Effect of group organization on the performance of cooperative processes

TL;DR

This study uses an agent-based model to explore how hierarchical social network structures influence group problem-solving efficiency, especially in complex landscapes, revealing that modular hierarchies enhance performance.

Contribution

It demonstrates that hierarchical, modular networks improve cooperative problem-solving efficiency in rugged landscapes, explaining the prevalence of such structures in nature.

Findings

Hierarchical networks outperform scale-free and random networks in rugged landscapes.

Optimal performance occurs when the main hub slightly favors imitation over independent search.

Performance declines when the main hub acts independently or imitates compulsively.

Abstract

Problem-solving competence at group level is influenced by the structure of the social networks and so it may shed light on the organization patterns of gregarious animals. Here we use an agent-based model to investigate whether the ubiquity of hierarchical networks in nature could be explained as the result of a selection pressure favoring problem-solving efficiency. The task of the agents is to find the global maxima of NK fitness landscapes and the agents cooperate by broadcasting messages informing on their fitness to the group. This information is then used to imitate, with a certain probability, the fittest agent in their influence networks. For rugged landscapes, we find that the modular organization of the hierarchical network with its high degree of clustering eases the escape from the local maxima, resulting in a superior performance as compared with the scale-free and the random networks. The optimal performance in a rugged landscape is achieved by letting the main hub to be only slightly more propense to imitate the other agents than vice versa. The performance is greatly harmed when the main hub carries out the search independently of the rest of the group as well as when it compulsively imitates the other agents.