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Autonomous Agents and Multi-Agent Systems

, Volume 30, Issue 6, pp 1148–1174 | Cite as

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation

  • Nidhi Parikh
  • Harshal G. Hayatnagarkar
  • Richard J. Beckman
  • Madhav V. Marathe
  • Samarth Swarup
Article

Abstract

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models.

Keywords

Social simulation Behavior modeling Disaster modeling 

Notes

Acknowledgments

We thank our external collaborators and members of the Network Dynamics and Simulation Science Laboratory (NDSSL) for their suggestions and comments. This work has been supported in part by DTRA CNIMS Contract HDTRA1-11-D-0016-0001, DTRA Grant HDTRA1-11-1-0016, NIH MIDAS Grant 5U01GM070694-11, NIH Grant 1R01GM109718, NSF NetSE Grant CNS-1011769, and NSF SDCI Grant OCI-1032677.

Supplementary material

10458_2016_9331_MOESM1_ESM.pdf (1.1 mb)
Supplementary material 1 (pdf 1106 KB)

References

  1. 1.
    SALT mass casualty triage: Concept endorsed by the American College of Emergency Physicians, American College of Surgeons Committee on Trauma, American Trauma Society, National Association of EMS Physicians, National Disaster Life Support Education Consortium, and State and Territorial Injury Prevention Directors Association. Disaster Medicine and Public Health Preparedness, 2(4), 245–246 (2008).Google Scholar
  2. 2.
    Adiga, A., Agashe, A., Arifuzzaman, S., Barrett, C.L., Beckman, R.J., Bisset, K.R., Chen, J., Chungbaek, Y., Eubank, S.G., Gupta, S., Khan, M., Kuhlman, C.J., Lofgren, E., Lewis, B.L., Marathe, A., Marathe, M.V., Mortveit, H.S., Nordberg, E., Rivers, C., Stretz, P., Swarup, S., Wilson, A., & Xie, D. (2015). Generating a synthetic population of the United States. Tech. Rep. NDSSL 15-009, Network Dynamics and Simulation Science Laboratory.Google Scholar
  3. 3.
    Adiga, A., Marathe, M., Mortveit, H., Wu, S., & Swarup, S. (2013). Modeling urban transportation in the aftermath of a nuclear disaster: The role of human behavioral responses. In: The Conference on Agent-Based Modeling in Transportation Planning and Operations. Blacksburg, VA.Google Scholar
  4. 4.
    Adiga, A., Mortveit, H.S., & Wu, S.(2013). Route stability in large-scale transportation systems. In: The Workshop on Multiagent Interaction Networks (MAIN), held in conjunction with AAMAS 2013. St. Paul, MN, USA.Google Scholar
  5. 5.
    Atun, F. (2014). Understanding effects of complexity in cities during disasters. In C. Walloth, J. M. Gurr, & J. A. Schmidt (Eds.), Understanding Complex Urban Systems: Multidisciplinary Approaches to Modeling (pp. 51–66). Cham: Springer.CrossRefGoogle Scholar
  6. 6.
    der Auf Heide, E. (2006). The importance of evidence-based disaster planning. Annals of Emergency Medicine, 47(1), 34–49.CrossRefGoogle Scholar
  7. 7.
    Barrett, C., Bisset, K., Chandan, S., Chen, J., Chungbaek, Y., Eubank, S., Evrenosoğlu, Y., Lewis, B., Lum, K., Marathe, A., Marathe, M., Mortveit, H., Parikh, N., Phadke, A., Reed, J., Rivers, C., Saha, S., Stretz, P., Swarup, S., Thorp, J., Vullikanti, A., & Xie, D. (2013). Planning and response in the aftermath of a large crisis: An agent-based informatics framework. In: R. Pasupathy, S.H. Kim, A. Tolk, R. Hill, M.E. Kuhl (eds.) Proceedings of the 2013 Winter Simulation Conference.Google Scholar
  8. 8.
    Barrett, C., Eubank, S., Marathe, A., Marathe, M., Pan, Z., & Swarup, S. (2011). Information integration to support policy informatics. The Innovation Journal 16(1), article 2.Google Scholar
  9. 9.
    Beckman, R., Channakeshava, K., Huang, F., Kim, J., Marathe, A., Marathe, M., et al. (2013). Integrated multi-network modeling environment for spectrum management. IEEE Journal on Selected Areas in Communications, 31(6), 1158–1168.CrossRefGoogle Scholar
  10. 10.
    Beckman, R. J. (1995). Plotting \(p^k\) factorial or \(p^{n-k}\) fractional factorial data. The American Statistician, 50(2), 170–174.Google Scholar
  11. 11.
    Beckman, R.J., Baggerly, K.A., McKay, M.D. (1996). Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice, 30(6), 415–429. http://ideas.repec.org/a/eee/transa/v30y1996i6p415-429.html
  12. 12.
    Buddemeier, B.R., Valentine, J.E., Millage, K.K., & Brandt, L.D.(2011). National Capital Region: Key response planning factors for the aftermath of nuclear terrorism. Tech. Rep. LLNL-TR-512111, Lawrence Livermore National Lab.Google Scholar
  13. 13.
    Chandan, S., Saha, S., Barrett, C., Eubank, S., Marathe, A., Marathe, M., Swarup, S., & Vullikanti, A.K. (2013). Modeling the interactions between emergency communications and behavior in the aftermath of a disaster. In: The International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP). Washington DC, USA.Google Scholar
  14. 14.
    Dillon, M. (2014). Determining optimal fallout shelter times following a nuclear detonation. Proceedings of the Royal Society A, 470, 20130,693.CrossRefGoogle Scholar
  15. 15.
    Dombroski, M. J., & Fischbeck, P. S. (2006). An integrated physical dispersion and behavioral response model for risk assessment of radiological dispersion device (RDD) events. Risk Analysis, 26(2), 501–514. doi: 10.1111/j.1539-6924.2006.00742.x.CrossRefGoogle Scholar
  16. 16.
    Drabek, T. E., & Boggs, K. S. (1968). Families in disaster: Reactions and relatives. Journal of Marriage and the Family, 30, 443–451.CrossRefGoogle Scholar
  17. 17.
    Epstein, J. (2006). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.zbMATHGoogle Scholar
  18. 18.
    Eubank, S., Guclu, H., Kumar, V. S. A., Marathe, M., Srinivasan, A., Toroczkai, Z., et al. (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429, 180–184.CrossRefGoogle Scholar
  19. 19.
    Goldman, C.V., & Zilberstein, S. (2008). Communication-based decomposition mechanisms for decentralized MDPs. Journal of Artificial Intelligence Research, 32(1), 169–202. http://dl.acm.org/citation.cfm?id=1622673.1622678.
  20. 20.
    González, M. C., Hidalgo, C. A., & Barabási, A. L. (2008). Understanding human mobility patterns. Nature, 453, 779–782.CrossRefGoogle Scholar
  21. 21.
    Guterbock, T. M., Lambert, J. H., Bebel, R. A., & Parker, M. W. (2011). NCR behavioral survey 2011: Work, school or home? Issues in sheltering in place during an emergency. Tech. rep., Center for Survey Research, University of Virginia.Google Scholar
  22. 22.
    Hedström, P., & Åberg, Y. (2005). Quantitative research, agent-based modeling, and theories of the social. Dissecting the social: On the principles of analytical sociology (pp. 114–144). Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  23. 23.
    Hedström, P., & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36, 49–67.CrossRefGoogle Scholar
  24. 24.
    Lasker, R.D. (2004). Redefining readiness: Terrorism Planning through the eyes of the public. Center for the Advancement of Collaborative Strategies in Health, New York Academy of Medicine. http://books.google.com/books?id=dvfgGgAACAAJ.
  25. 25.
    Lasker, R.D., Hunter, N.D., Francis, S.E. (2007). With the public’s knowledge, we can make sheltering in place possible. New York Academy of Medicine, New York, NY. http://books.google.com/books?id=PUHQMwAACAAJ.
  26. 26.
    Lewis, B., Swarup, S., Bisset, K., Eubank, S., Marathe, M., & Barrett, C. (2013). A simulation environment for the dynamic evaluation of disaster preparedness policies. The Journal of Public Health Management and Practice, 19, S42–S48.CrossRefGoogle Scholar
  27. 27.
    Lin, Y., Fedchenia, I., LaBarre, B., & Tomastik, R. (2010). Agent-based simulation of evacuation: An office building case study. In: W.W.F. Klingsch, C. Rogsch, A. Schadschneider, M. Schreckenberg (eds.) Pedestrian and evacuation dynamics 2008 (pp. 347–357). Springer, Berlin. doi: 10.1007/978-3-642-04504-2_30.
  28. 28.
    Liu, S., Murray-Tuite, P., & Schweitzer, L. (2012). Analysis of child pick-up during daily routines and for daytime no-notice evacuations. Transportation Research Part A: Policy and Practice, 46(1), 48–67. doi: 10.1016/j.tra.2011.09.003.Google Scholar
  29. 29.
    Lum, K., Chungbaek, Y., Eubank, S.G., & Marathe, M.V. (2013). A two-stage, fitted values approach to activity matching. In: The Conference on Agent-Based Modeling in Transportation Planning and Operations. Blacksburg, VAGoogle Scholar
  30. 30.
    Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–166.CrossRefGoogle Scholar
  31. 31.
    Marathe, M., Mortveit, H., Parikh, N., & Swarup, S. (2014). Prescriptive analytics using synthetic information. In W. H. Hsu (Ed.), Emerging trends in predictive analytics: Risk management and decision making. Hershey, PA: IGI Global.Google Scholar
  32. 32.
    Pan, X. (2006). Computational modeling of human and social behaviors for emergency egress analysis. Ph.D. thesis, Dept. of Civil and Environmental Engineering, Stanford University.Google Scholar
  33. 33.
    Parikh, N., Swarup, S., Stretz, P.E., Rivers, C.M., Lewis, B.L., Marathe, M.V., Eubank, S.G., Barrett, C.L., Lum, K., & Chungbaek, Y. (2013). Modeling human behavior in the aftermath of a hypothetical improvised nuclear detonation. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Saint Paul, MN, USA.Google Scholar
  34. 34.
    Pelechano, N., O’brien, K., Silverman, B., & Badler, N. (2005). Crowd simulation incorporating agent psychological models, roles and communication. In: 1st Int’l Workshop on Crowd Simulation (pp. 21–30).Google Scholar
  35. 35.
    Perry, R. W., & Lindell, M. K. (2003). Understanding citizen response to disasters with implications for terrorism. Journal of Contingencies and Crisis Management, 11(2), 49–60.CrossRefGoogle Scholar
  36. 36.
    Sherman, M. F., Peyrot, M., Magda, L. A., & Gershon, R. R. M. (2011). Modeling pre-evacuation delay by evacuees in World Trade Center towers 1 and 2 on September 11, 2001: A revisit using regression analysis. Fire Safety Journal. doi: 10.1016/j.firesaf.2011.07.001.
  37. 37.
    Steunebrink, B.R., Dastani, M., & Meyer, J.J.C. (2010). Emotions to control agent deliberation. In: Proceedings of AAMAS (pp. 973–980). Richland, SC. http://dl.acm.org/citation.cfm?id=1838206.1838337
  38. 38.
    Sutton, R., Precup, D., & Singh, S. (1999). Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1–2), 181–211.MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Tsai, J., Fridman, N., Bowring, E., Brown, M., Epstein, S., Kaminka, G., Marsella, S.C., Ogden, A., Rika, I., Sheel, A., Taylor, M., Wang, X., Zilka, A., & Tambe, M. (2011). ESCAPES—Evacuation simulation with children, authorities, parents, emotions, and social comparison. In: Proceedings of AAMAS. Taipei, Taiwan.Google Scholar
  40. 40.
    Wein, L. M., Choi, Y., & Denuit, S. (2010). Analyzing evacuation versus shelter-in-place strategies after a terrorist nuclear detonation. Risk Analysis, 30(9), 1315–1327.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Nidhi Parikh
    • 1
  • Harshal G. Hayatnagarkar
    • 1
  • Richard J. Beckman
    • 1
  • Madhav V. Marathe
    • 1
  • Samarth Swarup
    • 1
  1. 1.Network Dynamics and Simulation Science LabBiocomplexity Institute of Virginia TechBlacksburgUSA

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