High-Fidelity Agent-Based Modeling to Support Prevention Decision-making

Wouter Vermeer, PhD
Northwestern University

ABSTRACT:
Preventing adverse health outcomes is complex due to the multilevel contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general, and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM’s ability to do so depends strongly on its accuracy in capturing the phenomenon. What is more, to support what we call model-based decision-making, these models they need to be accepted by and available to decision-makers and other stakeholders. In this presentation we will present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication.