Finite automata play repeated prisoner's dilemma with information processing costs

被引:23
作者
Ho, TH
机构
[1] Anderson Grad. School of Management, Univ. of California at Los Angeles, Los Angeles
关键词
prisoner's dilemma; genetic algorithm; learning; computational complexity;
D O I
10.1016/0165-1889(94)00848-1
中图分类号
F [经济];
学科分类号
02 ;
摘要
We study the finitely repeated Prisoner's Dilemma game. Our players are modeled as finite automata. A population of boundedly rational players compete in a 'survival of the fittest' evolution contest simulated using Holland's genetic algorithm. Starting from a hostile population which plays defection frequently, our simulation results show that players converge to play cooperatively. This emergence of cooperative behavior breaks down when we penalize a complex strategy based on the size of the machine. On the other hand, a penalty cost that increases with the frequency an automaton switches states will not hurt the development of cooperative behavior.
引用
收藏
页码:173 / 207
页数:35
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