Prospect Theory: What It Is and How It Works, With Examples

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Prospect Theory: What it Is and How It Works

Prospect theory, developed by Amos Tversky and Daniel Kahneman, describes how people actually make choices under risk. It departs from classical expected-utility models by showing that individuals evaluate gains and losses relative to a reference point and that losses typically loom larger than equivalent gains—a phenomenon known as loss aversion.

Key takeaways

  • People evaluate outcomes relative to a reference point, not in absolute terms.
  • Losses have a greater psychological impact than equal-sized gains (loss aversion).
  • People are generally risk-averse for gains and risk-seeking for losses.
  • Cognitive shortcuts and framing strongly influence decisions under uncertainty.

Core concepts

  • Reference dependence: Outcomes are judged as gains or losses relative to a reference point (often the status quo), not by final wealth alone.
  • Loss aversion: A loss hurts more than an equal gain pleases. This skews choices toward avoiding losses.
  • Diminishing sensitivity: The subjective difference between $100 and $200 feels larger than between $1,100 and $1,200—implying a value function that is concave for gains and convex for losses.
  • Probability weighting: People tend to overweight small probabilities and underweight moderate-to-high probabilities, distorting risk assessments.

How prospect theory works

Prospect theory models decision-making in two main phases:

  1. Editing phase
  2. Individuals simplify and frame options, select a reference point, and ignore unlikely outcomes.

  3. This phase determines which aspects of choices enter the subsequent evaluation and can introduce framing biases.

  4. Evaluation phase

  5. People assign subjective values and weights to outcomes and probabilities, then choose the option with the highest perceived overall value.
  6. Decisions often reflect loss aversion and distorted probability perceptions rather than purely rational calculations.

Example illustrating reference framing:
– Option A: Receive $25 now.
– Option B: Receive $50 then give back $25.
Both yield $25 in net terms, but people typically prefer Option A because the straightforward gain avoids framing a part of the outcome as a loss.

Behavioral patterns and named effects

  • Certainty effect: People overweight certain outcomes relative to probable ones, preferring sure gains and avoiding sure losses.
  • Isolation effect (or cancellation): When two options share common components, people often ignore identical parts and focus on differences, which makes framing powerful.
  • Risk attitude reversal: The same decision can elicit risk aversion when described as a gain and risk-seeking when described as a loss.

Practical implications for investors and decisions

  • Investors may hold losers too long (to avoid realizing losses) and sell winners too soon (to lock in gains), driven by loss aversion and mental accounting.
  • Framing performance or outcomes in terms of gains versus losses can change choices—even when the underlying economics are identical.
  • Low-probability but high-impact events (black swans) are often underweighted in routine decision making, yet overweighted when described vividly.

How to reduce bias from prospect theory

  • Reframe problems in neutral terms (focus on final outcomes rather than gains/losses).
  • Compute expected values and stress-test decisions with scenarios that include low-probability events.
  • Use checklists, written decision rules, or precommitment strategies to limit emotional reactions.
  • Seek outside perspectives or quantitative models to counteract intuitive distortions.

Example

Two financial advisors present the same mutual fund differently:
– Advisor A: “This fund averaged 10% over the last three years.”
– Advisor B: “This fund’s returns have declined versus the prior decade, and the last three years are lower.”
Even though the underlying fund is the same, many people prefer Advisor A’s framing because it emphasizes gains rather than highlighting losses or decline.

FAQs (brief)

  • What does prospect theory explain?
    How people actually choose under risk—highlighting reference dependence, loss aversion, and probability weighting.
  • Why does it matter to me?
    It reveals predictable decision errors (framing, holding on to losers, underweighting rare risks) and suggests ways to improve choices.
  • Main components?
    Reference dependence, value function (loss aversion, diminishing sensitivity), and probability weighting.

Conclusion

Prospect theory provides a realistic account of decision-making under uncertainty. By recognizing reference dependence and loss aversion, individuals and investors can better understand common biases and apply practical steps—reframing, quantitative checks, and decision rules—to improve outcomes.

Key studies

  • Tversky, A. & Kahneman, D. (1979). Prospect Theory: An Analysis of Decision under Risk.
  • Tversky, A. & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty.