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.

Opinion dynamics models describe how social influence can lead groups toward consensus or conflict, but their empirical validation requires reliable parameter estimation. This work studies how to estimate the key parameters of a model of opinion formation. We prove that one parameter – the general "openness" – can be reconstructed given enough observations, while estimating how quickly opinions adjust is much more difficult, exhibiting persistent bias.

Estimating hidden opinions in agent-based models is challenging because individual beliefs are only partially observable. This paper compares two competing approaches for reconstructing those hidden states: Data Assimilation (DA) — widely used in applications like weather forecasting — and Likelihood-Based Inference (LBI), which directly exploits the model’s internal probabilistic structure but requires a manually designed likelihood function for each model. The study shows that LBI better recovers individual opinions, while DA remains effective for forecasting collective trends.

Digital platforms have become key arenas for digital nationalism, where humor and cultural symbols shape collective identities under conflict. The top-down approach of military objectives intertwines with bottom-up virality mechanisms. Examining government and grassroots meme campaigns during the Russian invasion of Ukraine, this work shows how narrative framing—from victimhood to antagonism—mobilizes both domestic and global publics in different ways. The study reveals how networked storytelling and affect operate as instruments of influence in memetic warfare.

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.