Abstract

We present a computational model which mainly concentrates on the effect of adjusting the signal strength of game from an agent to its a neighbor by considering the information quantity in individual’s database to study the evolutionary prisoner’s dilemma game on directed-weighted square lattices. In this model, each agent considers the current and historical payoff both obtained from the same neighbor, and regulates the strength of sending signal according to these information. More specifically, for paired agents x and y in network, x will increase the signal strength from it to y in case of it obtains current income higher than the historical payoff; conversely, x will reduce the signal strength because its current income less than the historical one. The simulation results show that this evolutionary rule not only can help cooperators get out of danger of extinction, but also can hugely boost the cooperation in population. Interestingly, for a fixed cost–benefit ratio r (\(0<r<0.246\)), there exists the minimal information quantity, resulting in the higher cooperation level (even can reach the status of full cooperation) due to the positive feedback effect in system. Besides, by a modified pair, approximation method qualitatively verifies the role of adjusting signal strength according to the information quantity on cooperation. We also explore the reason of emergence and persistence of cooperation by a few representative snapshots in system. Our results may enhance the understanding of evolutionary dynamics with stochastic interaction in graph-structured populations.

中文翻译：

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基于信息量的网络化调整信号强度的协同动力学

摘要

我们提出一个计算模型，主要集中在通过考虑个人数据库中的信息量来研究从代理人到邻居的游戏信号强度的影响，以研究有向加权方格上的进化囚徒困境游戏。在该模型中，每个代理都考虑从同一个邻居获得的当前和历史收益，并根据这些信息调节发送信号的强度。更具体地说，对于网络中成对的座席x和y，如果x获得的当前收入高于历史收益，x会将信号强度从y增加到y。相反，x会降低信号强度，因为其当前收入低于历史收入。仿真结果表明，这种进化规律不仅可以帮助合作者摆脱灭绝的危险，而且可以极大地促进人口合作。有趣的是，对于固定的成本效益比r（\（0 <r <0.246 \）），由于系统中的正反馈效应，信息量极小，导致更高的合作水平（甚至可以达到完全合作的状态）。此外，通过修改对，近似方法定性地验证了根据协作信息量调整信号强度的作用。我们还通过系统中的一些代表性快照来探讨出现和持续合作的原因。我们的结果可能会增强对图结构人口中随机相互作用的进化动力学的理解。