Optimal Cooperation in Evolutionary Games with Inequality and Diverse Populations

Hao Guo, Chen Shen, Rongcheng Zou, Zhen Wang, Junliang Xing
Nonlinear Sciences, Adaptation and Self-Organizing Systems, Adaptation and Self-Organizing Systems (nlin.AO)
2023-10-10 16:00:00
Cooperation is a fundamental aspect of human society, and extensive research has been devoted to understanding its emergence. However, exploring evolutionary games in asymmetric interaction scenarios has been relatively limited. This study aims to investigate asymmetric evolutionary games within communities characterized by multiple sources of inequality, encompassing unequal benefits and costs. The population comprises strong and weak communities, with the former consistently gaining greater cooperative benefits than the latter, thereby highlighting inherent inequality. The asymmetric cost manifests in labor allocation when cooperators from distinct communities interact. Our findings underscore the indispensable role played by the interplay between population composition and unequal factors. In well-mixed populations, cooperation peaks when weak players bear a larger share of costs, especially when strong communities are moderately proportioned. Contrarily, in structured populations, an optimal effect arises from the interplay between the population composition and unequal benefit even when the cost is evenly divided. With a moderate proportion of strong communities, weak defectors create barriers that shield cooperation clusters from exploitation by strong defectors. By manipulating cost division, we uncover a remarkable bi-optimal phenomenon, demonstrating that a greater cost-sharing commitment from strong or weak cooperators can trigger the highest level of cooperation.
PDF: Optimal Cooperation in Evolutionary Games with Inequality and Diverse Populations.pdf
Empowered by ChatGPT