Statistical Agent-Based Models for Discrete Spatio-Temporal Systems
Mevin B. Hooten, Christopher K. Wikle · Journal of the American Statistical Association · 2010
Agent-based models have been used to mimic natural processes in a variety of fields, from biology to social science. By specifying mechanistic models that describe how small-scale processes function and then scaling them up, agent-based approaches can result in very complicated large-scale behavior while often relying on only a small set of initial conditions and intuitive rules. Although many agent-based models are used strictly in a simulation context, statistical implementations are less common. To characterize complex dynamic processes, such as the spread of epidemics, we present a hierarchical Bayesian framework for formal statistical agent-based modeling using spatiotemporal binary data. Our approach is based on an intuitive parameterization of the system dynamics and can explicitly accommodate directionally varying dispersal, long distance dispersal, and spatial heterogeneity.