B1473
Title: Quantifying uncertainty of simulated populations
Authors: Leanna House - Virginia Tech (United States) [presenting]
Abstract: 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 and share. 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 that is imposed by the use of synthetic populations remains a difficult task. And, when done poorly, inferences derived from complex simulation models are likely overconfident and depend on random, unknown features of supplied input populations. Approaches for generating synthetic populations a posteriori are developed, which can be incorporated directly into simulation-based analyses.
