Agreement Threshold on Axelrod's model of Cultural Dissemination

TL;DR

This paper introduces a bipartite graph visualization for Axelrod's cultural dissemination model and proposes an agreement threshold modification that results in multiple opinion clusters, better modeling diverse opinion-based groups.

Contribution

It presents a novel bipartite graph visualization method and a threshold-based modification to Axelrod's model, enabling the formation of multiple opinion clusters.

Findings

Bipartite graph visualization reveals feature-trait combinations in cultural states.

Agreement threshold leads to multiple clusters, avoiding full agreement.

Model adapts to increasing features with sustained cluster diversity.

Abstract

Shared opinions are an important feature in the formation of social groups. In this paper, we use the Axelrod model of cultural dissemination to represent opinion-based groups. In the Axelrod model, each agent has a set of features which each holds one of a set of nominally related traits. Survey data, for example, has a similar structure, where each participant answers each of a set of items with responses from a fixed list. We present an alternative method of displaying the Axelrod model by representing it as a bipartite graph, i.e., participants and their responses as separate nodes. This allows us to see which feature-trait combinations are selected in the final state. This visualisation is particularly useful when representing survey data as it illustrates the co-evolution of cultures and opinion-based groups in Axelrod's model of cultural diffusion. We also present a modification to the Axelrod model. A standard finding of the Axelrod model with many features is for all agents to fully agree in one cluster. We introduce an agreement threshold and allow nodes to interact only with those neighbours who are within this threshold (i.e., those with similar opinions) rather than those with any opinion. This method reliably yields a large number of clusters for small agreement thresholds and, importantly, does not limit to single cluster when the number of features grows large. This potentially provides a method for modelling opinion-based groups where as opinions are added, the number of clusters increase.