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.

Abstract

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
• Status: Published
• Refereed: Yes
• 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