Bounded-confidence opinion models with random-time interactions

TL;DR

This paper extends bounded-confidence opinion models by incorporating random-time interactions using renewal processes, analyzing how different interevent-time distributions affect opinion dynamics and convergence.

Contribution

It introduces a framework for BCMs with random interaction times, connecting them to deterministic models and analyzing the effects of various ITDs on opinion evolution.

Findings

Markovian ITDs yield consistent statistical properties with same mean

Non-Markovian ITDs' properties depend on ITD type even with same mean

Different ITDs influence convergence times and steady-state opinions

Abstract

In models of opinion dynamics, agents interact with each other and can change their opinions as a result of those interactions. One type of opinion model is a bounded-confidence model (BCM), in which opinions take continuous values and interacting agents compromise their opinions with each other if their opinions are sufficiently similar. In studies of BCMs, researchers typically assume that interactions between agents occur at deterministic times. This assumption neglects an inherent element of randomness in social interactions, and it is desirable to account for it. In this paper, we study BCMs on networks and allow agents to interact at random times. To incorporate random-time interactions, we use renewal processes to determine social-interaction event times, which can follow arbitrary interevent-time distributions (ITDs). We establish connections between these random-time-interaction BCMs and deterministic-time-interaction BCMs. We analyze the quantitative impact of ITDs on the transient dynamics of BCMs and derive approximate master equations for the time-dependent expectations of the BCM dynamics. We find that BCMs with Markovian ITDs have consistent statistical properties (in particular, they have the same expected time-dependent opinions) when the ITDs have the same mean but that the statistical properties of BCMs with non-Markovian ITDs depend on the type of ITD even when the ITDs have the same mean. Additionally, we numerically examine the transient and steady-state dynamics of our models with various ITDs on different networks and compare their expected order-parameter values and expected convergence times.