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Week 1: The pain of losing - Prospect Theory & Loss Aversion (Deep dive)
- CheesyGoulash
- Aug 22
- 16 min read
Why losses hurt more than gains—and how to design smarter money habits.
🔎 Content Overview
1. Introduction: The Pain of Losing
Imagine this: you’re at a small dinner party, and someone proposes a simple gamble. They’ll flip a coin. If it lands heads, you win €200. If it lands tails, you lose €100. Rationally speaking, you should jump at the chance. The expected value is positive (+€50). Over many repetitions, you’d likely come out ahead.
And yet, most people refuse. They smile politely, shake their head, and say, “No thanks, I’m not a gambler.”
Why? The reason is not about logic—it’s about psychology. The emotional sting of losing €100 feels much stronger than the thrill of gaining €200. We are wired to treat losses as more important, more painful, and more urgent than equivalent gains.
Would you take the challenge?
Yes!
No!
This phenomenon, known as loss aversion, is one of the most robust findings in behavioral economics. It sits at the heart of Prospect Theory, introduced by psychologists Daniel Kahneman and Amos Tversky in 1979. Their insight overturned decades of economic orthodoxy, challenged the notion of humans as “rational agents,” and reshaped fields as diverse as finance, marketing, health, and public policy.
Loss aversion explains why:
Investors cling to losing stocks instead of cutting their losses.
Consumers overpay for extended warranties and insurance.
“Free” offers feel irresistible.
Retirement savings improve when enrollment is automatic.
In this deep dive, we will explore Prospect Theory in detail—its origins, mechanics, applications, critiques, and practical tools. By the end, you will not only understand why losses hurt twice as much as gains but also how to design a personal financial system that works with your psychology rather than against it.
2. Historical Context: Economics Before Kahneman & Tversky
To understand the revolution of Prospect Theory, we must first understand what came before.
2.1 The Reign of Expected Utility Theory
For much of the 20th century, economics was built on the foundation of Expected Utility Theory (EUT). Formalized by John von Neumann and Oskar Morgenstern in Theory of Games and Economic Behavior (1944), EUT proposed that when faced with risky choices, people act as rational agents who:
Assign utilities (subjective values) to outcomes.
Multiply these utilities by their probabilities.
Choose the option with the highest expected utility.
EUT became the gold standard because it offered elegant mathematics and seemed consistent with rational choice. It could explain why some people are risk-averse (preferring a sure €50 over a 50% chance at €100), while others are risk-seeking.
Economists loved EUT because it treated humans like predictable, optimizing machines—calculators of probability and value.
2.2 Cracks in the Foundation
But in practice, people often violated EUT’s predictions.
The Allais Paradox (1953): Maurice Allais demonstrated that people’s choices depend on how options are framed, even when probabilities and outcomes should make them indifferent.
The St. Petersburg Paradox: People reject infinite expected value gambles because they seem absurd.
Ellsberg Paradox (1961): People prefer known probabilities over unknown ones, revealing ambiguity aversion.
These paradoxes suggested that the “rational agent” model was too simple. Human psychology—expectations, emotions, context—played a role that traditional economics ignored.
2.3 The Psychologists Step In
By the 1970s, psychologists like Kahneman and Tversky began systematically testing decision-making under risk. They found consistent, predictable patterns of deviation from EUT.
People cared more about changes relative to a reference point than about absolute wealth.
They overweighted small probabilities (lotteries, jackpots) and underweighted large ones (car accidents, chronic illness).
Most strikingly: they were loss averse—the pain of losing was about twice as powerful as the pleasure of gaining.
Economics had long assumed these deviations were “noise.” Kahneman and Tversky showed they were systematic bias.
3. The Birth of Prospect Theory (1979)
In 1979, Kahneman and Tversky published their landmark paper:
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. DOI: 10.2307/1914185
This paper transformed economics. It proposed Prospect Theory, a new descriptive model of decision-making under risk that better matched observed human behavior.
3.1 Key Innovations
Reference Dependence
People evaluate outcomes relative to a reference point (often the status quo, or expectations).
Example: Winning €50 feels disappointing if you expected €100, but thrilling if you expected nothing.
Loss Aversion
Losses are weighted about 2x more heavily than equivalent gains.
Losing €100 hurts as much as gaining €200 pleases.
Diminishing Sensitivity
The subjective difference between €100 and €200 feels greater than between €1,100 and €1,200.
The value function is concave for gains, convex for losses.
Probability Weighting
People overweight small probabilities (lottery tickets, rare disasters).
People underweight large probabilities (car accidents, retirement savings shortfalls).
3.2 The Value Function
Prospect Theory’s central idea is the value function, which replaces the utility function of EUT. It has three important features:
Defined over gains and losses, not final wealth.
Steeper for losses than gains, reflecting loss aversion.
Curved: concave for gains (risk-averse), convex for losses (risk-seeking when trying to “break even”).
Imagine a graph:
At the origin is the reference point.
To the right are gains: curve rises but flattens (diminishing returns).
To the left are losses: curve falls more steeply (losses hurt more).
This asymmetry—the kink at zero—is the hallmark of Prospect Theory.
3.3 Experimental Support
Kahneman & Tversky demonstrated Prospect Theory with a range of experiments:
The Asian Disease Problem (1981): People’s choices flipped depending on whether outcomes were framed as lives saved or lives lost.
Gamble Studies: People rejected favorable bets because of loss aversion.
Framing Effects: Identical options described in gain vs. loss terms led to opposite preferences.
The findings were clear: humans are not “rational agents” in the economic sense. We are biased, but systematically so..
4. Anatomy of Prospect Theory
Prospect Theory is not just a critique of rational-choice models; it is a structured alternative. Where Expected Utility Theory relies on a utility function defined over final wealth, Prospect Theory replaces this with a value function defined over gains and losses relative to a reference point, combined with a probability weighting function.
4.1 Reference Dependence
At the heart of Prospect Theory is reference dependence. People do not evaluate outcomes in absolute terms but relative to a baseline or expectation.
Example 1: Salary expectations. If you expect a raise of €5,000 but only get €2,500, it feels like a loss, even though your salary still increased.
Example 2: Lottery winnings. Winning €100 in a raffle feels exciting—unless you later discover your neighbor won €1,000, which makes your €100 feel like a loss by comparison. This is one of the big reasons with the "Postcode Loterij" is so popular, because the fear of your neighbours winning and you not is often higher than the loss of the monthly costs of the lottery.
This insight has profound implications. It means expectations, comparisons, and framing are central to how we feel about money. Absolute wealth matters less than whether it represents progress or setback from where we thought we’d be.
4.2 The Value Function
The value function is the cornerstone of Prospect Theory. It has three key features:
Defined on changes: Gains and losses relative to a reference point, not absolute outcomes. (So for example again the expectation of a €5.000 raise is the reference point then)
Concave for gains: Reflecting diminishing sensitivity (the difference between €100 and €200 feels bigger than between €1,100 and €1,200).
Convex for losses: Reflecting diminishing sensitivity in the loss domain as well (the pain of losing €100 vs. €200 is sharper than losing €1,100 vs. €1,200).
Steeper for losses than gains: The slope is about twice as steep for losses, capturing loss aversion.
Visually, the curve is kinked at the origin: a shallow upward curve for gains, a steep downward curve for losses.
This shape predicts several common behaviors:
Risk aversion for gains: Most people prefer a sure €50 over a 50/50 chance of €100.
Risk seeking for losses: Faced with losing €50 for sure or a 50/50 chance of losing €100 or nothing, many prefer the gamble—trying to “break even.”
Endowment effect: Once you own something, giving it up feels like a loss, which inflates its subjective value. This can be driving a nice car, living in a big house, or spending your holidays abroad. Scaling back on these things because you want to achieve financial freedom can be very hard because you feel like you are losing more of something, instead of gaining more freedom in the future.
4.3 Probability Weighting
Expected Utility Theory assumes probabilities are weighted linearly: a 10% chance is exactly 0.1 in the equation. But humans don’t see it that way.
Prospect Theory introduces a probability weighting function, where:
Small probabilities are overweighted (a 1% chance feels larger than 0.01).
Large probabilities are underweighted (a 99% chance feels less certain than 0.99).
This explains why:
Lotteries are popular (tiny odds overweighted).
Insurance is purchased for rare events (small risks overweighted).
People hesitate with near-certainties (underweighting high probabilities).
Mathematically, Kahneman and Tversky used nonlinear functions to capture this distortion, later refined in Cumulative Prospect Theory (1992).
4.4 Loss Aversion
The most famous component of Prospect Theory is loss aversion: the idea that losses loom larger than equivalent gains.
Kahneman and Tversky estimated that losses are weighted about 2.25 times more than gains. This has become known as the 2-to-1 rule of thumb.
A €100 loss hurts roughly as much as a €225 gain pleases.
People reject fair bets unless the potential gain is at least twice the potential loss.
This asymmetry explains countless behaviors in finance and daily life—and we’ll dive into it further in Section 5.
5. Loss Aversion in Depth
Loss aversion is more than a quirky finding. It is a central principle of human decision-making. To appreciate its power, we need to examine the evidence, the neuroscience, and the real-world manifestations.
5.1 Experimental Evidence
Kahneman and Tversky (1979, 1992) ran dozens of experiments in which participants repeatedly turned down favorable gambles. For example:
Gamble Example: 50% chance to win €200, 50% chance to lose €100. Expected value = +€50. Most participants declined.
Result: People required potential gains of about 2x to 2.5x the size of potential losses before accepting gambles.
These results have been replicated in hundreds of experiments across cultures and contexts. A meta-analysis by Yechiam & Hochman (2013) confirmed the robustness of loss aversion in monetary and non-monetary domains.
5.2 The 2-to-1 Ratio
A key empirical regularity:
People are roughly twice as sensitive to losses as to gains.
This varies by context (1.5–3x), but the ratio holds remarkably well.
Practical example:
To get someone to risk losing €100, you need to offer them €200–€250 in return.
This ratio underpins marketing strategies (e.g., 2-for-1 deals, “risk-free trials”) and financial design (e.g., incentive programs framed around avoiding losses).
5.3 The Endowment Effect
The endowment effect is a direct product of loss aversion. People value items more once they own them, because giving them up feels like a loss.
Classic experiment (Kahneman, Knetsch, & Thaler, 1990):
Participants given mugs demanded about twice as much to sell them as others were willing to pay to buy them.
Interpretation: The potential loss of the mug outweighed the potential gain of money.
This has profound implications for pricing, negotiations, and asset ownership.
5.4 Neuroscience of Loss Aversion
Neuroeconomic studies reveal that loss aversion is not just psychological—it is biological.
Tom et al. (2007, Science, DOI: 10.1126/science.1134239): fMRI scans showed stronger brain activation for potential losses than equivalent gains, particularly in the amygdala and striatum.
De Martino et al. (2010): People with amygdala damage showed reduced loss aversion, suggesting this brain region mediates the emotional impact of losses.
This supports the idea that loss aversion is an evolved mechanism: avoiding threats and preserving resources was more crucial for survival than chasing incremental gains.
5.5 Cross-Cultural Variations
Is loss aversion universal? Broadly, yes—but intensity varies.
Weber et al. (1999): Found loss aversion across U.S., German, and Chinese samples.
Harinck et al. (2007): Suggested emotional intensity of losses vs. gains can vary by culture and by the domain (money vs. relationships).
Still, the core principle appears robust: humans everywhere dislike losses more than they enjoy gains.
5.6 Everyday Manifestations
Loss aversion shows up in countless decisions:
Investing: Refusal to sell losing stocks (“waiting to break even”).
Insurance: Buying overpriced coverage for small risks.
Shopping: Falling for “limited time only” deals.
Work & Career: Fear of changing jobs because of potential losses outweighing potential gains.
Relationships: Staying in bad situations to avoid the “loss” of leaving.
Understanding loss aversion helps us see patterns in our own lives that might otherwise feel like “just the way I am.”
The following quote is directly related to ourselves and our current situation.
"Leasing a private lease car instead of buying an used car, because we are completely covered when something goes wrong with the car, even though we know that over the course of the contract it will be financially more expensive"
6. Applications in Real Life
One of the reasons Prospect Theory has had such a lasting impact is that it explains behaviors we see every day—in investing, shopping, policymaking, and even health. Loss aversion is not just a lab finding; it shapes markets, consumer behavior, and government design.
6.1 Investing and the Stock Market
Perhaps nowhere is loss aversion more visible than in investing.
The Disposition Effect
Definition: Investors tend to sell assets that have gained value too early (locking in small gains) and hold onto losing assets too long (avoiding realization of losses).
Evidence: Shefrin & Statman (1985) documented this phenomenon among individual investors.
Mechanism: Selling a winner frames the outcome as a gain (pleasurable), while selling a loser frames it as a loss (painful). Thus, investors cling to losers in the hope of breaking even.
Overtrading
Barber & Odean (2000) found that individual investors often trade excessively, driven partly by the pain of realizing losses and the temptation of chasing gains. Ironically, this behavior typically reduces returns.
Market Bubbles and Crashes
Loss aversion contributes to herding behavior in markets. During bubbles, fear of missing out (FOMO) drives investors, while during crashes, the pain of losses triggers panicked selling—often at the worst possible time. This is even more visible in crypto, where the ups and downs are even more common than in the stockmarket.
6.2 Insurance and Warranties
Loss aversion explains why people willingly overpay for insurance and extended warranties.
Insurance paradox: Statistically, many policies have negative expected value (you pay more in premiums than you’ll ever claim). Yet people buy them eagerly.
Loss framing: People focus on the potential pain of losing something (house, car, health) and are willing to pay disproportionately to avoid that pain.
Warranties: Thaler (1980) showed that consumers overvalue extended warranties for appliances—even when replacement costs are low. The idea of losing a working device feels intolerable.
6.3 Consumer Marketing
Marketers have long intuited what psychologists later proved: losses drive action more powerfully than gains.
Free Trials & Money-Back Guarantees: Once you possess something, giving it up feels like a loss. Free trials exploit the endowment effect, making people reluctant to cancel.
Scarcity & Urgency: “Only 2 left in stock!” or “Offer ends tonight!” frames inaction as a potential loss.
Framing Discounts: A €10 surcharge for late payment feels worse than a €10 discount for early payment, even though the outcomes are equivalent.
The company TEMU has mastered these concepts. Every purchases feels like a win, because they make it seem like you always achieve 70% discount.
6.4 Negotiation and Policy Design
Loss aversion is a powerful tool in both bargaining and public policy.
Negotiation: Parties resist concessions framed as losses but may accept the same terms framed as “avoiding a worse outcome.”
Retirement Savings: Auto-enrollment in pension schemes works because opting out feels like a loss. Madrian & Shea (2001) showed participation rates skyrocketed under defaults.
Health Campaigns: Messages emphasizing losses (“If you don’t quit smoking, you lose years of life”) tend to be more persuasive than gain-framed messages.
6.5 Environmental and Social Policy
Loss aversion is even used in environmental messaging. For instance, framing climate change in terms of avoided losses (“Without action, sea levels will rise 1 meter”) is often more motivating than framing it as gains (“Action could preserve coastlines”).
7. Extensions: Cumulative Prospect Theory (1992)
While the 1979 version of Prospect Theory was revolutionary, it had limitations. In 1992, Kahneman and Tversky published “Advances in Prospect Theory: Cumulative Representation of Uncertainty” (Journal of Risk and Uncertainty, 5(4), 297–323. DOI: 10.1007/BF00122574).
7.1 Why an Update Was Needed
The original model had difficulties handling complex lotteries with many outcomes.
Probability weighting needed refinement—it sometimes produced inconsistencies.
7.2 Key Refinements in Cumulative Prospect Theory (CPT)
Cumulative Probability Weighting
Instead of weighting probabilities directly, CPT applies weights to cumulative probability distributions.
This solved mathematical issues with stochastic dominance.
Improved Fit to Data
CPT captured the “inverse S-shaped” weighting function more accurately:
Overweighting of small probabilities.
Underweighting of large probabilities.
Broader Applicability
CPT could handle continuous outcomes, not just simple gambles.
It became the dominant descriptive model of decision-making under risk.
7.3 Ongoing Influence
CPT remains a cornerstone of behavioral economics and is widely used in:
Behavioral finance models.
Health economics.
Insurance pricing.
Public policy design (nudges, default options).
8. Critiques & Debates
No theory is perfect. While Prospect Theory is enormously influential, it has also faced critiques from economists and psychologists.
8.1 Ambiguity of Reference Points
Problem: The theory assumes a clear reference point, but in practice, this is ambiguous.
Example: Is your reference point yesterday’s wealth, your expectations, your peers’ income, or your past maximum wealth? You reference point is something that is mostly emotional, something that often can not even be predicted until the loss of gain have been realized.
Implication: Different reference points can lead to different predictions.
8.2 Variability of Loss Aversion
Not everyone shows the same level of loss aversion.
Factors include:
Culture: Some societies show weaker loss aversion (Wang et al., 2016).
Domain: People may be more loss averse in money but not in health or time.
Context: High-stakes vs. low-stakes decisions can change the effect.
8.3 Market Efficiency Arguments
Critics from finance argue that while individuals may be biased, markets aggregate rationally.
However, evidence of bubbles, crashes, and anomalies (like the equity premium puzzle) suggest markets do not always eliminate these biases.
8.4 Competing Theories
Regret Theory (Loomes & Sugden, 1982): Suggests regret and rejoicing drive choices, not just loss aversion.
Rank-Dependent Utility: A rival model that also modifies probability weighting but without reference points.
Heuristics and Biases: Some argue that Prospect Theory still oversimplifies the variety of decision-making shortcuts.
8.5 Methodological Critiques
Some lab experiments rely on hypothetical stakes. Do they generalize to real-world, high-stakes decisions?
Replication studies often support loss aversion, but some (Gal & Rucker, 2018) argue its magnitude has been overstated in applied contexts.
9. Practical Tools & Exercises
Understanding loss aversion is one thing—counteracting it in daily life is another. Here are practical tools, exercises, and mental models that translate Prospect Theory into actionable strategies.
9.1 The “2x Rule” Worksheet
Loss aversion suggests people need potential gains to be about twice as large as potential losses before taking a risk. You can use this as a decision aid:
Write down the potential gain and loss of a choice.
Apply the “2x Rule”: Would I only accept this bet if the gain is at least double the loss?
Compare your emotional answer with the actual expected value.
Example:
Gamble: 50% chance of +€300, 50% chance of –€100.
Ratio: Gain/Loss = 3.0 → Above the 2x threshold.
Rationally AND psychologically acceptable.
This tool forces you to acknowledge the bias without ignoring it.
9.2 Reframing Journals
Loss aversion is often about framing. A reframing journal can help you shift perspective:
Instead of thinking “I lost €100 by not investing”, write “I gained €100 worth of risk protection.”
Instead of “I failed to save €200 this month”, write “I still preserved €X compared to spending everything.”
Over time, this practice reduces the emotional asymmetry of losses and gains.
9.3 Automatic Systems
Turn loss aversion into your ally. For example:
Automatic savings transfers: Once money is moved into savings, canceling it feels like a loss.
Commitment contracts: Services like Stickk.com allow you to pledge money that you lose if you fail to hit your goal.
Defaults: Enroll in programs that require opting out rather than in—quitting feels like losing.
9.4 Negotiation Strategy
In negotiations, flip the script:
Frame concessions you offer as avoiding a loss for the other party.
Frame concessions you demand as gains they could achieve.
By aligning with others’ loss aversion, you can increase cooperation.
10. Implications for Your Personal Money System
Loss aversion is not a flaw to be erased—it’s a feature of human psychology. The key is designing your money system to work with it.
10.1 Budgeting
Frame overspending as a loss of future freedom, not just a missed saving opportunity.
Use “loss alerts” in budgeting apps that warn you about what you stand to lose if you exceed a category.
Calculate with our Loss Aversion tool how much your future you will lose if you decide to spend X amount at this moment. Just a small reminder to be a consious spender.
10.2 Investing
Recognize the disposition effect in your portfolio reviews.
Use rules (e.g., sell if a stock drops 20%) to override emotional reluctance to realize losses.
Automate investing (e.g., index funds with auto-pilot) to minimize loss-driven tinkering.
10.3 Saving
Leverage automatic transfers and penalties for missed contributions.
Treat saving failures as losses of future wealth rather than missed opportunities.
10.4 Career and Life Decisions
Reframe risks like job changes: instead of focusing on what you might lose (security, familiarity), consider what you stand to lose by not acting (growth, higher income, opportunities).
10.5 Financial Coaching
If you coach clients (or yourself), integrate loss framing in guidance:
“If you don’t pay down debt, you’ll lose €X in interest every year.”
“Not increasing your retirement contribution means losing out on €Y in employer match.”
By channeling loss aversion, you align natural psychology with better outcomes.
11. Further Reading & Resources
For readers who want to explore further, here’s a curated set of foundational works, replications, and accessible resources.
11.1 Primary Research
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. DOI: 10.2307/1914185
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. DOI: 10.1007/BF00122574
Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515–518. DOI: 10.1126/science.1134239
11.2 Applied Studies
Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. Journal of Finance, 55(2), 773–806. DOI: 10.1111/0022-1082.00226
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. Journal of Finance, 40(3), 777–790. DOI: 10.1111/j.1540-6261.1985.tb05002.x
11.3 Books
Kahneman, D. (2011). Thinking, Fast and Slow.
Thaler, R. (2015). Misbehaving: The Making of Behavioral Economics.
Camerer, C. (2003). Behavioral Game Theory.
11.4 Resources
12. Conclusion
Prospect Theory marked a turning point in economics, psychology, and finance. By demonstrating that losses loom larger than gains, Kahneman and Tversky dismantled the myth of the perfectly rational agent and replaced it with a richer, more realistic model of human decision-making.
Loss aversion is not a trivial quirk. It influences how we invest, insure, shop, negotiate, and vote. It shapes marketing tactics, retirement policies, and even health campaigns. It is embedded in our biology and our evolutionary history.
But perhaps most importantly—it is manageable. By recognizing it, reframing choices, and building systems that channel loss aversion constructively, we can turn it from a liability into a strength.
As we continue this series, keep this principle in mind: your brain will always care more about avoiding losses than chasing gains. Instead of fighting that fact, design your money system to harness it.
Next week, we’ll explore Mental Accounting—how our minds create invisible “money jars” that shape spending, saving, and investing decisions, often in ways that surprise us.
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