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Behavioral Game Theory: A Brief Introduction Networked Life CSE 112 Spring 2005 Prof. Michael Behavioral Game Theory: A Brief Introduction Networked Life CSE 112 Spring 2005 Prof. Michael Kearns Supplementary slides courtesy of Colin Camerer, Cal. Tech

Behavioral Game Theory and Game Practice • Game theory: how rational individuals should behave Behavioral Game Theory and Game Practice • Game theory: how rational individuals should behave • Who are these rational individuals? • BGT: looks at how people actually behave – experiment by setting up real economic situations – account for people’s economic decisions – don’t break game theory when it works • Fit a model to observations, not “rationality”

Feeling in ultimatum games: How much do you offer out of $10? n n Feeling in ultimatum games: How much do you offer out of $10? n n n Proposer has $10 Offers x to Responder (keeps $10 -x) What should the Responder do? Self-interest: Take any x>0 n Empirical: Reject x=$2 half the time n

How People Ultimatum-Bargain Thousands of games have been played in experiments… • • • How People Ultimatum-Bargain Thousands of games have been played in experiments… • • • In different cultures around the world With different stakes With different mixes of men and women By students of different majors Etc. etc. Pretty much always, two things prove true: 1. 2. Player 1 offers close to, but less than, half (40% or so) Player 2 rejects low offers (20% or less)

Ultimatum offer experimental sites Ultimatum offer experimental sites

Ultimatum Bargaining across Cultures Sharing norms differ in the industrialized world Japan, Israel lowest Ultimatum Bargaining across Cultures Sharing norms differ in the industrialized world Japan, Israel lowest (Roth et al. 1991) Machiguenga farmers in Peru (Henrich 2000) Offered 26% on average, accepted all but 1 offer Very socially disconnected Ache in Paraguay, Lamelara in Indonesia Made hyperfair (more than 50%) offers Headhunters (potlatch culture), whalers

The Machiguenga independent families cash cropping slash & burn gathered foods fishing hunting The Machiguenga independent families cash cropping slash & burn gathered foods fishing hunting

Fair offers correlate with market integration (top), cooperativeness in everyday life (bottom) Fair offers correlate with market integration (top), cooperativeness in everyday life (bottom)

Ultimatum offers across societies (mean shaded, mode is largest circle…) Ultimatum offers across societies (mean shaded, mode is largest circle…)

Ultimatum Bargaining across Majors Economics majors offer 7% less, accept 7% less (Carter and Ultimatum Bargaining across Majors Economics majors offer 7% less, accept 7% less (Carter and Irons 1991) They must have learned game theory! … but this behavior is consistent across years of study (freshman to seniors) … maybe their game-theoretic nature made them want to study economics? Other studies show no correlation, or that econ/business students offer more.

Ultimatum Bargaining and Looks 70 University of Miami students, photographed and rated for attractiveness Ultimatum Bargaining and Looks 70 University of Miami students, photographed and rated for attractiveness (Schweitzer and Solnick 1999) Man as player 1, attractive woman as player 2… Doesn’t make much difference Woman as player 1, attractive man as player 2… Average offer is 50. 7% (hyperfair!) Small percentage (1 or 2? ) offer almost everything

Stakes, Entitlement, Framing Indonesia: from a day’s wages to a month’s wages No difference… Stakes, Entitlement, Framing Indonesia: from a day’s wages to a month’s wages No difference… Florida: answer questions to get $400 pie instead of $20 More low offers at $400 … but subjects earned it Framing it as a buyer/seller exchange lowers offers 10% Framing it as a resource competition raises them slightly (Hoffman et al. 1994)

Ultimatum offers of children who failed/passed false belief test Ultimatum offers of children who failed/passed false belief test

Subject (autistic? ) complaining postexperiment (Zamir, 2000) Subject (autistic? ) complaining postexperiment (Zamir, 2000)

Feeling: This is your brain on unfairness (Sanfey et al, Sci 13 March ’ Feeling: This is your brain on unfairness (Sanfey et al, Sci 13 March ’ 03)

1. Limited equilibration Beauty contest game n N players choose numbers xi in [0, 1. Limited equilibration Beauty contest game n N players choose numbers xi in [0, 100] n Compute target (2/3)*( xi /N) n Closest to target wins $20

Beauty Contest Some number of players try to guess a number that is 2/3 Beauty Contest Some number of players try to guess a number that is 2/3 of the average guess. The answer can’t be between 68 and 100 - no use guessing in that interval. It is dominated. But if no one guesses in that interval, the answer won’t be greater than 44. But if no one guesses more than 44, the answer won’t be greater than 29… Everyone should guess 0! And good game theorists would… But they’d lose…

Iterated Dominance People don’t instantly compute all the way to 0 The median subject Iterated Dominance People don’t instantly compute all the way to 0 The median subject uses 1 or 2 rounds of iteration (25, 35) Guessing 0 on the first round (game theorist) is poor Guessing 30 (behavioral game theory) is much better But 30 isn’t a good guess the seventh time you play…

A New Theory… We could create new per-game theories… But this would be useless. A New Theory… We could create new per-game theories… But this would be useless. We could consider these as repeated games of some sort… But that complicates a lot of things. Maybe we can make a small change to something underlying… What if people don’t only care about their own payoffs?

A New Theory of Utility Consider that people still like their payoffs They also A New Theory of Utility Consider that people still like their payoffs They also dislike others having more money, with some coefficient . And they dislike having more money than others, with coefficient . U_1 is player 1’s utility; P_1 & P_2 are the players’ payoffs. U_1 = P_1 - (max[P_2 - P_1, 0]) - (max[P_1 - P_2, 0]) is “envy” is “guilt” 0 <= < 1 < Different players can have different and

Inequality Aversion U_1 = P_1 - _1(max[P_2 - P_1, 0]) - _1(max[P_1 - P_2, Inequality Aversion U_1 = P_1 - _1(max[P_2 - P_1, 0]) - _1(max[P_1 - P_2, 0]) (Fehr and Schmidt 1999) Now, we can do classical game theory, but with U, not P Player 2 should reject any offer < _2/(1 + 2 _2) If = 1/3, player 2 should reject any offer less than 20% Player 1 offers will depend on Estimates of player 2 envy ( _2) distribution and Player 1 guilt ( _1)

Inequality Aversion: Advantages • • Model generalizes easily to more than 2 players = Inequality Aversion: Advantages • • Model generalizes easily to more than 2 players = 1/3, = 0 can explain a lot! • Ultimatum bargaining • Multi-player ultimatum bargaining (“Market game”) • Even dictator games Parameters can be tuned for cultures or individuals Does not break most of the existing, correct predictions of non-IA game theory

Inequality Aversion on Graphs For games where IA game theory works, we could put Inequality Aversion on Graphs For games where IA game theory works, we could put these games on graphs. Do players care about global inequalities or neighborhood inequalities? Our guesses may agree, but it’s an open question: no experiment has been done!