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Disaster world

Decision-theoretic agents for simulating population responses to hurricanes

  • S.I. : Ground Truth: in silico Social Science (GTIS3)
  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript


Artificial intelligence (AI) research provides a rich source of modeling languages capable of generating socially plausible simulations of human behavior, while also providing a transparent ground truth that can support validation of social-science methods applied to that simulation. In this work, we leverage two established AI representations: decision-theoretic planning and recursive modeling. Decision-theoretic planning (specifically Partially Observable Markov Decision Processes) provides agents with quantitative models of their corresponding real-world entities’ subjective (and possibly incorrect) perspectives of ground truth in the form of probabilistic beliefs and utility functions. Recursive modeling gives an agent a theory of mind, which is necessary when a person’s (again, possibly incorrect) subjective perspectives are of another person, rather than of just his/her environment. We used PsychSim, a multiagent social-simulation framework combining these two AI frameworks, to build a general parameterized model of human behavior during disaster response, grounding the model in social-psychological theories to ensure social plausibility. We then instantiated that model into alternate ground truths for simulating population response to a series of natural disasters, namely, hurricanes. The simulations generate data in response to socially plausible instruments (e.g., surveys) that serve as input to the Ground Truth program’s designated research teams for them to conduct simulated social science. The simulation also provides a graphical ground truth and a set of outcomes to be used as the gold standard in evaluating the research teams’ inferences.

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  1. Each actor’s decision-making function is invoked in an arbitrarily determined sequence.

  2. Once all actors had answered the survey, we reset the pool to be all actors.


  • Boutilier C, Poole D (1996) Computing optimal policies for partially observable decision processes using compact representations. In: Proceedings of the national conference on artificial intelligence, pp 1168–1175

  • Boutilier C, Dean T, Hanks S (1999) Decision-theoretic planning: structural assumptions and computational leverage. J Artif Intell Res 11(1):94

    Google Scholar 

  • Carley KM, Fridsma DB, Casman E, Yahja A, Altman N, Chen LC, Kaminsky B, Nave D (2006) BioWar: scalable agent-based model of bioattacks. IEEE Trans Syst Man Cybern A 36(2):252–265

    Article  Google Scholar 

  • Collins J, Ersing R, Polen A (2017) Evacuation decision-making during Hurricane Matthew: an assessment of the effects of social connections. Weather Clim Soc 9(4):769–776

    Article  Google Scholar 

  • Collins J, Ersing R, Polen A, Saunders M, Senkbeil J (2018) The effects of social connections on evacuation decision making during Hurricane Irma. Weather Clim Soc 10(3):459–469

    Article  Google Scholar 

  • Dash N, Gladwin H (2007) Evacuation decision making and behavioral responses: individual and household. Nat Hazards Rev 8(3):69–77

    Article  Google Scholar 

  • Demuth JL, Morss RE, Morrow BH, Lazo JK (2012) Creation and communication of hurricane risk information. Bull Am Meteorol Soc 93(8):1133–1145

    Article  Google Scholar 

  • Farmer AK, DeYoung SE, Wachtendorf T (2017) Pets and evacuation: an ongoing challenge in disasters. J Homel Secur Emerg Manag.

  • Gmytrasiewicz PJ, Durfee EH (1995) A rigorous, operational formalization of recursive modeling. In: Proceedings of the international conference on multi-agent systems. pp 125–132

  • Gmytrasiewicz PJ, Doshi P (2005) A framework for sequential planning in multi-agent settings. J Artif Intell Res 24:49–79

  • Goodie AS, Doshi P, Young DL (2012) Levels of theory-of-mind reasoning in competitive games. J Behav Decis Mak 25(1):95–108

    Article  Google Scholar 

  • Heath SE, Kass PH, Beck AM, Glickman LT (2001) Human and pet-related risk factors for household evacuation failure during a natural disaster. Am J Epidemiol 153(7):659–665

    Article  Google Scholar 

  • Hoey J, Little JJ (2007) Value-directed human behavior analysis from video using partially observable Markov decision processes. IEEE Trans Pattern Anal Mach Intell 29(7):1118–1132

    Article  Google Scholar 

  • Howard RA (1988) Decision analysis: practice and promise. Manag Sci 34(6):679–695

    Article  Google Scholar 

  • Howard RA, Matheson JE (eds) (1984/2005a) Influence diagrams. In: The principles and applications of decision analysis, Vol. II. Strategic Decisions Group, Menlo Park, California, 719–763. Reprinted, Decision Anal 2, 127–143.

  • Huang SK, Lindell MK, Prater CS (2016) Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environ Behav 48(8):991–1029

    Article  Google Scholar 

  • Hunt MG, Bogue K, Rohrbaugh N (2012) Pet ownership and evacuation prior to Hurricane Irene. Animals 2(4):529–539

    Article  Google Scholar 

  • Ito JY, Pynadath DV, Marsella SC (2010) Modeling self-deception within a decision-theoretic framework. J Auton Agents Multiagent Syst 20(1):3–13

    Article  Google Scholar 

  • JASSS (1998–present) The Journal of Artificial Societies and Social Simulation.

  • Kaelbling LP, Littman ML, Cassandra AR (1998) Planning and acting in partially observable stochastic domains. Artif Intell 101:99–134

    Article  Google Scholar 

  • Kim JM, Hill Jr RW, Durlach PJ, Lane HC, Forbell E, Core M, Marsella S, Pynadath D, Hart J (2009) BiLAT: a game-based environment for practicing negotiation in a cultural context. Int J Artif Intell Educ 19(3):289–308

    Google Scholar 

  • Kjaerulff U (1992) A computational scheme for reasoning in dynamic probabilistic networks. In: Proceedings of the eighth international conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., Milan, pp 121–129

  • Koller D, Milch B (2003) Multi-agent influence diagrams for representing and solving games. Games Econ Behav 45(1):181–221

    Article  Google Scholar 

  • Lazo JK, Bostrom A, Morss RE, Demuth JL, Lazrus H (2015) Factors affecting hurricane evacuation intentions. Risk Anal 35(10):1837–1857

    Article  Google Scholar 

  • Lindell MK, Perry RW (2012) The protective action decision model: theoretical modifications and additional evidence. Risk Anal 32(4):616–632

    Article  Google Scholar 

  • Lindell MK, Lu JC, Prater CS (2005) Household decision making and evacuation in response to Hurricane Lili. Nat Hazards Rev 6(4):171–179

    Article  Google Scholar 

  • Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: a multiagent simulation environment. Simulation 81(7):517–527

    Article  Google Scholar 

  • MABS (1998–present) Proceedings of the international workshop on multi-agent-based simulation.

  • Marsella SC, Pynadath DV, Read SJ (2004) PsychSim: agent-based modeling of social interactions and influence. In: Proceedings of the international conference on cognitive modeling. pp 243–248

  • McAlinden R, Pynadath D, Hill RW Jr (2014) UrbanSim: using social simulation to train for stability operations. In: Ehlschlaeger C (ed) Understanding megacities with the reconnaissance, surveillance, and intelligence paradigm, chap 10. pp 90–99

  • NOAA (2020) U.S. billion-dollar weather and climate disasters. Accessed 23 Sept 2020

  • Paruchuri P, Chakraborty N, Gordon G, Sycara K, Brett J, Adair W (2013) Inter-cultural opponent behavior modeling in a POMDP based automated negotiating agent. In: Models for intercultural collaboration and negotiation. Springer, pp 165–182

  • Polich K, Gmytrasiewicz P (2007) Interactive dynamic influence diagrams. In: Proceedings of the International joint conference on autonomous agents and multiagent systems. ACM, p 34

  • Pynadath DV, Marsella SC (2005) PsychSim: modeling theory of mind with decision-theoretic agents. In: Proceedings of the international joint conference on artificial intelligence. pp 1181–1186

  • Pynadath DV, Marsella SC (2007) Minimal mental models. In: Proceedings of the conference on artificial intelligence. pp 1038–1046

  • Pynadath DV, Rosoff H, John RS (2016) Semi-automated construction of decision-theoretic models of human behavior. In: Proceedings of the international conference on autonomous agents and multiagent systems

  • Ross S, Pineau J, Paquet S, Chaib-Draa B (2008) Online planning algorithms for POMDPs. J Artif Intell Res 32:663–704

    Article  Google Scholar 

  • Schott T, Landsea C, Hafele G, Lorens J, Taylor A, Thurm H, Ward B, Willis M, Zaleski W (2019) Saffir–Simpson hurricane wind scale., published by the NOAA. Accessed 23 Sept 2020

  • Si M, Marsella SC, Pynadath DV (2010) Modeling appraisal in theory of mind reasoning. J Auton Agents MultiAgent Syst 20(1):14–31

    Article  Google Scholar 

  • Sun R (2006) Cognition and multi-agent interaction: from cognitive modeling to social simulation. Cambridge University Press, Cambridge

  • Tatman JA, Shachter RD (1990) Dynamic programming and influence diagrams. IEEE Trans Syst Man Cybern 20(2):365–379

    Article  Google Scholar 

  • Wang N, Pynadath DV, Marsella SC (2015) Subjective perceptions in wartime negotiation. IEEE Trans Affect Comput 6(2):118–126

    Article  Google Scholar 

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This study was supported by Defense Sciences Office, DARPA [Grant No. HR00111820004].

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Correspondence to David V. Pynadath.

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Pynadath, D.V., Dilkina, B., Jeong, D.C. et al. Disaster world. Comput Math Organ Theory 29, 84–117 (2023).

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