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.

翻译：尽管基于智能体的模型（ABMs）广泛应用于研究社会现象，但其参数估计仍存在挑战，通常依赖于计算成本高昂的模拟启发式方法。本研究通过将估计问题转化为可直接求解的优化任务，采用变分推断来估计观点动力学ABM的参数。我们的方案基于概率生成式ABMs（PGABMs）：首先从ABM规则合成概率生成模型，然后将推断过程转化为适用于自动微分的优化问题。具体而言，我们使用Gumbel-Softmax重参数化方法处理分类智能体属性，采用随机变分推断进行参数估计。此外，我们探讨了不同复杂度变分分布（正态分布与归一化流）的权衡。我们通过带智能体角色（领导者与追随者）的有界置信模型验证了该方法。相较于基于模拟的方法和MCMC方法，我们的方法能更准确地估计宏观参数（有界置信区间与逆火阈值）以及微观参数（200个分类智能体角色）。因此，该技术使专家能够基于真实观测数据调整和验证其ABMs，通过数据驱动分析揭示社会系统中的人类行为。