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Agent-based models (ABMs) use rules at the individual level to simulate a social, or socio-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 to simulate epidemics or transportation, typically producing random trajectories, even when the model parameters and initial conditions are identical.
This introduces a number of challenges in designing ensembles of model runs for sensitivity analysis and computer model calibration.
This talk will survey a number of approaches for dealing with stochastic computer model output. A case study will be explored, seeking to forecast an epidemic’s behavior given initial administrative information.
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