Key Takeaways
- People feel losses more intensely than gains.
- Decisions depend on reference points, not just outcomes.
- Small probabilities are often overweighted in choices.
- Risk aversion for gains; risk seeking for losses.
What is Prospect Theory: What It Is and How It Works, With Examples?
Prospect theory is a behavioral economics model developed by Daniel Kahneman and Amos Tversky that explains how people make decisions under risk and uncertainty. Unlike traditional models relying on objective probability, it highlights how individuals value gains and losses relative to a reference point rather than absolute outcomes.
This theory reveals that people are generally more sensitive to potential losses than equivalent gains, influencing choices in finance, insurance, and everyday decisions.
Key Characteristics
Prospect theory centers on several key features that shape decision-making under risk:
- Loss Aversion: Losses impact you roughly twice as much as gains of the same size, driving risk-averse behavior when facing gains and risk-seeking behavior when facing losses.
- Reference Dependence: Outcomes are evaluated relative to a neutral reference point, such as your current financial position, not just their absolute value.
- Value Function: The S-shaped curve is concave for gains, leading to risk aversion, and convex for losses, encouraging risk seeking.
- Probability Weighting: People tend to overweight small probabilities and underweight moderate to high probabilities, distorting real risk assessments.
- Editing and Evaluation Phases: Decisions involve simplifying prospects before weighing outcomes and probabilities to select the most valued option.
How It Works
When you face a risky choice, prospect theory suggests you first frame the decision relative to a reference point, categorizing outcomes as gains or losses. Then you apply a value function that reflects your sensitivity to these gains and losses, combined with a probability weighting function that adjusts how you perceive the likelihood of outcomes.
This process often leads to preferences that deviate from expected utility theory, as you might reject a fair gamble that offers a gain but accept a risky bet to avoid a loss. Understanding this helps explain behaviors like why people buy insurance yet also engage in high-risk investments.
Examples and Use Cases
Prospect theory explains many real-world financial behaviors and decision patterns:
- Airlines: Companies like Delta often price options and refunds taking into account customers’ loss aversion, influencing ticket sales and cancellation policies.
- Investment Choices: Investors balancing portfolios with dividend stocks may prefer steady gains over volatile growth stocks, aligning with risk aversion for gains.
- Risk Management: Understanding how investors overweight small probabilities helps explain why some buy lottery tickets or gamble despite negative expected returns, contrasting with cautious moves towards low-cost index funds.
- Behavioral Biases: The tendency to hold onto losing positions longer than winners, known as the disposition effect, stems from loss aversion and reference dependence.
Important Considerations
While prospect theory provides a powerful framework for understanding decision-making under risk, it requires careful consideration of your personal reference points, which can shift with context. This variability means the theory describes behavior "as if" you use these cognitive shortcuts rather than calculating exact probabilities or values.
Applying prospect theory insights can improve financial planning and risk assessment, but combining it with traditional models and awareness of biases will guide better decisions. Exploring how concepts like random variables and tail risk affect your choices can deepen your understanding of uncertainty in investments.
Final Words
Prospect theory reveals that your decisions are influenced more by perceived gains and losses than by actual outcomes, highlighting the impact of loss aversion and probability weighting. To apply this insight, review your risk choices by framing potential outcomes relative to your current situation rather than just their absolute values.
Frequently Asked Questions
Prospect Theory, developed by Kahneman and Tversky in 1979, explains how people make decisions under risk by focusing on perceived gains and losses relative to a reference point. It challenges traditional economic theories by showing people are loss averse and evaluate outcomes differently based on framing.
Loss aversion means people feel the pain of losses about twice as strongly as the pleasure of equivalent gains. This causes individuals to often avoid losses more aggressively than they pursue gains, influencing risk-taking behavior in both financial and everyday decisions.
The reference point is the baseline or neutral position from which people judge gains and losses. People don't evaluate outcomes based on absolute values but rather in comparison to this reference, which can change depending on context and affect their choices.
The value function is S-shaped: concave for gains, indicating risk aversion, and convex for losses, indicating risk seeking. For example, people prefer a sure gain of $450 over a 50% chance of $1,000, but prefer a gamble with a 50% chance to lose $1,100 over a sure loss of $500.
This function describes how people perceive probabilities inaccurately, overweighting small chances and underweighting moderate to high ones. For instance, they might treat a 1% chance like it's 5-10%, distorting their risk assessment.
Expected Utility Theory assumes people make rational choices by maximizing expected value using linear utility and objective probabilities. Prospect Theory shows that people often deviate from this rational model due to loss aversion and probability distortion, explaining real-world behaviors better.
People buying insurance illustrate risk aversion in gains, while gambling to avoid a certain loss shows risk seeking in losses. Investors holding losing stocks longer than winning ones also reflect the theory’s insights about loss aversion and reference dependence.
It’s applied in marketing, finance, and policy-making to frame choices and influence behavior, such as using loss framing to nudge decisions. Limitations include dependence on varying reference points and not fully explaining all decision-making contexts.
