Understanding the emergence and sustainability of cooperation is a fundamental problem in evolutionary biology and is frequently studied by the framework of evolutionary game theory. A very powerful mechanism to promote cooperation is network reciprocity, where the interaction patterns and opportunities for strategy spread of agents are constrained to limited sets of permanent interactions partners. Cooperation survives because it is possible for close-knit communities of cooperation to be shielded from invasion by defectors. Here we show that parameter ranges in which cooperation can survive are strongly expanded if game play on networks is skewed towards more frequent interactions with more successful neighbours. In particular, if agents exclusively select neighbors for game play that are more successful than themselves, cooperation can even dominate in situations in which it would die out if interaction neighbours were chosen without a bias or with a preference for less successful opponents. We demonstrate that the "selecting fitter neighbours" strategy is evolutionarily stable. Moreover, it will emerge as the dominant strategy out of an initially random population of agents.
Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
Department of MicroData Analytics(微数据分析系);BEACON Center for the Study of Evolution in Action(行为进化研究中心);Department of Microbiology, Genetics, and Immunology(微生物学、遗传学与免疫学系);Program in Ecology, Evolution, and Behavior(生态、进化与行为项目);Department of Physics and Astronomy(物理与天文学系)
Social welfare optimisation under institutional reward and punishment
Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam(胡志明市技术大学计算机科学与工程学院,越南);Vietnam National University - Ho Chi Minh City (VNU-HCM), Vietnam(越南胡志明市国家大学(VNU-HCM),越南);School of Computing, Engineering and Digital Technologies, Teesside University, United Kingdom(泰赛德大学计算、工程与数字技术学院,英国);School of Mathematics, University of Birmingham, Birmingham, United Kingdom(伯明翰大学数学学院,英国)