Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study

TL;DR

This paper introduces a method to improve the efficiency of calibrating agent-based models for social dynamics, enabling faster policy analysis using a case study on epidemiological modeling.

Contribution

The paper presents a novel application of history matching to accelerate the calibration process of agent-based models for policy-relevant social dynamics.

Findings

Significantly increased calibration efficiency

Broader applicability of agent-based models in policy contexts

Validated approach on epidemiological model

Abstract

Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to inform real-world policy decision-making, often with significant time sensitivity. One such modeling approach is agent-based modeling, which offers particular strengths for capturing spatial and behavioral realism, and for in-silico experiments (varying input parameters and assumptions to explore their downstream impact on key outcomes). To be useful in the real world, these models must be able to qualitatively or quantitatively capture observed empirical phenomena, forming the starting point for subsequent experimentation. Computational constraints often form a significant hurdle to timely calibration and policy analysis in high resolution agent-based models. In this paper, we present a technical solution to address this bottleneck, substantially increasing efficiency and thus widening the range of utility for policy models. We illustrate our approach with a case study using a previously published and widely used epidemiological model.