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Perceptron versus automaton in the finitely repeated prisoner's dilemma

机译:有限重复囚徒困境中的感知器与自动机

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We study the finitely repeated prisoner's dilemma in which the players are restricted to choosing strategies which are implementable by a machine with a bound on its complexity. One player has to use a finite automaton while the other player has to use a finite perceptron. Some examples illustrate that the sets of strategies which are induced by these two types of machines are different and not ordered by set inclusion. Repeated game payoffs are evaluated according to the limit of means. The main result establishes that a cooperation at almost all stages of the game is an equilibrium outcome if the complexity of the machines the players may use is limited enough and if the length T of the repeated game is sufficiently large. This result persists when more than T states are allowed in the player's automaton. We further consider a variant of the model in which the two players are restricted to choosing strategies which are implementable by perceptrons and prove that the players can cooperate at most of the stages provided that the complexity of their perceptrons is sufficiently reduced.
机译:我们研究了有限重复犯人的困境,在这种困境中,玩家只能选择可由机器执行的策略,而机器的复杂性受到限制。一个玩家必须使用有限的自动机,而另一个玩家必须使用有限的感知器。一些示例说明,这两种类型的机器所诱发的策略集是不同的,并且没有按集合包含来排序。重复的游戏收益根据均值限制进行评估。主要结果表明,如果玩家可以使用的机器的复杂性受到足够的限制,并且重复游戏的长度T足够大,则在游戏的几乎所有阶段的合作都是均衡的结果。当玩家的自动机中允许超过T个状态时,此结果仍然存在。我们进一步考虑该模型的一种变体,其中两个参与者只能选择感知器可以实现的策略,并证明只要足够降低感知器的复杂性,参与者就可以在大多数阶段进行合作。

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