2 (lives lost – loss domain)

• Program C: 600 people die for sure.
• Program D: 1/3 chance 0 die, 2/3 chance 600 die.
• Most choose D – risk seeking.

The outcomes are identical: A saves 200 (C lets 400 die); B has expected 200 saved (D has expected 400 die). The reference point shifts: 0 lives saved in problem 1 → gain domain; 600 lives saved (i.e., 0 lost) in problem 2 → loss domain.

Reference point (gain domain): 0 saved
   v(200 saved) > expected v of gamble → choose A

Reference point (loss domain): 600 saved (= 0 lost)
   v(400 die) < expected v of gamble → choose D

The value function’s concavity (gains) and convexity (losses) directly predicts this preference reversal.

Key Takeaways (Sensitivity to Gains and Losses)

• People are risk averse in the gain domain and risk seeking in the loss domain.
• The reference point determines whether an outcome is coded as a gain or loss.
• The value function is concave for gains, convex for losses, and steeper for losses.
• Loss aversion coefficient $\lambda \approx 2$: losses hurt roughly twice as much as gains please.
• Framing (e.g., “lives saved” vs “lives lost”) reverses choices even when expected outcomes are identical.
• These patterns are anomalies for expected utility theory.

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Risk Perception

How people perceive probabilities is not linear. The Allais paradox and other experiments show that decision weights differ from objective probabilities.

Allais Paradox

Expected utility analysis shows inconsistency:

$$\text{B preferred to A } \Rightarrow 0.34 \cdot u(2400) > 0.33 \cdot u(2500)$$ $$\text{C preferred to D } \Rightarrow 0.33 \cdot u(2500) > 0.34 \cdot u(2400)$$

These contradict each other. The Allais paradox violates the independence axiom of expected utility.

Russian Roulette Experiment (Zuckerman)

Participants were asked how much they would pay to reduce the number of bullets in a 6‑chamber gun:

• From 4 to 3 bullets (probability reduction 4/6 → 3/6).
• From 1 to 0 bullets (probability reduction 1/6 → 0).

People pay much more to reduce 1→0 than 4→3, even though the probability reduction is the same (1/6). This shows overweighting of low probabilities (the jump from 1/6 to 0 is near certainty of safety) and underweighting of high probabilities (4/6 → 3/6 is in the upper range).

Possibility Effect and Certainty Effect

A 5‑percentage‑point increase in the chance of winning $1 million:

• Possibility effect: improbable outcomes are overweighted.
• Certainty effect: near‑certain outcomes are underweighted relative to full certainty.

Key Takeaways (Risk Perception)

• The Allais paradox shows that people violate the independence axiom: choices between gambles are inconsistent with expected utility.
• Low probabilities are overweighted; high probabilities are underweighted.
• Small changes near 0 (possibility) and near 1 (certainty) have a disproportionate psychological impact.
• These effects shape how people evaluate risk, especially for rare events.

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Probability Weighting Function

Prospect theory models subjective probability processing via a probability weighting function $w(p)$ that maps objective probabilities $p$ onto decision weights. The weighting function is S‑shaped:

• $w(p) > p$ for small $p$ (overweighting)
• $w(p) < p$ for large $p$ (underweighting)
• Crosses the 45° line at one point (typically around $p \approx 1/3$)

The function is inverse‑S in shape: steep near 0 and 1, flatter in the middle.

$$w(p) = \frac{p^\gamma}{(p^\gamma + (1-p)^\gamma)^{1/\gamma}}, \quad 0<\gamma<1$$

Exam tip: The probability weighting function explains the Allais paradox and the Russian roulette experiment. Always check whether a problem involves small or large probabilities – overweighting of small probabilities often drives risk‑seeking for long shots.

Real‑World Examples

• Crack dealers (Freakonomics): Most earn below minimum wage, but a tiny chance of hitting the top is overweighted – they stay despite the risk.
• Aspiring film directors and cricketers: The very low objective probability of success is over‑weighted, motivating people to take risky paths (moving to Mumbai, training for national team).

These examples are consistent with (but not uniquely explained by) probability weighting.

Key Takeaways (Probability Weighting)

• Decision weights $w(p)$ replace objective probabilities $p$ in prospect theory.
• The weighting function is S‑shaped: low $p$ overweighted, high $p$ underweighted.
• The possibility effect (overweighting of very small probabilities) and certainty effect (underweighting of near‑certainty) are both captured by $w(p)$.
• Probability weighting explains why people gamble on long‑shot risks and why they over‑insure against low‑probability catastrophes.

Mental Accounting

Mental accounting is the tendency to treat money differently depending on its source, intended use, or the mental “account” to which it is assigned – even when the total amount is identical. This violates the classical economic principle of fungibility (the idea that money can be freely substituted across uses).

Definition (Thaler): Mental accounts are the set of cognitive operations people use to organize, evaluate, and keep track of financial activities. Individuals assign transactions to exogenously created categories and, in doing so, do not maximise total wealth; instead they minimise losses within each account.

Illustrations of mental accounting

These effects can coexist: a person can feel simultaneously triumphantly wealthy (in one account) and perilously poor (in another).

Consequences of mental accounting

• Borrow high, save low: People take out high‑interest loans while maintaining low‑interest savings, unwilling to “mix accounts”.
• Credit card debt: People often do not use savings to pay off credit card balances because the savings and debt belong to different mental accounts.

Experimental evidence (Heath & Soll, JCR)

Participants estimated weekly spending in three categories: entertainment, food, clothes. They were then asked how much they would spend after a specific expense.

Spending in one category depresses spending only within the same or closely related mental account; it does not spill over to unrelated categories.

Key takeaways – Mental Accounting

• People create separate mental accounts for different income sources and spending categories, violating fungibility.
• Decisions are driven by the budget constraint within each account, not total wealth.
• This explains borrowing while saving, ignoring credit card payoff using savings, and feeling simultaneously wealthy and poor.
• Experimental evidence shows spending in one category primarily reduces spending in the same (or related) category.

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Bundling Gains and Losses

Given the value function from prospect theory:

$$v(x) = \begin{cases} x^\beta & \text{for } x \ge 0 \\[2pt] -\lambda (-x)^\beta & \text{for } x < 0 \end{cases}$$

with typical values $\beta = 0.88$, $\lambda = 2.25$. The function is concave for gains (diminishing sensitivity) and convex and steeper for losses (loss aversion). This curvature determines how people prefer to combine or separate multiple outcomes to maximise experienced utility.

Segregating Gains

Principle: For multiple gains, segregation (treating them as separate) yields higher total value than integration (lumping them together).

$v(25) + v(50) > v(75)$

Why: Concavity implies $v(a) + v(b) > v(a+b)$ for positive $a,b$. The reference point resets after the first gain, so the second gain is evaluated from the new reference point.

Worked example – Tax refund ₹4800 and friend repayment ₹2700 (both gains).

• Integration: $v(4800+2700) = v(7500)$
• Segregation: $v(4800) + v(2700)$
  Because of concave curvature, $v(4800)+v(2700) > v(7500)$. Segregation is preferred.

Integrating Losses

Principle: For multiple losses, integration (lumping them together) yields higher (less negative) total value than segregation.

$v(-100) \; \text{(one loss)} \; > \; v(-50) + v(-50) \; \text{(two losses)}$

Why: Losses are convex in the negative domain; $v(a+b) > v(a)+v(b)$ for negative $a,b$ (more precisely, the loss function is steeper and convex, so two smaller losses cause more disutility than one larger loss).

Worked example – Two losses of ₹50 vs. one loss of ₹100 (with $\beta=0.88$, $\lambda=2.25$).

$$\begin{aligned} v(-50) &= -2.25 \times 50^{0.88} \approx -2.25 \times 31.3 = -70.4 \\ 2 \times v(-50) &\approx -140.8 \\[4pt] v(-100) &= -2.25 \times 100^{0.88} \approx -2.25 \times 57.5 = -129.5 \end{aligned}$$

Result: $v(-100) > v(-50)+v(-50)$. So integrating the loss is better (less painful).

Mixed outcomes (gains and losses)

Exam tip: The only ambiguous case is large loss + small gain. Check the net loss: if the net loss is large, segregate (“silver lining”); if the net loss is small, integrate. For all other combinations (gains/gains, losses/losses, small loss/large gain), the rule is unambiguous.

Managerial implications

Key takeaways – Bundling Gains and Losses

• Due to concave gains: segregate gains (multiple bonuses > one big bonus).
• Due to convex, loss‑averse losses: integrate losses (one penalty > several small ones).
• For mixed outcomes: integrate when net is positive or net loss is small; segregate (silver lining) when net loss is large.
• Managers can design bonuses and penalties to exploit these principles to shape employee motivation and behaviour.

Acquisition Utility and Transaction Utility

Acquisition utility is the conventional economic utility a consumer derives from consuming a good itself — the classical “value for money” based on intrinsic benefit. Transaction utility is the psychological value from paying a price below a reference price — i.e., the thrill of a deal.

$\text{Total Utility} = \text{Acquisition Utility} + \text{Transaction Utility}$

• Acquisition utility depends only on the good and its price relative to its benefit.
• Transaction utility depends on the gap between actual price and a reference price (what the consumer thinks is “fair” or expects to pay).

Key illustrations

1. The quilt that didn’t fit
Maya Bar-Hillel wanted a double-bed quilt. All sizes were on sale at $150 (regular prices: king$300, queen $250, double$200). She bought the king size — even though it would hang over her double bed — because the transaction utility (saving $150 vs. king’s regular price) outweighed the lower acquisition utility of a poorly fitting quilt.

2. Auto vs. Uber (Christ University to Bangalore)

• Auto: ₹150 (metered price ~₹72) → friend prefers it for low acquisition utility (cheaper ride).
• Uber: ₹150 (fair preannounced price) → narrator prefers it for positive transaction utility (paying equal to reference price, not above).
  The difference: friend cares about acquisition utility; narrator cares about transaction utility.

3. Beach beer experiment (Thaler)
On a hot beach, you have only ice water. A companion offers to buy your favourite beer from the only nearby place — either a fancy resort hotel or a small rundown grocery store. The acquisition utility (cold beer) is identical in both scenarios; only the reference price changes.
Results: median willingness-to-pay was $7.25** (resort) vs. **$4.10 (grocery). People form a higher reference price for the resort, so paying even $7 feels like a fair deal. The same beer, same situation, different transaction utility.

Marketing implications

• MRP / Suggested retail price is used to set an artificially high reference price, creating transaction utility when the actual price is lower.
• Discounts are more common for infrequently purchased goods (laptops, fashion, electronics) because consumers have no daily reference; MRP becomes the anchor. For frequent purchases (FMCG, stationery), the reference is what you paid yesterday — discounts are rare.
• Macy’s (2006-2007): Used coupons extensively. In spring 2007 they cut coupons by 30% → sales plummeted. Coupons generated transaction utility by making the reference price (MRP) seem high.
• J.C. Penney: CEO Ron Johnson called full prices “fake prices”, eliminated coupons and prices ending in .99. Less than 1% of revenue came from full price transactions. After the change, sales fell, stock dropped, and he was ousted. Coupons returned.
• Walmart / Costco: Generate transaction utility via lowest-price guarantees — the reference price becomes the outside market price, not MRP.

Extreme case: acquisition utility = 0

When a product is not needed (acquisition utility zero) but is on sale (positive transaction utility), people buy useless things purely for the deal. Example: buying an item just because it’s discounted.

Exam tip: Transaction utility explains why “sale” signs work even when the product is not essential. The size of the reference price gap drives the purchase, not the product’s intrinsic value.

Key takeaways

• Total utility = acquisition utility + transaction utility.
• Transaction utility depends on the difference between actual price and a context-dependent reference price.
• Reference prices are influenced by store type, past prices, MRP, coupons, and guarantees.
• Frequent purchases have stable reference prices; infrequent purchases rely on suggested retail prices.
• Negative transaction utility (paying above reference) can reduce overall satisfaction, even for identical goods.

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Sunk Costs

Sunk costs are past, unrecoverable expenditures. Classical economics (econs) ignores them: only future costs and benefits should matter. Humans, however, often continue a course of action because of what they have already invested — the sunk cost fallacy.

Why sunk costs matter: mental accounting

When you pay for something, you open a mental account with the expectation to close it by consuming the good. If you do not consume, you close the account with a loss (the money gone with no benefit). Because losses are painful (loss aversion), you are driven to “use” the sunk cost to avoid that negative account closure.

Concert ticket example
You paid ₹2000 for a concert. It’s raining heavily. If you go: account closes with (value of concert) – ₹2000. If you skip: account closes with –₹2000 (a pure loss). Loss aversion pushes you to brave the rain.

Illustrations

Payment depreciation effect (Gourville & Soman)

The psychological impact of a sunk cost diminishes with time. At a health club:

• Members attended more immediately after the billing date.
• As time passed, attendance declined — the payment felt less “salient”.
• The pattern repeated at each billing cycle.

Flip side: windfall gains

Milkman et al. found that people spend more at a grocery store after receiving a “$10 off" certificate - about$2 extra out of the $10. The certificate creates a mental account surplus (positive transaction utility), leading to additional spending.

Exam tip: Sunk cost fallacy is distinct from loss aversion but often works through it. The key test: are you continuing solely because of past unrecoverable costs? If yes, that’s the fallacy. Econs stop when marginal cost exceeds marginal benefit, regardless of past expenditure.

Key takeaways

• Econs ignore sunk costs; humans often cannot, due to loss aversion and mental accounting.
• Mental account: you want to “close” a purchase by consuming, to avoid a net loss.
• Larger sunk costs lead to greater persistence (e.g., more expensive shoes worn longer).
• Transaction utility can offset the pain of a sunk cost (shoe example C).
• Payment depreciation: the sunk cost effect fades with time, causing attendance patterns tied to billing cycles.
• Windfall gains (e.g., discount certificates) can increase spending — the opposite of sunk cost.