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时间尺度与选择倾向性协同作用下的演化博弈模型 被引量:2

Evolutionary game model under synergistic effect of time scale and selection preference
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摘要 针对合作行为的涌现与维持问题,基于演化博弈理论和网络理论,提出了一种促进合作的演化博弈模型。该模型同时将时间尺度、选择倾向性引入到演化博弈中。在初始化阶段,根据持有策略的时间尺度将个体分为两种类型:一种个体在每个时间步都进行策略更新;另一种个体在每一轮博弈后,以某种概率来决定是否进行策略更新。在策略更新阶段,模型用个体对周围邻居的贡献来表征他的声誉,并假设参与博弈的个体倾向于学习具有较好声誉邻居的策略。仿真实验结果表明,所提出的时间尺度与选择倾向性协同作用下的演化博弈模型中,合作行为能够在群体中维持;惰性个体的存在不利于合作的涌现,但是个体的非理性行为反而能够促进合作。 Considering emergence and maintenance of cooperative behavior, based on evolutionary game theory and network theory, an evolutionary game model which can promote cooperation was proposed. In the proposed model, time scale and selection preference were introduced simultaneously into evolutionary game. In initialization phase, players were segmented into two categories according to their time scales of the strategies. Players in one category updated their strategies in each round, while players in the other category determined wether to update their strategies according to certain probability after every round of game. In strategy updating phase, the reputation of a player was determined by his distribution to his neighbors, and all players perfered to learn the strategies of neighbors with good reputation. The simulation experimental results show that, in the proposed evolutionary game model under synergistic effect of time scale and selection preference, cooperative behavior can be maintained in the group, the players with inertia hinders the emergence of cooperation, but the irrational behavior of players can promote cooperation.
作者 王西龙 王继成 罗成 田秀霞 WANG Xilong;WANG Jicheng;LUO Cheng;TIAN Xiuxia(College of Electronics and Information Engineering,Tongji University,Shanghai 200092,China;College of Computer Science and Technology,Shanghai University of Electric Power,Shanghai 200090,China)
出处 《计算机应用》 CSCD 北大核心 2019年第6期1824-1828,共5页 journal of Computer Applications
基金 国家自然科学基金资助项目(61772327,61532021) 上海市科委地方能力建设项目(15110500700)~~
关键词 演化博弈 时间尺度 选择倾向性 囚徒困境 复杂网络 evolutionary game time scale selection preference prisoner’s dilemma complex network
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