Promoting collective intelligence: The advantage of temporal star-structures

TL;DR

This paper investigates how different temporal network structures influence collective cooperation, revealing that single-star structures uniquely promote cooperation more effectively than clustered or random structures.

Contribution

It systematically analyzes the impact of various temporal network structures on cooperation, introducing an index to quantify their effectiveness in promoting collective behavior.

Findings

Single-star temporal networks enhance cooperation more than clustered or random structures.

Temporal networks outperform static networks in fostering collective cooperation.

An index is proposed to measure the structural capacity to promote cooperation.

Abstract

System structures play an essential role in the emergence of collective intelligence in many natural and engineering systems. In empirical systems, interactions among multiple agents may change over time, forming a temporal network structure, where nodes represent the system's components and links capture who interacts with whom. Recent studies report that temporal networks are more conducive to the emergence of collective cooperation compared to their aggregated static structures. However, the question of which kind of structural characteristics of temporal networks promote collective cooperation still remains elusive. Here we systematically investigate the evolution of cooperation on temporal networks with diverse structural characteristics, such as random, star, and cluster structures. We uncover that temporal networks with single-star structures which lack network clusters are more conducive to collective cooperation than other structures. This counterintuitive result cautions against the common belief that network clusters normally facilitate collective cooperation, revealing the unique advantages of temporal networks over static networks. We further propose an index to quantify the capacity of structural characteristics of temporal networks in promoting collective cooperation. Our findings pave the way for designing the optimal structure of temporal networks to favour collective cooperation.