Optimal network structure for collective performance with strategic information sharing

TL;DR

This paper models how strategic information sharing in a network affects collective estimation performance, revealing optimal network structures and sampling strategies for maximizing group accuracy.

Contribution

It introduces an evolutionary game framework to analyze the impact of network topology and individual sampling on collective task performance.

Findings

Optimal network balances sharing rate and information integration.

Intermediate average degree maximizes collective performance.

Heterogeneous sampling improves performance when inversely related to node degree.

Abstract

Information sharing between individuals is crucial to improve performance in collective tasks. However, in a competitive world, individuals may be reluctant to share information with the others, and it is still unclear how the presence of strategic behaviors affects the collective performance of a group. In this study, we introduce an evolutionary game modeling the dynamics of individual behaviors in a collective estimation task. The individuals are organized in a network and have to guess the distribution of ball colors in a box. Each of them samples a given number of balls and can strategically decide whether to share or not this information with its neighbors. We develop a framework that allows to investigate analytically how the collective performance depends on the network structure. We find that the optimal network results from a trade-off between the sharing rate and the way the information is integrated in the network. We further reveal that there exists an intermediate average degree for each type of network maximizing the collective performance. In addition to the uniform case, we consider the case of non-homogeneous allocations of the number of individual samples, showing that the largest collective performance is obtained when the number of ball extracted by an individual is inversely proportional to its degree.