Conceptualizing and implementing an agent-based model of information flow and decision making during hurricane threats

Abstract This article introduces an agent-based modeling laboratory for investigating how evolving hazard information, propagated through forecaster, media, public official, and peer information networks, affects patterns of public protective-action decisions during hurricane threats. The model, called CHIME ABM, provides a platform for integrating atmospheric science, social science, and computer and information science knowledge and data to explore the complex socio-ecological dynamics of modern hazard information and decision systems from a new perspective. First, the model's interdisciplinary conceptualization and implementation is described. Results are then presented from experiments demonstrating the model's behaviors and comparing patterns of evacuation decisions when key agent parameters and the geographical population distribution, forecast skill, and storm are varied. The article illustrates how this type of theoretically and empirically informed digital laboratory can be used to develop new insights into the interactions among environmental hazards, information flow, protective decisions, and societal outcomes.