Evolution of Cooperation on Temporal Networks

TL;DR

This paper investigates how the temporal nature of social networks influences the evolution of cooperation, revealing that intermediate levels of network temporality most effectively promote cooperative behavior.

Contribution

It introduces a framework for analyzing cooperation on temporal networks and identifies the impact of bursty human interaction patterns on cooperation evolution.

Findings

Temporal networks generally promote cooperation compared to static networks.

Bursty interaction patterns hinder the evolution of cooperation.

An optimal intermediate level of network temporality maximizes cooperation.

Abstract

The structure of social networks is a key determinant in fostering cooperation and other altruistic behavior among naturally selfish individuals. However, most real social interactions are temporal, being both finite in duration and spread out over time. This raises the question of whether stable cooperation can form despite an intrinsically fragmented social fabric. Here we develop a framework to study the evolution of cooperation on temporal networks in the setting of the classic Prisoner's Dilemma. By analyzing both real and synthetic datasets, we find that temporal networks generally facilitate the evolution of cooperation compared to their static counterparts. More interestingly, we find that the intrinsic human interactive pattern like bursty behavior impedes the evolution of cooperation. Finally, we introduce a measure to quantify the temporality present in networks and demonstrate that there is an intermediate level of temporality that boosts cooperation most. Our results open a new avenue for investigating the evolution of cooperation in more realistic structured populations.