** This workshop is by invitation only **
Agent-based modeling is widely used across many disciplines to study complex emergent behavior generated from simulated entities that interact with each other and their environment according to relatively simple rules. Applications include automobile traffic modeling, weather forecasting, and the study of epidemics. The inferential challenge of agent-based models is that (in general) there is no tractable likelihood function, and thus it is difficult to fit the model or make quantified statements about the accuracy of predictions. This workshop addresses that challenge from the perspective of uncertainty quantification, so that emulator methodology can be used to make approximate principled inferences about agent-based simulations.
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