Variational Inference of Parameters in Opinion Dynamics Models

TL;DR

This paper introduces a variational inference method for efficiently estimating parameters in agent-based opinion dynamics models, enabling more accurate and scalable analysis of social phenomena.

Contribution

It presents a novel approach transforming ABM parameter estimation into an optimization problem using probabilistic generative models and variational inference techniques.

Findings

Outperforms simulation-based and MCMC methods in accuracy

Estimates both macro and micro-level parameters effectively

Enables data-driven validation of social ABMs

Abstract

Despite the frequent use of agent-based models (ABMs) for studying social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM, by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (PGABMs): we start by synthesizing a probabilistic generative model from the ABM rules. Then, we transform the inference process into an optimization problem suitable for automatic differentiation. In particular, we use the Gumbel-Softmax reparameterization for categorical agent attributes and stochastic variational inference for parameter estimation. Furthermore, we explore the trade-offs of using variational distributions with different complexity: normal distributions and normalizing flows. We validate our method on a bounded confidence model with agent roles (leaders and followers). Our approach estimates both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic ($200$ categorical, agent-level roles) more accurately than simulation-based and MCMC methods. Consequently, our technique enables experts to tune and validate their ABMs against real-world observations, thus providing insights into human behavior in social systems via data-driven analysis.