Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

TL;DR

This paper introduces a novel Markovian opinion dynamics model incorporating attractive and repulsive forces to better simulate decision-making processes among interacting agents, inspired by traffic junction scenarios.

Contribution

It extends existing opinion dynamics models by integrating attractive and repulsive forces, capturing complex group interactions and decision dependencies.

Findings

Model effectively captures behaviors influenced by individual preferences and group interactions.

Demonstrates applicability to traffic junction decision scenarios.

Provides a framework for analyzing collective decision-making in networked systems.

Abstract

In this paper, we model a decision-making process involving a set of interacting agents. We use Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. Existing opinion dynamics models are extended by introducing attractive and repulsive forces that act within and between groups of agents, respectively. Such an extension enables the resemblance of behaviours emerging in networks where agents make decisions that depend both on their own preferences and the decisions of specific groups of surrounding agents. The considered modeling problem and the contributions in this paper are inspired by the interaction among road users (RUs) at traffic junctions, where each RU has to decide whether to go or to yield.