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Effects of group interactions on the network Parrondo’s games
Physica A: Statistical Mechanics and its Applications ( IF 3.3 ) Pub Date : 2021-07-23 , DOI: 10.1016/j.physa.2021.126271
Ye Ye 1 , Xin-shi Zhang 1 , Lin Liu 2 , Neng-Gang Xie 2
Affiliation  

A minimalistic multi-agent Parrondo’s game structure with network evolution (Game A) and branching dependent on the number of wins and losses of neighbors (Game B) was previously introduced, indicating that Parrondo’s paradox occurs, in which a losing strategy and a neutral strategy combine to yield a winning one. Using a similar Game B’s structure and introducing a new Game A’s structure with competition and cooperation behaviors, we further analyze the influences of network evolution, cooperation and competition behaviors as different group interactions on the network Parrondo’s games. Based on the multi-agent Parrondo’s game, the discrete Markov chain method is used. Theoretical analysis reveals that losing configurations of Game B, when stochastically mixed with neutral Game A with competition and cooperation behaviors, can result in paradoxical winning scenarios like network evolution and can even produce larger parameter space. Simulation results indicate that under different network topology structures stochastically mixing Game A with different group interactions and Game B can produce different enhanced winning outcomes, despite Game B being individually losing. The underlying paradoxical mechanisms where the ratcheting mechanism of Game B and the agitating mechanism of Game A with different group interactions are analyzed. It is also elucidated that agitation from Game A with different group interactions improves the capital exchange between individuals.



中文翻译:

小组互动对网络 Parrondo 游戏的影响

之前介绍了一种简约的多智能体 Parrondo 博弈结构,其具有网络演化(博弈 A)和依赖于邻居胜负数的分支(博弈 B),表明出现了 Parrondo 悖论,其中有失败策略和中立策略结合产生一个获胜的。使用相似的博弈B的结构,并引入一个新的具有竞争和合作行为的博弈A的结构,我们进一步分析了网络演化、合作和竞争行为作为不同群体互动对网络Parrondo博弈的影响。在多智能体 Parrondo 博弈的基础上,采用离散马尔可夫链方法。理论分析表明,当游戏 B 的输配置与具有竞争和合作行为的中性游戏 A 随机混合时,可能会导致网络进化等矛盾的获胜场景,甚至会产生更大的参数空间。仿真结果表明,在不同的网络拓扑结构下,将游戏 A 与不同的组交互和游戏 B 随机混合可以产生不同的增强获胜结果,尽管游戏 B 是单独失败的。分析了游戏 B 的棘轮机制和游戏 A 的激动机制在不同群体互动下的潜在悖论机制。还阐明了具有不同群体互动的游戏 A 的激动改善了个人之间的资本交换。仿真结果表明,在不同的网络拓扑结构下,将游戏 A 与不同的组交互和游戏 B 随机混合可以产生不同的增强获胜结果,尽管游戏 B 是单独失败的。分析了游戏 B 的棘轮机制和游戏 A 的激动机制在不同群体互动下的潜在悖论机制。还阐明了具有不同群体互动的游戏 A 的激动改善了个人之间的资本交换。仿真结果表明,在不同的网络拓扑结构下,将游戏 A 与不同的组交互和游戏 B 随机混合可以产生不同的增强获胜结果,尽管游戏 B 是单独失败的。分析了游戏 B 的棘轮机制和游戏 A 的激动机制在不同群体互动下的潜在悖论机制。还阐明了具有不同群体互动的游戏 A 的激动改善了个人之间的资本交换。

更新日期:2021-07-30
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