Agent-based models capture how local decisions give rise to collective phenomena, but are rarely learnable from real data. This study introduces a differentiable framework that reconstructs individual behavioral rules while preserving interaction structure and stochasticity through the integration of graph neural networks and diffusion models. The approach bridges mechanistic modeling and machine learning, enabling empirical testing of theories about decentralized and emergent social and ecological systems.

Understanding why people mobilize for collective action requires linking online behavior to social position, exposure, and ideology. Using longitudinal Reddit data and Bayesian causal modeling, this research disentangles how media attention, climate experiences, and peer dynamics jointly shape participation in climate activism. The results highlight how information diffusion and class-linked engagement transform awareness into sustained political mobilization.

Agent-based models of opinion formation often rely on repeated simulations to approximate observed collective outcomes, limiting both interpretability and efficiency. This work introduces a likelihood-based estimation approach that directly connects model parameters to data through probabilistic generative modeling, allowing opinions and interactions to be inferred from evidence rather than tuned by trial. By applying it to the bounded-confidence model, the study advances a data-driven understanding of how opinions evolve through social influence.

Agent-based models explain how micro-level rules produce macro-level patterns, yet linking them to data remains a challenge. This paper presents a protocol for inferring latent agent variables through probabilistic modeling and gradient-based expectation maximization, demonstrated on a housing market simulation. The method enhances predictive accuracy and interpretability, promoting a data-driven way to calibrate generative models of collective behavior.

Online discussions evolve through cascades of interactions that mirror collective opinion dynamics. This work introduces a probabilistic generative model that infers latent communities based on their degree of echo-chamber behavior and ideological polarity. By learning from both network structure and information propagation, the model identifies how polarized subgroups emerge and interact, providing a scalable statistical tool for understanding political and social fragmentation online.

Understanding ideology in social media requires models that learn from how information propagates, not just what it contains. This paper introduces a stochastic generative model that infers multidimensional ideological embeddings by fitting the flow of politically salient content across users. By capturing alignment through diffusion dynamics rather than labels, the model learns the structural complexity of political belief systems, offering a principled framework for representing ideology in networked communication.

Agent-based models explain how opinions evolve through interaction, yet they often remain detached from real data. This work develops a probabilistic inference framework that learns latent opinions and model parameters directly from social traces, retaining the interpretability of theoretical models while gaining empirical grounding. Applied to political conversations on Reddit, it provides a data-driven test of opinion dynamics theories, revealing limited evidence for the backfire effect in online debate.