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

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

Probe: Learning Users' Personalized Projection Bias in Intertemporal Bundle Choices

Intertemporal choices involve making decisions that require weighing the costs in the present against the benefits in the future. One specific type of intertemporal choice is the decision between purchasing an individual item or opting for a bundle that includes that item. Previous research assumes that individuals have accurate expectations of the factors involved in these choices. However, in reality, users' perceptions of these factors are often biased, leading to irrational and suboptimal decision-making. In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect. To address these biases, we propose a novel bias-embedded preference model called Probe. The Probe incorporates a weight function to capture users' projection bias and a value function to account for the reference-point effect, and introduce prospect theory from behavioral economics to combine the weight and value functions. This allows us to determine the probability of users selecting the bundle or a single item. We provide a thorough theoretical analysis to demonstrate the impact of projection bias on the design of bundle sales strategies. Through experimental results, we show that the proposed Probe model outperforms existing methods and contributes to a better understanding of users' irrational behaviors in bundle purchases. This investigation can facilitate a deeper comprehension of users' decision-making mechanisms, enable the provision of personalized services, and assist users in making more rational and optimal decisions

Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals

Choice problems refer to selecting the best choices from several items, and learning users' preferences in choice problems is of great significance in understanding the decision making mechanisms and providing personalized services. Existing works typically assume that people evaluate items independently. In practice, however, users' preferences depend on the market in which items are placed, which is known as context effects; and the order of users' preferences for two items may even be reversed, which is referred to preference reversals. In this work, we identify three factors contributing to context effects: users' adaptive weights, the inter-item comparison, and display positions. We propose a context-dependent preference model named Pacos as a unified framework for addressing three factors simultaneously, and consider two design methods including an additive method with high interpretability and an ANN-based method with high accuracy. We study the conditions for preference reversals to occur and provide an theoretical proof of the effectiveness of Pacos in addressing preference reversals. Experimental results show that the proposed method has better performance than prior works in predicting users' choices, and has great interpretability to help understand the cause of preference reversals.Comment: 29 pages, 12 figure

Modeling and Analysis of the Epidemic-Behavior Co-evolution Dynamics with User Irrationality

During a public health crisis like COVID-19, individuals' adoption of protective behaviors, such as self-isolation and wearing masks, can significantly impact the spread of the disease. In the meanwhile, the spread of the disease can also influence individuals' behavioral choices. Moreover, when facing uncertain losses, individuals' decisions tend to be irrational. Therefore, it is critical to study individuals' irrational behavior choices in the context of a pandemic. In this paper, we propose an epidemic-behavior co-evolution model that captures the dynamic interplay between individual decision-making and disease spread. To account for irrational decision-making, we incorporate the Prospect Theory in our individual behavior modeling. We conduct a theoretical analysis of the model, examining the steady states that emerge from the co-evolutionary process. We use simulations to validate our theoretical findings and gain further insights. This investigation aims to enhance our understanding of the complex dynamics between individual behavior and disease spread during a pandemic

Modeling Information Acquisition and Social Learning Dynamics: A Rational Inattention Perspective

Social learning, a fundamental process through which individuals shape their beliefs and perspectives via observation and interaction with others, is critical for the development of our society and the functioning of social governance. Prior works on social learning usually assume that the initial beliefs are given and focus on the update rule. With the recent proliferation of online social networks, there is an avalanche amount of information, which may significantly influence users' initial beliefs. In this paper, we use the rational inattention theory to model how agents acquire information to form initial beliefs and assess its influence on their adjustments in beliefs. Furthermore, we analyze the dynamic evolution of belief distribution among agents. Simulations and social experiments are conducted to validate our proposed model and analyze the impact of model parameters on belief dynamics.Comment: 10 pages, 6 figures, submitted to ICASSP 202

Opinion Dynamics in Two-Step Process: Message Sources, Opinion Leaders and Normal Agents

According to mass media theory, the dissemination of messages and the evolution of opinions in social networks follow a two-step process. First, opinion leaders receive the message from the message sources, and then they transmit their opinions to normal agents. However, most opinion models only consider the evolution of opinions within a single network, which fails to capture the two-step process accurately. To address this limitation, we propose a unified framework called the Two-Step Model, which analyzes the communication process among message sources, opinion leaders, and normal agents. In this study, we examine the steady-state opinions and stability of the Two-Step Model. Our findings reveal that several factors, such as message distribution, initial opinion, level of stubbornness, and preference coefficient, influence the sample mean and variance of steady-state opinions. Notably, normal agents' opinions tend to be influenced by opinion leaders in the two-step process. We also conduct numerical and social experiments to validate the accuracy of the Two-Step Model, which outperforms other models on average. Our results provide valuable insights into the factors that shape social opinions and can guide the development of effective strategies for opinion guidance in social networks

Individual Behavior Modeling and Transmission Control During Disease Spread: A Review

In this paper, we provide a detailed review of two categories of the literature: the spontaneous protective behaviors of individuals during disease spread and the mandatory measures to control the disease spread. In the literature, the models of individual protective behaviors can be divided into two parts: the environment-induced protective behaviors and the information-induced protective behaviors. And the mandatory measures of disease control can be divided into two parts: the macro-based control methods and the micro-based control methods. We provide a detailed review to the various categories of research. Then we compare the effects of different control methods through simulation. Among the micro-based control methods, the method based on minimizing the largest eigenvalue has the best effect. This review is of crucial importance to summarize the studies of the spontaneous protective behaviors during disease spread and the mandatory measures to control the disease spread