LEARNING, MUTATION, AND LONG-RUN EQUILIBRIA IN GAMES

被引:1063
作者
KANDORI, M
MAILATH, GJ
ROB, R
机构
[1] PRINCETON UNIV, DEPT ECON, PRINCETON, NJ 08544 USA
[2] UNIV PENN, DEPT ECON, PHILADELPHIA, PA 19104 USA
关键词
EVOLUTIONARY GAME THEORY; EVOLUTION; BOUNDED RATIONALITY; LEARNING; MARKOV CHAINS; STRICT EQUILIBRIA; RISK DOMINANCE; EQUILIBRIUM SELECTION;
D O I
10.2307/2951777
中图分类号
F [经济];
学科分类号
02 ;
摘要
We analyze an evolutionary model with a finite number of players and with noise or mutations. The expansion and contraction of strategies is linked-as usual-to their current relative success, but mutations-which perturb the system away from its deterministic evolution-are present as well. Mutations can occur in every period, so the focus is on the implications of ongoing mutations, not a one-shot mutation. The effect of these mutations is to drastically reduce the set of equilibria to what we term ''long-run equilibria.'' For 2 x 2 symmetric games with two symmetric strict Nash equilibria the equilibrium selected satisfies (for large populations) Harsanyi and Selten's (1988) criterion of risk-dominance. In particular, if both strategies have equal security levels, the Pareto dominant Nash equilibrium is selected, even though there is another strict Nash equilibrium.
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页码:29 / 56
页数:28
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