Interacting Agents and Continuous Opinions Dynamics

TL;DR

This paper introduces a flexible model of opinion dynamics where agents update continuous opinions through binary interactions, showing how thresholds influence consensus or clustering, with extensions to networks and heterogeneous thresholds.

Contribution

The paper presents a novel, generalized model of opinion dynamics incorporating network interactions, threshold heterogeneity, and adaptive thresholds, expanding previous binary encounter models.

Findings

High thresholds lead to opinion convergence.

Low thresholds result in multiple opinion clusters.

Model extensions include network interactions and adaptive thresholds.

Abstract

We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, whereas low thresholds result in several opinion clusters. The model is further generalised to network interactions, threshold heterogeneity, adaptive thresholds and binary strings of opinions.