|
Abstract:
|
Agent-based models (ABMs) use rules at the individual (agent) level to simulate a social, ecologic, or social-technical system, producing structured behavior when viewed at an aggregated level. For example, dynamic network simulation models commonly evolve a very large collection of agents interacting over a network that evolves with time. Such models are often used simulate animal populations, epidemics or transportation, typically producing random trajectories, even when model parameters and initial conditions are identical. While Approximate Bayesian Computation has been used with such models to carry out statistical inference, an alternative is to consider the approaches commonly used in UQ and model calibration. Adapting to the inherent randomness in these simulations is necessary before applying the standard tools of UQ. This talk shows some approaches for adapting Bayesian model calibration to these stochastic systems. We'll consider a case study of a recent epidemic, seeking to forecast the epidemic’s behavior given initial administrative information.
|
