Supercooperation in Evolutionary Games on Correlated Weighted Networks

TL;DR

This paper demonstrates that specific correlations between link weights and node degrees in complex networks significantly enhance cooperation levels in evolutionary games, surpassing unweighted network performance.

Contribution

It introduces a novel class of weighted networks with degree-correlated weights that promote supercooperation in evolutionary game dynamics.

Findings

Weighted networks with degree-correlated links increase cooperation.

Transforming weighted networks reveals topological features fostering cooperation.

Supercooperative networks can be intentionally constructed based on these insights.

Abstract

In this work we study the behavior of classical two-person, two-strategies evolutionary games on a class of weighted networks derived from Barab\'asi-Albert and random scale-free unweighted graphs. Using customary imitative dynamics, our numerical simulation results show that the presence of link weights that are correlated in a particular manner with the degree of the link endpoints, leads to unprecedented levels of cooperation in the whole games' phase space, well above those found for the corresponding unweighted complex networks. We provide intuitive explanations for this favorable behavior by transforming the weighted networks into unweighted ones with particular topological properties. The resulting structures help to understand why cooperation can thrive and also give ideas as to how such supercooperative networks might be built.