An evidence-accumulating drift-diffusion model of competing information spread on networks

TL;DR

This paper introduces an agent-based model using evidence accumulation via drift-diffusion processes to simulate how competing information spreads and influences consensus or polarization in networks.

Contribution

It presents a novel evidence-accumulating drift-diffusion model for information spread on networks, incorporating psychological insights and analyzing emergent behaviors through simulations.

Findings

Consensus emerges with a single information source.

Opposing sources can lead to polarization or consensus depending on persistence.

Duration of information sources impacts behavior more than the number of sources.

Abstract

In this paper, we propose an agent-based model of information spread, grounded on psychological insights on the formation and spread of beliefs. In our model, we consider a network of individuals who share two opposing types of information on a specific topic (e.g., pro- vs. anti-vaccine stances), and the accumulation of evidence supporting either type of information is modelled by means of a drift-diffusion process. After formalising the model, we put forward a campaign of Monte Carlo simulations to identify population-wide behaviours emerging from agents' exposure to different sources of information, investigating the impact of the number and persistence of such sources, and the role of the network structure through which the individuals interact. We find similar emergent behaviours for all network structures considered. When there is a single type of information, the main observed emergent behaviour is consensus. When there are opposing information sources, both consensus or polarisation can result; the latter occurs if the number and persistence of the sources exceeds some threshold values. Importantly, we find the emergent behaviour is mainly influenced by how long the information sources are present for, as opposed to how many sources there are.