Collective decision-making with heterogeneous biases: Role of network topology and susceptibility

TL;DR

This paper explores how network topology and individual biases influence collective decision-making, revealing that adjusting network connectivity can optimize responsiveness to environmental changes.

Contribution

It introduces a spin model with heterogeneous preferences on sparse random graphs, extending previous work on complete and regular lattices.

Findings

Maximum susceptibility depends on network connectivity.

Network modifications can enhance system responsiveness.

Adaptive network structures may improve decision accuracy.

Abstract

The ability of groups to make accurate collective decisions depends on a complex interplay of various factors, such as prior information, biases, social influence, and the structure of the interaction network. Here, we investigate a spin model that accounts for heterogeneous preferences and enables control over the non-linearity of social interactions. Building on previous results for complete graphs and regular 2D lattices, we investigate how the modification of network topology towards (sparse) random graphs can affect collective decision-making. We use two different measures of susceptibility to assess the responsiveness of the system to internal and external perturbations. In particular, we investigate how the maximum of susceptibility depends on network connectivity. Based on our findings, we discuss how collective systems might adapt to changes in environmental fluctuations by adjusting their network structure or the nature of their social interactions in order to remain in the region of maximal susceptibility.