Strategy evolution on dynamic networks

Abstract

Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. But most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here, we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures.

45 pages; final version

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• Nonlinear social evolution and the emergence of collective action
• The coalescent in finite populations with arbitrary, fixed structure
• Evolution of cooperation and competition in multilayer networks
• Evolution of trust in structured populations
• Fixation probability in evolutionary dynamics on switching temporal networks
• Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems
• A Parrondo paradox in susceptible-infectious-susceptible dynamics over periodic temporal networks
• Sensitivity-Driven Migration and the Evolution of Cooperation in Multi-Player Games on Structured Populations
• Evolutionary dynamics of collective decision-making with local social influence on static and dynamic networks