Interactive Playground

Explore Prospect Theory

Interact with the Nobel Prize-winning framework that explains how people make decisions under uncertainty. Adjust parameters in real-time and see how they shape behavior.

Your Brain Calculates. Just Not the Way You Think.

In 1979, Kahneman and Tversky asked people to choose between bets. Simple gambles with clear odds. Then they reverse-engineered the "math" people's brains were using.

Turns out we do calculate expected value. Just not with the real numbers. We do something weirder. We distort probabilities. We feel losses more than gains. We follow patterns.

This work won the Nobel Prize in 2002. Now I'm testing if the same math explains trust.

1. Sensitivity to Outcomes

How we perceive gains and losses. Losses loom larger than gains (loss aversion), and marginal value decreases as amounts grow.

Value Function v(x)
α (alpha) • Curvature
0.88

How much you care about the amount versus simply winning or losing. Lower values = stronger diminishing sensitivity.

λ (lambda) • Loss Aversion
2.25

λ = 2.25 means a $10 loss feels like a $22.50 gain would feel good. Higher values = losses hurt more.

Key Insights

  • The curve is steeper for losses than gains (asymmetry)
  • Both sides show diminishing sensitivity
  • People are risk-averse for gains but risk-seeking for losses

2. How We Distort Probabilities

We don't perceive probabilities linearly. Small chances feel bigger than they are, while medium and high probabilities feel less certain.

Weighting Function w(p)
(Gains) γ⁺
0.61

Lower values = more distortion. Small probabilities loom larger; high probabilities feel less certain.

(Losses) δ⁻
0.69

Same concept as gains, but for losses. Typically similar to γ⁺ but empirically slightly different.

Real-World Examples

  • Lottery tickets: we overweight the tiny chance of winning millions
  • Insurance: we overweight small disaster risks
  • The diagonal line shows perfect calibration

From Risk to Trust

Prospect Theory works for coin flips and dice. But what about trusting people?

3. Test It Yourself

Same bet. Different source. Adjust the sliders to see how your perception changes.

Current Scenario

Your investment $10
If you win 60%
+$30
If you lose 40%
-$10

Quick Comparison

Trust Value +2.4
Risk Value +2.4

Same numbers. Different sources. Does it change your decision?

Trust Game

Social Context

A stranger decides. They reciprocate 60% of the time.

Subjective Value

+2.4

2.4

Yes, it is worth it to invest

Adjust Trust Parameters

Sensitivity α 0.95
Loss Aversion λ 2.70
Distortion (Gains) γ⁺ 2.01
Distortion (Losses) δ⁻ 0.51

Risk Game

Non-Social Context

A computer decides. 60% chance to win.

Subjective Value

+2.4

2.4

Yes, it is worth it to invest

Adjust Risk Parameters

Sensitivity α 0.88
Loss Aversion λ 2.25
Distortion (Gains) γ⁺ 0.61
Distortion (Losses) δ⁻ 0.69

The Point

Same bet. Different decision. Your parameters change how you see it.

  • Parameters = perception. Same numbers, different feelings.
  • High loss aversion? Even good bets feel scary.
  • Distort probabilities? Changes everything.
  • My research: Do trust and risk use different parameters?

See How We Build This

Now that you understand the theory, see how we designed the experiment to measure these parameters in the brain.

View Research Journey