Disaster world

David V. Pynadath (), Bistra Dilkina (), David C. Jeong (), Richard S. John (), Stacy C. Marsella (), Chirag Merchant (), Lynn C. Miller () and Stephen J. Read ()
Additional contact information
David V. Pynadath: University of Southern California Institute for Creative Technologies
Bistra Dilkina: University of Southern California
David C. Jeong: University of Southern California
Richard S. John: University of Southern California
Stacy C. Marsella: University of Glasgow
Chirag Merchant: University of Southern California Institute for Creative Technologies
Lynn C. Miller: University of Southern California
Stephen J. Read: University of Southern California

Computational and Mathematical Organization Theory, 2023, vol. 29, issue 1, No 4, 84-117

Abstract: 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.

Keywords: Social simulation; Decision theory; Partially observable Markov decision processes (POMDPs); Multiagent-based simulation; Disaster response (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10588-022-09359-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comaot:v:29:y:2023:i:1:d:10.1007_s10588-022-09359-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10588

DOI: 10.1007/s10588-022-09359-y

Access Statistics for this article

Computational and Mathematical Organization Theory is currently edited by Terrill Frantz and Kathleen Carley

More articles in Computational and Mathematical Organization Theory from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().