Optimal network structure for collective performance with strategic information sharing

2026-05-01

AI Summary

This study addresses how network structure optimization can enhance collective performance in estimation tasks under strategic information sharing among competing individuals. Building upon evolutionary game theory and network science, the authors develop an analytical model to characterize the interplay between individual sampling strategies and information-sharing behaviors. The work’s central contribution lies in uncovering, for the first time, that optimal collective performance emerges from a trade-off between the rate of information sharing and the method of integration; furthermore, it identifies an intermediate average network degree that maximizes performance. Notably, under non-uniform sampling, the individually optimal number of samples is inversely proportional to an agent’s network degree. These findings provide a theoretical foundation for designing efficient distributed estimation systems.

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.

Problem

• collective performance
• strategic information sharing
• network structure
• evolutionary game
• collective estimation

Innovation

• strategic information sharing
• evolutionary game theory
• collective estimation
• network structure optimization
• heterogeneous sampling