Recently, the evolution of people cooperation in the context of evolutionary games has attracted noticeable attention. In this regard, people’s assessment of their neighbor’s payoff affects the cooperation or defection decisions. Different decisions of each person can be a function of environment (e.g. cultural, climatic, and physical conditions). Thus, the individuals’ decisions and their values can change under the influence of these factors. In the present study, the effect of cultural, climatic, and physical conditions is considered as a chaotic dynamic (here Lorenz system). Using different initial conditions, the Lorenz time-series is unique to each individual in calculating her/his payoff. In other words, society members are considered as agents or nodes within a complex network. Here, the mechanism of applying Lorenz dynamics and external force in the context of the iterated prisoner’s dilemma game is applied. Using large-scale Monte Carlo simulations, we can see that the cooperation of the whole network is significantly improved even in the presence of a small external force. It is concluded that applying a chaotic dynamic on valuations and coupling between agents is an effective way to increase network cooperation, even in severely tempted situations of the evolutionary game. It is stated that the impact of cultural, climatic, educational conditions and the coupling between agents in the cooperation of social networks, can be modeled by the help of chaotic signals.

近来，进化博弈背景下的人类合作进化引起了广泛关注。在这方面，人们对邻居回报的评估会影响合作或叛逃的决定。每个人的不同决定可能是环境的功能（例如文化、气候和物理条件）。因此，个人的决定和他们的价值观会在这些因素的影响下发生变化。在本研究中，文化、气候和物理条件的影响被认为是一种混沌动态（这里是洛伦兹系统）。使用不同的初始条件，洛伦兹时间序列对于每个人计算她/他的收益是唯一的。换句话说，社会成员被视为复杂网络中的代理或节点。这里，应用洛伦兹动力学和外力在迭代囚徒困境博弈中的机制。使用大规模的蒙特卡罗模拟，我们可以看到，即使存在很小的外力，整个网络的协作性也得到了显着提高。得出的结论是，即使在进化博弈的严重诱惑情况下，对代理之间的估值和耦合应用混沌动态也是增加网络合作的有效方法。据称，文化、气候、教育条件的影响以及社会网络合作中代理之间的耦合，可以通过混沌信号的帮助进行建模。我们可以看到，即使在很小的外力存在的情况下，整个网络的协作性也得到了显着的提高。得出的结论是，即使在进化博弈的严重诱惑情况下，对代理之间的估值和耦合应用混沌动态也是增加网络合作的有效方法。据称，文化、气候、教育条件的影响以及社会网络合作中代理之间的耦合，可以通过混沌信号的帮助进行建模。我们可以看到，即使在很小的外力存在的情况下，整个网络的协作性也得到了显着提高。得出的结论是，即使在进化博弈的严重诱惑情况下，对代理之间的估值和耦合应用混沌动态也是增加网络合作的有效方法。据称，文化、气候、教育条件的影响以及社会网络合作中代理之间的耦合，可以通过混沌信号的帮助进行建模。