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

TL;DR

This paper introduces a Prospect Theory-based model to understand and influence individual decision-making during a pandemic, accounting for irrational behaviors and proposing a behavior inducement algorithm validated through simulations and user tests.

Contribution

It develops a novel Prospect Theory-based framework for modeling irrational decision-making in epidemics and proposes an effective behavior inducement algorithm to control disease spread.

Findings

Irrationality leads to more conservative behavior at low risk levels.

Irrationality causes more risk-seeking behavior at high risk levels.

The proposed algorithm effectively guides user behavior in simulations and tests.

Abstract

It is critical to understand and model the behavior of individuals in a pandemic, as well as identify effective ways to guide people's behavior in order to better control the epidemic spread. However, current research fails to account for the impact of users' irrationality in decision-making, which is a prevalent factor in real-life scenarios. Additionally, existing disease control methods rely on measures such as mandatory isolation and assume that individuals will fully comply with these policies, which may not be true in reality. Thus, it is critical to find effective ways to guide people's behavior during an epidemic. To address these gaps, we propose a Prospect Theory-based theoretical framework to model individuals' decision-making process in an epidemic and analyze the impact of irrationality on the co-evolution of user behavior and the epidemic. Our analysis shows that irrationality can lead individuals to be more conservative when the risk of being infected is small, while irrationality tends to make users be more risk-seeking when the risk of being infected is high. We then propose a behavior inducement algorithm to guide user behavior and control the spread of disease. Simulations and real user tests validate our proposed model and analysis, and simulation results show that our proposed behavior inducement algorithm can effectively guide users' behavior.