Evolutionary dynamics on sequential temporal networks
Anzhi Sheng, Aming Li, Long Wang

TL;DR
This paper develops a theory for evolutionary dynamics on growing, time-varying networks, showing that sequential temporal networks can promote cooperation even when static networks do not.
Contribution
It introduces a new framework for analyzing evolution on sequential temporal networks and derives conditions where they enhance cooperation compared to static networks.
Findings
Sequential temporal networks can promote cooperation more effectively than static networks.
Even disfavoring cooperation on static networks, temporal networks can rescue cooperative behavior.
Empirical datasets confirm the promotion of cooperation on evolving networks.
Abstract
It is well-known that population structure is a catalyst for the evolution of cooperation since individuals can reciprocate with their neighbors through local interactions defined by network structures. Previous research typically relies on the assumption that population size is fixed and the structure is time-invariant, which is represented by a static network. However, real-world populations are often evolving with the successive growth of nodes and links in time, resulting in time-varying population structures. Here we model such growing networked populations by sequential temporal networks with an increasing number of nodes and edges and develop the theory of evolutionary dynamics on sequential temporal networks. We derive explicit conditions under which sequential temporal networks promote the evolution of cooperation relative to their static counterparts. In particular, even if…
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Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Evolution and Genetic Dynamics · Evolutionary Psychology and Human Behavior
