Modeling Human Decision-Making during Hurricanes: From Model to Data Collection to Prediction

Yongsatianchot, N. and Marsella, S. (2019) Modeling Human Decision-Making during Hurricanes: From Model to Data Collection to Prediction. In: 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '19), Montreal, QC, Canada, 13-17 May 2019, pp. 2294-2296.

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Hurricanes are devastating natural disasters. To effectively plan to help people at risk during a hurricane, a model of human decision-making is needed to predict people's decisions and to potentially identify ways to influence those decisions. In this work, we propose a generative model of human decision making based on a Markov Decision Process where we explicitly model concerns, risk perception, and information. As a first step toward evaluating the model, the work presented here focuses on one step of the decision part of the model. We created a questionnaire based on the model and collect data from 2018 Hurricanes, Florence and Michael. The results show that, across hurricane data-sets that we collected, the features of the models correlate well with evacuation decisions and our model outperforms existing methods in most cases, demonstrating the validity of the proposed model.

Item Type:Conference Proceedings
Additional Information:The work is supported by the National Science Foundation under grant no. CMMI-1638234
Glasgow Author(s) Enlighten ID:Yongsatianchot, Nutchanon and Marsella, Professor Stacy
Authors: Yongsatianchot, N., and Marsella, S.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
College of Science and Engineering

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