Cooperation on Social Networks and Its Robustness

TL;DR

This paper demonstrates through simulations that cooperation can emerge and remain stable on social-like networks, showing robustness across various conditions, with some limitations under specific payoff and noise scenarios.

Contribution

It introduces realistic social network models for studying cooperation, showing their effectiveness and robustness compared to traditional scale-free networks.

Findings

Cooperation levels are comparable to scale-free networks.

Cooperation remains stable across different update rules and dynamics.

High noise or average payoff conditions reduce cooperation.

Abstract

In this work we have used computer models of social-like networks to show by extensive numerical simulations that cooperation in evolutionary games can emerge and be stable on this class of networks. The amounts of cooperation reached are at least as much as in scale-free networks but here the population model is more realistic. Cooperation is robust with respect to different strategy update rules, population dynamics, and payoff computation. Only when straight average payoff is used or there is high strategy or network noise does cooperation decrease in all games and disappear in the Prisoner's Dilemma.