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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

Abstract

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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Notes

  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.

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Funding

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

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Authors and Affiliations

  1. University of Southern California Institute for Creative Technologies, Los Angeles, USA

    David V. Pynadath & Chirag Merchant

  2. University of Southern California, Los Angeles, USA

    Bistra Dilkina, David C. Jeong, Richard S. John, Lynn C. Miller & Stephen J. Read

  3. Santa Clara University, Santa Clara, USA

    David C. Jeong

  4. University of Glasgow, Glasgow, UK

    Stacy C. Marsella

Authors
  1. David V. Pynadath
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  2. Bistra Dilkina
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  3. David C. Jeong
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  4. Richard S. John
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  5. Stacy C. Marsella
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  6. Chirag Merchant
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  7. Lynn C. Miller
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  8. Stephen J. Read
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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). https://doi.org/10.1007/s10588-022-09359-y

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  • DOI: https://doi.org/10.1007/s10588-022-09359-y

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