Network topology and movement cost, not updating mechanism, determine the evolution of cooperation in mobile structured populations

• Published in: PloS one Vol. 18; no. 8; p. e0289366
• Main Authors: Pires, Diogo L, Erovenko, Igor V, Broom, Mark
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-08-2023
• Subjects: Analysis; Behavior evolution; Biology and Life Sciences; Collective behavior; Completeness; Computer and Information Sciences; Cooperation; Economic aspects; Evaluation; Evolution; Games; Graph theory; Network architecture; Network topologies; Physical Sciences; Political asylum; Population structure; Populations; Social Sciences; Stochasticity; Topology; Viscosity; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0289366

Evolutionary models are used to study the self-organisation of collective action, often incorporating population structure due to its ubiquitous presence and long-known impact on emerging phenomena. We investigate the evolution of multiplayer cooperation in mobile structured populations, where individuals move strategically on networks and interact with those they meet in groups of variable size. We find that the evolution of multiplayer cooperation primarily depends on the network topology and movement cost while using different stochastic update rules seldom influences evolutionary outcomes. Cooperation robustly co-evolves with movement on complete networks and structure has a partially detrimental effect on it. These findings contrast an established principle from evolutionary graph theory that cooperation can only emerge under some update rules and if the average degree is lower than the reward-to-cost ratio and the network far from complete. We find that group-dependent movement erases the locality of interactions, suppresses the impact of evolutionary structural viscosity on the fitness of individuals, and leads to assortative behaviour that is much more powerful than viscosity in promoting cooperation. We analyse the differences remaining between update rules through a comparison of evolutionary outcomes and fixation probabilities.