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arXiv physics/0606076physics.soc-ph
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2 × 2空间博弈中的演化马尔可夫策略

Evolutionary Markovian Strategies in 2 x 2 Spatial Games

Hugo Fort, Estrella Sicardi

摘要:

进化空间2 × 2异质代理之间的游戏进行了分析,使用不同的变种元胞自动机(CA)。代理反复玩对他们最近的邻居2 × 2游戏指定的重新缩放的回报矩阵与两个参数。每个代理都由一个二进制马尔可夫策略(BMS)控制,该策略由4个条件概率[p_R,p_S,p_T,p_P]指定,这些条件概率取值为0或1。初始配置包括在2^4= 16个可能的BMS中随机分配“战略家”。然后,系统根据简单的标准规则在策略空间内演化:每个代理复制获得最高收益的邻居的策略。除了收益矩阵,主导策略和合作程度还取决于:i)邻域的类型(冯·诺依曼或摩尔); ii)合作状态的实现方式(确定性或随机性); iii)由参数k测量的噪声量。然而,一个稳健的赢家策略是[1,0,1,1]。

英文摘要:

Evolutionary spatial 2 x 2 games between heterogeneous agents are analyzed using different variants of cellular automata (CA). Agents play repeatedly against their nearest neighbors 2 x 2 games specified by a rescaled payoff matrix with two parameteres. Each agent is governed by a binary Markovian strategy (BMS) specified by 4 conditional probabilities [p_R, p_S, p_T, p_P] that take values 0 or 1. The initial configuration consists in a random assignment of "strategists" among the 2^4= 16 possible BMS. The system then evolves within strategy space according to the simple standard rule: each agent copies the strategy of the neighbor who got the highest payoff. Besides on the payoff matrix, the dominant strategy -and the degree of cooperation- depend on i) the type of the neighborhood (von Neumann or Moore); ii) the way the cooperation state is actualized (deterministically or stochastichally); and iii) the amount of noise measured by a parameter epsilon. However a robust winner strategy is [1,0,1,1].

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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