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Spatial Temporal Exponential-Family Point Processes for the Evolution of Social Systems


Realistic stochastic models for the co-evolution of social relations and individual behavior over time have broad applicability in social science. The stochastic actor oriented model and temporal exponential-family random graph model have proven useful in modeling longitudinal social networks with nodal covariates while latent space approaches to network analysis offer unique insights into social phenomena. We borrow ideas from these frameworks to construct a spatial temporal exponential-family of point processes (STEPP) to jointly model the co-evolution of social relations and individual behavior in discrete time. We develop likelihood-based inference of STEPP parameters for spatial temporal data as well as latent space inference for longitudinal social networks.

We utilize the general STEPP framework to construct a virtual laboratory for simulating social systems and interventions. This virtual laboratory provides a novel simulation tool for developing and assessing potential strategies for influencing behavior in particular communities. We apply these methods to a study of risky behavior in adolescent friendship networks to model and simulate social processes associated with drug and alcohol use in middle school students.

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