Evolutionary dynamics of cooperation in neutral populations

TL;DR

This paper explores how cooperation evolves in multiple interacting populations using spatial prisoner's dilemma models, revealing complex dynamics like cyclic dominance and conditions favoring cooperation's stability.

Contribution

It introduces a novel multi-population framework showing how simultaneous populations lead to complex spatiotemporal patterns and enhance understanding of cooperation stability.

Findings

Spontaneous emergence of cyclic dominance among populations.

Counterintuitive faster extinction of defectors with higher temptation.

Cooperators eventually dominate when social differences are significant.

Abstract

Cooperation is a difficult proposition in the face of Darwinian selection. Those that defect have an evolutionary advantage over cooperators who should therefore die out. However, spatial structure enables cooperators to survive through the formation of homogeneous clusters, which is the hallmark of network reciprocity. Here we go beyond this traditional setup and study the spatiotemporal dynamics of cooperation in a population of populations. We use the prisoner's dilemma game as the mathematical model and show that considering several populations simultaneously give rise to fascinating spatiotemporal dynamics and pattern formation. Even the simplest assumption that strategies between different populations are payoff-neutral with one another results in the spontaneous emergence of cyclic dominance, where defectors of one population become prey of cooperators in the other population, and vice versa. Moreover, if social interactions within different populations are characterized by significantly different temptations to defect, we observe that defectors in the population with the largest temptation counterintuitively vanish the fastest, while cooperators that hang on eventually take over the whole available space. Our results reveal that considering the simultaneous presence of different populations significantly expands the complexity of evolutionary dynamics in structured populations, and it allow us to understand the stability of cooperation under adverse conditions that could never be bridged by network reciprocity alone.