Evolutionary game dynamics for higher-order interactions

TL;DR

This paper develops a theoretical framework to analyze how cooperation evolves in systems with higher-order interactions involving multiple individuals, revealing that such interactions lower the threshold for cooperation and favor its emergence in large systems.

Contribution

It introduces a systematic theoretical approach for understanding cooperation dynamics in higher-order interactions, extending beyond traditional pairwise models.

Findings

Higher-order interactions lower the threshold for cooperation.

They promote cooperation in large-scale systems.

Surprisingly, higher-order interactions favor cooperation where pairwise interactions do not.

Abstract

Cooperative behaviors are deeply embedded in structured biological and social systems. Networks are often employed to portray pairwise interactions among individuals, where network nodes represent individuals and links indicate who interacts with whom. However, it is increasingly recognized that many empirical interactions often involve triple or more individuals instead of the massively oversimplified lower-order pairwise interactions, highlighting the fundamental gap in understanding the evolution of collective cooperation for higher-order interactions with diverse scales of the number of individuals. Here, we develop a theoretical framework of evolutionary game dynamics for systematically analyzing how cooperation evolves and fixates under higher-order interactions. Specifically, we offer a simple condition under which cooperation is favored under arbitrary combinations of different orders of interactions. Compared to pairwise interactions, our findings suggest that higher-order interactions enable lower thresholds for the emergence of cooperation. Surprisingly, we show that higher-order interactions favor the evolution of cooperation in large-scale systems, which is the opposite for lower-order scenarios. Our results offer a new avenue for understanding the evolution of collective cooperation in empirical systems with higher-order interactions.