Social Consensus Modeling Using Threshold Boolean Networks

This study leverages evolutionary computation, particularly Particle Swarm Optimization (PSO) and its fuzzy variant (FST-PSO), to infer the weight matrix and threshold values required for threshold Boolean networks to reach a fixed-point attractor state, where all nodes converge to either 0 or 1. The research investigates the efficacy of these algorithms in generating networks that exhibit consensus properties, analyzing the topology and time steps needed to reach consensus. The results indicate that while PSO's effectiveness dropped significantly with increasing network size, achieving only 79% effectiveness for networks with eight nodes, FST-PSO maintained 100% effectiveness across all sizes. FST-PSO also demonstrated faster convergence, requiring fewer iterations and showing better scalability and stability in optimizing network parameters for consensus formation. This work contributes to understanding how network topology influences consensus formation, offering insights applicable to decision-making, optimization problems, and complex system analysis.