Skip to main content
SpringerLink
Log in

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

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. http://nuclearsecrecy.com/blog/2011/12/30/friday-image-bullseye-on-washington-1953/.

  2. The unit Gray (Gy) expresses absorbed radiation. Roentgen (R) is the unit for exposure in air. It is assumed that 1 R \(=\) 0.01 Gy [12].

  3. http://washington.org.

  4. http://www.citytowninfo.com/.

  5. http://data.dc.gov.

References

  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).

  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.

  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.

  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.

  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.

    Chapter  Google Scholar 

  6. der Auf Heide, E. (2006). The importance of evidence-based disaster planning. Annals of Emergency Medicine, 47(1), 34–49.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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. 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. 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.

  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.

  14. Dillon, M. (2014). Determining optimal fallout shelter times following a nuclear detonation. Proceedings of the Royal Society A, 470, 20130,693.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  16. Drabek, T. E., & Boggs, K. S. (1968). Families in disaster: Reactions and relatives. Journal of Marriage and the Family, 30, 443–451.

    Article  Google Scholar 

  17. Epstein, J. (2006). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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. González, M. C., Hidalgo, C. A., & Barabási, A. L. (2008). Understanding human mobility patterns. Nature, 453, 779–782.

    Article  Google Scholar 

  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.

  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.

    Chapter  Google Scholar 

  23. Hedström, P., & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36, 49–67.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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. 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. 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, VA

  30. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–166.

    Article  Google Scholar 

  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. 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.

  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.

  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).

  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.

    Article  Google Scholar 

  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. 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. 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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

  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.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Network Dynamics and Simulation Science Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, 24061, USA

    Nidhi Parikh, Harshal G. Hayatnagarkar, Richard J. Beckman, Madhav V. Marathe & Samarth Swarup

Authors
  1. Nidhi Parikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harshal G. Hayatnagarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard J. Beckman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Madhav V. Marathe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Samarth Swarup
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nidhi Parikh.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1106 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parikh, N., Hayatnagarkar, H.G., Beckman, R.J. et al. A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation. Auton Agent Multi-Agent Syst 30, 1148–1174 (2016). https://doi.org/10.1007/s10458-016-9331-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-016-9331-y

Keywords

Search

Navigation