3a

Rational Actor / Game Theory: The Beauty Contest

Controls

Average Guess
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Target (p x Avg)
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Winner's Guess
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Round
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How It Works

In the Beauty Contest (p-guess game), N players each pick a number from 0-100. The winner is closest to p times the average of all guesses. Level-0 thinkers guess randomly (~50). Level-1 thinkers assume others are Level-0, so they guess p*50. Level-2 thinkers guess p*p*50, and so on. The Nash equilibrium is 0, but real people typically guess 20-35 in the first round.

In the Ultimatum Game, a proposer offers a split. The responder accepts or rejects. Rational theory says accept any positive amount, but humans reject "unfair" offers ~50% of the time.

Real-World Examples

  • Stock market pricing: Investors guess what others think stocks are worth
  • Auction bidding: Bidders reason about competitors' valuations
  • Competitive pricing: Firms set prices based on expected competitor responses
  • Salary negotiations: Each party reasons about the other's reservation price
  • Keynesian beauty contest: Original metaphor for stock market speculation
3b

Behavioral Economics Simulator

Bias Explorer

Click gambles below to reveal your implied value function:

Gambles Answered
0
Risk Seeking (Losses)
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Risk Averse (Gains)
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Lambda Implied
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How It Works

Prospect Theory (Kahneman & Tversky): People evaluate outcomes relative to a reference point. Losses hurt ~2.25x more than equivalent gains feel good. The value function is concave for gains (risk aversion) and convex for losses (risk seeking).

Hyperbolic Discounting: People prefer $100 today over $110 tomorrow, but are indifferent between $100 in 30 days and $110 in 31 days. This time inconsistency differs from rational exponential discounting.

Real-World Examples

  • Insurance purchasing: People overpay for insurance due to loss aversion
  • Investment behavior: Selling winners too early, holding losers too long
  • Retirement saving: Hyperbolic discounting causes under-saving
  • Marketing/pricing: "Don't miss out" framing exploits loss aversion
  • Status quo bias: Default options dominate (organ donation opt-in vs opt-out)
3c

Zero Intelligence Traders / Rule-Based Market

Market Controls

Last Price
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Trades
0
Bid-Ask Spread
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Efficiency
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ORDER BOOK
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How It Works

Zero Intelligence (ZI) traders submit random bids and asks. With budget constraints (ZI-C), buyers cannot bid above their value and sellers cannot ask below their cost. Remarkably, even these "mindless" traders produce realistic market outcomes: prices converge near equilibrium, and the double auction mechanism extracts nearly all available surplus.

This demonstrates that market institutions (rules and structure) can matter more than individual intelligence in producing efficient outcomes.

Real-World Examples

  • Double auction markets: NYSE, commodity exchanges
  • Stock exchanges: Market microstructure drives efficiency
  • Automated/algorithmic trading: Simple rules, complex outcomes
  • Prediction markets: Aggregating dispersed information
  • Gode & Sunder (1993): Original ZI-C paper showing institutional efficiency