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Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Wenxiang Dong, H. Vicky Zhao*

Department of Automation, BNRist, Tsinghua University, Beijing, 100084, China

* Corresponding Author: H. Vicky Zhao. Email: email

Computer Modeling in Engineering & Sciences 2024, 140(1), 139-170. https://doi.org/10.32604/cmes.2024.047156

Abstract

Understanding and modeling individuals’ behaviors during epidemics is crucial for effective epidemic control. However, existing research ignores the impact of users’ irrationality on decision-making in the epidemic. Meanwhile, existing disease control methods often assume users’ full compliance with measures like mandatory isolation, which does not align with the actual situation. To address these issues, this paper proposes a prospect theory-based framework to model users’ decision-making process in epidemics and analyzes how irrationality affects individuals’ behaviors and epidemic dynamics. According to the analysis results, irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high. Then, this paper proposes a behavior inducement algorithm to guide individuals’ behaviors and control the spread of disease. Simulations and real user tests validate our analysis, and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’ behavior.

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APA Style
Dong, W., Zhao, H.V. (2024). Prospect theory based individual irrationality modelling and behavior inducement in pandemic control. Computer Modeling in Engineering & Sciences, 140(1), 139-170. https://doi.org/10.32604/cmes.2024.047156
Vancouver Style
Dong W, Zhao HV. Prospect theory based individual irrationality modelling and behavior inducement in pandemic control. Comput Model Eng Sci. 2024;140(1):139-170 https://doi.org/10.32604/cmes.2024.047156
IEEE Style
W. Dong and H.V. Zhao, "Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control," Comput. Model. Eng. Sci., vol. 140, no. 1, pp. 139-170. 2024. https://doi.org/10.32604/cmes.2024.047156



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