Scalable simulation of a Disaster Response Agent-based network Management and Adaptation System (DRAMAS)

Abstract

The objective of this paper is to advance the study of disaster response and recovery (generally, disaster relief) by providing tools and insights to agencies that work in disaster relief. This paper is built on extensive research of disaster relief literature and practice, and provides a comprehensive analysis of agency posturing following an extreme event. We present the Disaster Response Agent-based network Management and Adaptation System (DRAMAS) model, which uses stochastic processes to model the complex interactions between relief agencies of different sizes and capabilities. The DRAMAS simulation environment provide an excellent testing ground for hypotheses regarding relief agency partnerships, goals, roles, and prior involvement, by providing a depiction of the change in agency partnerships and resource investments following a disaster. The goal of this research is to expand the current body of knowledge and examine the fundamental principles of agency success during relief operations. We find that (a) larger relief networks tended to be less efficient at meeting the typical needs of the community, (b) having a relief network with more agents appeared to increase the time it took for a typical need, (c) having a high percentage of local agents resulted in an increased time for typical services, (d) a more dense network resulted in more effective identification of long-term needs and also improved services time, etc. Results from this work provide a path for improving our understanding of interagency partnerships and interaction, and could provide new insights into the behavior of agency networks in response to a disaster.

Keywords:

• Disaster relief
• partnerships
• simulation
• DRAMAS

Acknowledgements

We thank the editors and anonymous referees for their helpful comments. The authors assume responsibility for any errors.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was partially supported by the U.S. National Science Foundation through a Graduate Research Fellowship and others [grant numbers 1200899, 1261058, and 1334930]. This research was also partially supported by the U.S. Department of Homeland Security (DHS) through the National Center for Risk and Economic Analysis of Terrorism Events (CREATE) [grant number 2010-ST-061-RE0001]. However, any opinions, findings, and conclusions or recommendations in this document are those of the authors and do not necessarily reflect views of the NSF, DHS, or CREATE.