A Framework for Simulating Populations to Quantify Uncertainty

Music City Center 

Applied areas such as, epidemiology, social policy, transportation, etc., often rely on complex simulation models (e.g., agent-based) to assess the viability of potential mitigation and/or policy strategies. Among other inputs, these models tend to require specific, individual-level details for entire populations of interest; e.g., the number, age, and income for every home in a municipality. Yet, rarely is such detail available, or even possible to collect. Some success has resulted from pairing simulation models with synthetic population generators (e.g., iterated conditional models and other imputation methods), but challenges remain in such cases. In particular, describing and accounting for uncertainty in analyses imposed by the use of synthetic populations remains a difficult task. In this paper, we develop approaches for generating synthetic populations a posteriori, which can be incorporated directly into simulation-based analyses for subsequent inference.

social policy

simulation models

agent-based models

synthetic populations

population modeling

uncertainty quantification 

International Society for Bayesian Analysis (ISBA)