Self-organized Boolean game on networks

TL;DR

This paper introduces a Boolean game model on networks where agents' herd behavior influences system dynamics, revealing that network topology and information heterogeneity significantly impact overall system performance and agent success.

Contribution

The paper presents a new Boolean game model with a single parameter for herd behavior, analyzing how network topology affects system dynamics and agent success.

Findings

Larger degree variance networks lead to lower system profit.

The system self-organizes into a stable state outperforming random choice.

Agents with more information gain more in heterogeneous networks.

Abstract

A model of Boolean game with only one free parameter $p$ that denotes the strength of herd behavior is proposed where each agent acts according to the information obtained from his neighbors in network and those in the minority are rewarded. The simulation results indicate that the dynamic of system is sensitive to network topology, where the network of larger degree variance, i.e. the system of greater information heterogeneity, leads to less system profit. The system can self-organize to a stable state and perform better than random choice game, although only the local information is available to the agents. In addition, in heterogeneity networks, the agents with more information gain more than those with less information for a wide extent of herd strength $p$.