Memory-based reduced modelling and data-based estimation of opinion spreading

TL;DR

This paper develops a data-driven approach using the Mori-Zwanzig formalism to predict opinion spreading in agent-based models, showing that incorporating memory improves prediction accuracy across network types.

Contribution

It introduces a method to estimate nonlinear autoregressive models with memory from opinion percentage data, enhancing prediction of opinion dynamics.

Findings

Memory inclusion improves prediction accuracy.

Method works across various network topologies.

Nonlinear autoregressive models effectively capture opinion evolution.

Abstract

We investigate opinion dynamics based on an agent-based model, and are interested in predicting the evolution of the percentages of the entire agent population that share an opinion. Since these opinion percentages can be seen as an aggregated observation of the full system state, the individual opinions of each agent, we view this in the framework of the Mori-Zwanzig projection formalism. More specifically, we show how to estimate a nonlinear autoregressive model (NAR) with memory from data given by a time series of opinion percentages, and discuss its prediction capacities for various specific topologies of the agent interaction network. We demonstrate that the inclusion of memory terms significantly improves the prediction quality on examples with different network topologies.