Modeling human behavior in the aftermath of a hypothetical improvised nuclear detonation

Authors:
Nidhi Parikh;Samarth Swarup;Paula E. Stretz;Caitlin M. Rivers;Bryan L. Lewis;Madhav V. Marathe;Stephen G. Eubank;Christopher L. Barrett;Kristian Lum;Youngyun Chungbaek
Affiliations:
Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacsbug, VA, USA;Virginia Bioinformatics Institute, Virginia Tech, Blacksbug, VA, USA
Venue:
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Year:
2013

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Abstract

In this paper we describe a multiagent simulation model of human behavior in the aftermath of a hypothetical, large-scale, human-initiated crisis in the center of Washington D.C. Prior studies of this scenario have focused on modeling the physical effects of the attack, such as thermal and blast effects, prompt radiation, and fallout. Casualty and mortality estimates have been obtained by assuming a spatially static human population, ignoring human behavioral response to the event. We build a simulation of a behaving human population and its interaction with various interdependent infrastructures, to try to understand how human response to such an event would change outcomes, and also how modeling this response would enable us to develop new perspectives on planning for this event. Here we present details of the simulation, focusing on the agent design and multiagent interaction, and present initial results on how rapid restoration of communication could alter behavior beneficially.