Variational Inference of Parameters in Opinion Dynamics Models

Jacopo Lenti, Fabrizio Silvestri, Gianmarco De Francisci Morales

Abstract: Understanding human behavior represents a paramount challenge in modern social systems. This task must be tackled with tools that both explain the mechanisms underlying the social dynamics and efficiently handle vast amounts of data. While agent-based models (ABMs) are generally used as simulating tools to describe social dynamics, their connection to data is lackluster. Instead, here we adopt a probabilistic machine learning approach for fitting ABMs to real data. To this end, we propose a variational inference (VI) framework that estimates the macroscopic and microscopic parameters of several opinion dynamics models. Our methodology encompasses three steps: (i) translation of the opinion dynamics models into probabilistic generative models (PGMs), (ii) relaxation of discrete variables to make the models differentiable, and (iii) estimation of the parameters and latent variables via stochastic VI (SVI). Experiments show that VI improves over existing methods in estimating discrete and continuous variables, both at the microscopic and macroscopic scales, in all four different categories of rules opinion dynamics models. Moreover, VI effectively estimates high-dimensional variables, up to 400 agent-level attributes, and is faster than the alternatives.

External IDs:doi:10.1109/tcss.2025.3640692