Variational Inference of Parameters in Opinion Dynamics Models

arXiv:2403.05358v1 Announce Type: cross
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 …

abstract agent arxiv challenge cs.cy cs.lg cs.si dynamics heuristics inference opinion optimization parameters simulation social stat.ml studying type work