Data-Driven Hierarchical Digital Twins of Social Interactions

Social interactions are inherently complex, shaped by dynamic trust building, biases, and adaptive strategies. Yet in laboratory settings, their study is often constrained by small datasets that nonetheless encode rich and sophisticated cognitive processes. This scarcity has historically limited modeling approaches to hand-tailored frameworks that embed strong priors about the underlying mechanisms. Recent advances in data-driven modeling of latent dynamical processes provide an alternative, extracting generative models directly from behavioral data without restrictive assumptions. Building on these methods, we derive hierarchical digital twins from sparse, non-Gaussian investment sequences in repeated trust games. Our approach proceeds in three stages. First, we demonstrate that, despite the limited data, the inferred twins can accurately predict future investments, establishing predictive validity and providing a solid baseline for subsequent mechanistic analyses. Second, we analyze the latent dynamics of these models and show that they capture mechanistic structure in how investments and choice uncertainty are organized in state space and how social versus non-social cues are represented, in a way that parallels patterns observed directly in the empirical behavior. Third, we exploit the generative nature of the hierarchical digital twins to run in-silico experiments beyond the original data, such as probing how specific cues steer participants into trusting or mistrusting states and simulating responses to novel, unobserved cue-outcome combinations and trustee strategies. Together, this work demonstrates how generative digital twins open a new path toward mechanistic, data-driven accounts of social interaction dynamics that both reproduce observed behavior and support hypothesis generation in virtual environments.