A computational model of coping and decision making in high-stress, uncertain situations: an application to hurricane evacuation decisions

Yongsatianchot, N. and Marsella, S. (2023) A computational model of coping and decision making in high-stress, uncertain situations: an application to hurricane evacuation decisions. IEEE Transactions on Affective Computing, 14(3), pp. 2539-2556. (doi: 10.1109/TAFFC.2022.3173812)

Abstract

People often encounter highly stressful, emotion-evoking situations. Modeling and predicting people's behavior in such situations, how they cope, is a critical research topic. To that end, we propose a computational model of coping that casts Lazarus' theory of coping into a Partially Observable Markov Decision Process (POMDP) framework. This includes an appraisal process that models the factors leading to stress by assessing a person's relation to the environment and a coping process that models how people seek to reduce stress by directly altering the environment or changing one's beliefs and goals. We evaluated the model's assumptions in the context of a high-stress situation, hurricanes. We collected questionnaire data from major U.S. hurricanes in 2018 to evaluate the model's features for appraisal calculation. We also conducted a series of controlled experiments simulating a hurricane experience to investigate how people change their beliefs and goals to cope with the situation. The results support the model's assumptions showing that the proposed features are significantly associated with the evacuation decisions and people change their beliefs and goals to cope with the situation.

• Item Type: Articles
• Additional Information: The work is supported by the National Science Foundation under grant no. CMMI-1638234.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Marsella, Professor Stacy and Yongsatianchot, Nutchanon
• Authors: Yongsatianchot, N., and Marsella, S.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience; College of Science and Engineering
• Journal Name: IEEE Transactions on Affective Computing
• Publisher: IEEE
• ISSN: 2371-9850
• ISSN (Online): 1949-3045
• Published Online: 10 May 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Affective Computing 14(3):2539 - 2556
• Publisher Policy: Reproduced in accordance with the publisher copyright policy