Impact of centrality on cooperative processes

TL;DR

This paper investigates how the centrality of nodes in communication networks affects group problem-solving efficiency, revealing that network topology impacts performance differently depending on task complexity.

Contribution

It introduces an agent-based model analyzing the role of betweenness centrality in cooperative problem-solving across different landscape ruggedness levels.

Findings

For easy tasks, network topology does not affect performance.

Central nodes tend to find the global maximum first in simple landscapes.

In complex landscapes, variance in betweenness correlates with improved group performance.

Abstract

The solution of today's complex problems requires the grouping of task forces whose members are usually connected remotely over long physical distances and different time zones. Hence, understanding the effects of imposed communication patterns (i.e., who can communicate with whom) on group performance is important. Here, we use an agent-based model to explore the influence of the betweenness centrality of the nodes on the time the group requires to find the global maxima of NK-fitness landscapes. The agents cooperate by broadcasting messages, informing on their fitness to their neighbors, and use this information to copy the more successful agents in their neighborhood. We find that for easy tasks (smooth landscapes), the topology of the communication network has no effect on the performance of the group, and that the more central nodes are the most likely to find the global maximum first. For difficult tasks (rugged landscapes), however, we find a positive correlation between the variance of the betweenness among the network nodes and the group performance. For these tasks, the performances of individual nodes are strongly influenced by the agents dispositions to cooperate and by the particular realizations of the rugged landscapes.