Upcoming Events
Uncertainty Quantification, Agent-Based Models, and Synthetic Populations
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. This will be a largely conceptual talk showing some approaches for using such models for parameter estimation and quantifying uncertainty in resulting predictions. If time allows, the talk will also deal some with accounting for uncertainty in synthetic populations which are required for ABMs.
Speaker Biography
Dave Higdon is a professor in the Department of Statistics at Virginia Tech; he served as department head from 2019 to 2022 and back in that role for 2025. Prior to that, he spent 5 years with VT’s Social Decisions Analytics Laboratory in Arlington. He spent 14 years with the Statistical Sciences Group at Los Alamos National Laboratory, serving as Group leader for five years. He also spent his first seven years since graduate school as an assistant professor with the Statistical Science Department at Duke University. He is an expert in uncertainty quantification, Bayesian statistical modeling of environmental, physical, and social systems, combining observations with computer simulation models for prediction and inference. His research interests include space-time modeling; inverse problems in a variety of scientific applications; statistical modeling in ecology, environmental science, and biology; multiscale models; distributed methods for Markov chain Monte Carlo and posterior exploration; statistical computing; and Monte Carlo and simulation-based methods.
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