> ## Documentation Index
> Fetch the complete documentation index at: https://meta.niceshare.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Prospect Theory

> Prospect Theory explains how people evaluate gains and losses asymmetrically under risk. Learn its origin, practical uses, and limits for better decisions.

<Info>
  **Category**: Models<br />
  **Type**: Behavioral Decision Model<br />
  **Origin**: Daniel Kahneman and Amos Tversky, 1979<br />
  **Also known as**: Behavioral Decision Theory, Value Function Model
</Info>

<Note>
  **Quick Answer** — Prospect Theory is a decision model showing that people judge outcomes as gains or losses relative to a reference point, not as final wealth levels. Introduced by Kahneman and Tversky in 1979, it explains why we become risk-averse in gains and risk-seeking in losses. Its core insight is that losses usually feel stronger than equivalent gains, which systematically bends real-world choices away from "rational" expected utility.
</Note>

## What is Prospect Theory?

Prospect Theory is a descriptive model of decision-making under risk that explains how people actually choose, not how perfectly rational agents should choose.

> "Losses loom larger than gains." — Daniel Kahneman and Amos Tversky

Instead of evaluating absolute outcomes, people compare options to a reference point, then overweight or underweight probabilities in predictable ways. This is why two mathematically equivalent options can trigger opposite choices when framed differently, a pattern closely related to `/effects/loss-aversion` and `/effects/decoy-effect`.

### Prospect Theory in 3 Depths

* **Beginner**: If someone offers you a sure gain of $500 or a 50% chance to gain $1,100, many people pick the sure \$500, even though the gamble's expected value is higher.
* **Practitioner**: In pricing, policy, and product design, framing outcomes as avoiding losses can change behavior more than framing them as equivalent gains.
* **Advanced**: The model combines a value function (concave in gains, convex in losses) with probability weighting, explaining risk reversals, insurance demand, and many "irrational" market patterns.

## Origin

Prospect Theory was formalized in 1979 in *Econometrica* by psychologists Daniel Kahneman and Amos Tversky, directly challenging expected utility theory as a universal account of risky choice. In 1981, their framing experiments in *Science* showed strong preference reversals: in the famous Asian disease problem, most participants preferred the sure option in gain framing (72%), but preferred the gamble in loss framing (78%).

Their later 1992 cumulative prospect theory refined probability weighting and became a core foundation for behavioral economics, alongside models such as `/models/expected-value` and `/models/regret-minimization`. Kahneman received the 2002 Nobel Memorial Prize in Economic Sciences (shared with Vernon Smith) partly for this research program.

## Key Points

Prospect Theory is most useful when you treat it as an operational checklist for decisions under uncertainty.

<Steps>
  <Step title="Reference points drive perceived value">
    People evaluate outcomes as gains or losses relative to "where they feel they are now," not relative to total wealth. Changing the reference point can change the choice even when payoffs stay constant.
  </Step>

  <Step title="Loss aversion is asymmetric">
    A loss of 100 units usually hurts more than a gain of 100 units helps. This asymmetry explains status-quo stickiness, endowment effects, and why teams delay admitting bad investments.
  </Step>

  <Step title="Probability weighting distorts risk perception">
    People tend to overweight small probabilities and underweight large probabilities. That supports both lottery buying and excessive fear of rare events.
  </Step>

  <Step title="Framing changes behavior without changing math">
    "Lives saved" versus "lives lost" framing can reverse preferences. Good decision design separates emotional framing from structural payoff analysis.
  </Step>
</Steps>

## Applications

Use Prospect Theory to redesign choices so that people can make better decisions under pressure.

<CardGroup cols={2}>
  <Card title="Pricing and Product Packaging">
    Frame annual plans as "avoiding monthly overpayment" rather than "getting a discount." Teams can test framing effects through A/B experiments before rollout.
  </Card>

  <Card title="Risk Communication in Public Policy">
    Present both gain and loss frames side by side in health or safety communication to reduce manipulation and improve informed consent.
  </Card>

  <Card title="Investment Process Design">
    Pre-commit exit rules to prevent loss-driven risk chasing. Pair this with `/models/decision-tree` to distinguish emotional escalation from rational updates.
  </Card>

  <Card title="Career and Personal Decisions">
    Reframe a possible job change from "risking what I have" to "avoiding stagnation cost." This often reveals hidden opportunity costs.
  </Card>
</CardGroup>

## Case Study

The Asian disease experiment remains the canonical case. Participants faced two equivalent policy sets for a disease expected to kill 600 people. In gain framing, 72% chose the sure option ("200 people will be saved") over a probabilistic alternative. In loss framing, preferences reversed: 78% chose the risky option over the sure loss ("400 people will die"). The arithmetic was equivalent, but framing shifted behavior dramatically.

This measurable reversal became one of the clearest demonstrations that real decisions depend on perceived gains and losses, not only on expected values. It directly influenced behavioral finance, medical decision communication, and modern experiment design in product strategy.

## Boundaries and Failure Modes

Prospect Theory is powerful but not universal. First, expertise and repeated feedback can reduce framing effects in some professional domains. Second, high-stakes institutional decisions often include governance constraints that dampen individual bias. A common misuse is treating the model as a license to manipulate users with loss framing instead of building transparent decision environments.

## Common Misconceptions

Prospect Theory is frequently simplified in ways that weaken its practical value.

<AccordionGroup>
  <Accordion title="It means people are always irrational">
    The theory does not say choices are random or foolish. It says decisions are systematic, context-sensitive, and shaped by human perception.
  </Accordion>

  <Accordion title="Loss aversion is the whole theory">
    Loss aversion is only one component. Reference dependence and probability weighting are equally important for prediction.
  </Accordion>

  <Accordion title="It replaces all classical decision models">
    In many contexts, expected value models still work well. Prospect Theory complements rather than abolishes classical tools.
  </Accordion>
</AccordionGroup>

## Related Concepts

Prospect Theory works best when combined with adjacent models for structure, probability, and execution.

<CardGroup cols={3}>
  <Card title="Expected Value" href="/models/expected-value">
    Normative baseline for comparing options under risk.
  </Card>

  <Card title="Regret Minimization" href="/models/regret-minimization">
    A practical lens for choices where emotional aftermath matters.
  </Card>

  <Card title="Nash Equilibrium" href="/models/nash-equilibrium">
    Game-theoretic complement when outcomes depend on others' choices.
  </Card>

  <Card title="Loss Aversion" href="/effects/loss-aversion">
    Core behavioral mechanism inside Prospect Theory.
  </Card>
</CardGroup>

## One-Line Takeaway

<Tip>
  When stakes feel high, check your reference point and framing first; your choice may be reacting to perceived loss, not true value.
</Tip>