Optimal network structure for collective performance with strategic information sharing
2026-05-01 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors studied how people sharing information affects how well a group can guess the colors of balls in a box when each person only sees some balls. They used a model where people decide strategically whether to share what they saw with their connected neighbors. The authors found that the best group performance happens when there is a balance between how much people share and how information spreads on the network. They also discovered that performance is highest when people who see fewer balls have more connections, and those who see more balls have fewer connections.
evolutionary game theorycollective estimationinformation sharingnetwork structurestrategic behavioraverage degreesamplingcollective performancenon-homogeneous allocation
Authors
Ye Wang, Andrea Civilini, Anzhi Sheng, Xiaojie Chen, Long Wang, Vito Latora
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.