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Research on the improvement of national park recreation policies has attracted much attention to discrete choice experiments to obtain tourists' preferences and willingness to pay. However, individual choice behavior is extremely complex, and the single Random Utility Maximization (RUM) model ignores anticipated regret and is insufficient to explain individuals' actual choice behavior. To investigate whether regret influences tourists' choices regarding the improvement of national park recreation attributes, this study introduces the Random Regret Minimization (RRM) model and explores the performance of polynomial logit models and hybrid latent class models in analyzing discrete choice models based on utility and regret. By constructing a hybrid utility-regret model, we examine how tourists trade off between attributes such as vegetation coverage, water clarity, amount of litter, and level of crowding in national park recreation. Results indicate that the RRM model has better goodness-of-fit and predictive ability than the RUM model, indicating that regret is a significant choice paradigm, and the hybrid model better explains respondents' choices. Specifically, 62.5% of tourists' choices are driven by regret, and regret-driven respondents are more inclined to increase vegetation coverage and improve water clarity, while utility-driven respondents are more inclined to reduce litter and crowding. This study not only provides a reference for managers to develop more optimal recreation improvement strategies but also offers theoretical insights for national park recreation improvement policies.

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Discrete choice models are almost exclusively estimated assuming random utility maximization (RUM) is the decision rule applied by individuals. Recent studies indicate alternative behavioral assumptions may be more appropriate in health. Decision field theory (DFT) is a psychological theory of decision-making, which has shown promise in transport research. This study introduces DFT to health economics, empirically comparing it to RUM and random regret minimization (RRM) in risky health settings, namely tobacco and vaccine choices. Model fit, parameter ratios, choice shares, and elasticities are compared between RUM, RRM and DFT. Test statistics for model differences are derived using bootstrap methods. Decision rule heterogeneity is investigated using latent class models, including novel latent class DFT models. Tobacco and vaccine choice data are better explained with DFT than with RUM or RRM. Parameter ratios, choice shares and elasticities differ significantly between models. Mixed results are found for the presence of decision rule heterogeneity. We conclude that DFT shows promise as a behavioral assumption that underpins the estimation of discrete choice models in health economics. The significant differences demonstrate that care should be taken when choosing a decision rule, but further evidence is needed for generalizability beyond risky health choices.

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BACKGROUND: The COVID-19 pandemic is a public health crisis and an inspection of national governance systems and crisis response capabilities of countries globally. China has adopted a tough accountability system for officials and has succeeded in containing the spread of the pandemic. This study aimed to assess the impact of accountability on local officials' behavior in the pandemic prevention and control based on the official promotion tournament theory and utility maximization analysis framework. METHODS: The panel data of 237 Chinese cities were extracted with local officials' characteristics, confirmed cases, Baidu migration index, Baidu search index according to city names, and data were excluded with local officials' relocation or sub-provincial cities between January 1, 2020 and May 5, 2020. Promotion gain and accountability cost were constructed by adopting promotion speed indicator, and the research hypotheses were assumed based on the utility maximization. It was the first time to apply the interaction model to empirically investigate the relationship between the promotion speed of local officials and the COVID-19 confirmed cases. RESULTS: Our study showed that the promotion speed of provincial governors and mayors significantly affected the number of confirmed cases (ß = - 11.615, P < 0.01). There was a significant interaction between the promotion speeds of provincial governors and mayors (ß = - 2594.1, P < 0.01), indicating that they had a coordinated effect on the pandemic control. Additionally, mayors with different promotion speeds made a significant difference in controlling the imported cases and those who promoted faster better controlled the imported cases (ß = - 0.841, P < 0.01). Mayors with full-time postgraduate education, titles, and majors in science and engineering had a better effect on controlling the number of confirmed cases. CONCLUSIONS: Our study provides evidence that the official accountability system has played an important role in containing the pandemic, which suggests that local officials motivated by the accountability system would respond to the pandemic actively for higher utility. Furthermore, provincial governors and mayors have played a coordinated effect in pandemic control. The above evidences reveal that implementing the official accountability system could improve the government's emergency management capability and the efficiency of pandemic control. Therefore, adopting a strict accountability system could be effective in pandemic containment globally, especially in centralized countries.

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Choice models in health are almost exclusively based on the neoclassical economic paradigm of utility maximization. Recently developed choice models have captured and shown empirical support for regret minimization as an alternative decision rule. In health economics, recent applications of RRM models indicate that individuals making health-based choices may exhibit regret minimization-type behavior. In this paper, we build on this research using a more flexible model that allows for heterogeneous decision rules, separately from preference heterogeneity, and comparing it to models that assume single decision rules. We use four datasets from diverse settings in which individuals make health choices: tobacco markets, genomic testing, and HIV prevention. We found that, if a one-size-fits-all rule is applied, then utility maximization was preferable to regret minimization for these datasets. However, we also find that individuals apply varying decision rules in similar proportions in these health settings, suggesting that models for heterogeneous decision rules were needed to capture these behaviors in these settings.

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The rational investor behavior and news triggered price change assumed by the Efficient Market Hypothesis (EMH) could not explain most of asset price variances, suggesting the need for an alternative theory. The Behavioral Finance Theory (BFT) advocates those economic judgments and decisions in markets are often irrational because of systematic and predictable psychological bias. However, due to the lack of measurable investment behaviors, proponents of the efficient market hypothesis argue that irrational behavior could not be reliably identified and predicted. Here we show that the price-takers behavior gauged by the normalized excess demand (NED) can be measured and the results explain most of the variances of SP500 daily returns over eight years of available data, the remaining variances are due to price-makers behavior, an influence abstracted out by the Walrasian general equilibrium theory. The interactions between behaviors of price-takers and price-makers drive market price fluctuations. For short-term prediction, we demonstrate that detected market makers' inventory positions often lead to intraday and daily market reversals. For long-term forecasting, feedback analyses of NED and SP500 data reveal signals of looming plunges and recovery processes in 2000, 2008, and 2020 market crises.

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This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP-sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP-sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP-sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.

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Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness.

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Travel activities and travel behaviors have been greatly affected by the outbreak of Covid-19. Facing the change of individuals' travel choices, policymakers have to make an appropriate response to mitigate negative consequences. This paper aims to explore how the COVID-19 would impact travel mode choice and the intention of car purchase. The data was collected from a large-scale survey conducted in June 2020 after the highest point. Random utility maximization (RUM), random regret minimization (RRM) and generalized regret minimization (GRRM) are employed to examine the effects of various factors on mode choice behaviors. The estimation results reveal that regret aversion psychology doesn't have a dominant proportion of decision choices, even if the congested condition of the mass mobility plays a significant role in the consideration of decision-making. Combined with the statistical results from the official departments, we concluded that public transport displays a great propensity on the long trip, and meanwhile, the industry of ride-hailing services has shocked sharply. In terms of the intention of traffic tool purchase, carless people prefer to buy electric two-wheel vehicles rather than automobiles. The research findings and the contribution to policy implications give assistance to authority in understanding citizens' travel mode preferences under the impact of COVID-19.

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We show that during the weeks following the initiation of the COVID-19 pandemic, the United States equity market was inefficient. This is demonstrated by showing that utility maximizing agents over the time period ranging from mid-February to late March 2020 can generate statistically significant profits by utilizing only historical price and virus related data to forecast future equity ETF returns. We generalize Merton's optimal portfolio problem using a novel method based upon a likelihood ratio in order to construct a dynamic trading strategy for utility maximizing agents. These strategies are shown to have statistically significant profitability and strong risk and performance statistics during the COVID-19 time-frame.

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Governments of the world have invested a lot of manpower and material resources to combat COVID-19 this year. At this moment, the most efficient way that could stop the epidemic is to leverage the contact tracing system to monitor people's daily contact information and isolate the close contacts of COVID-19. However, the contact tracing data usually contains people's sensitive information that they do not want to share with the contact tracing system and government. Conversely, the contact tracing system could perform better when it obtains more detailed contact tracing data. In this article, we treat the process of collecting contact tracing data from a crowdsourcing perspective in order to motivate users to contribute more contact tracing data and propose the incentive algorithm named CovidCrowd. Different from previous works where they ask users to contribute their data voluntarily, the government offers some reward to users who upload their contact tracing data to reimburse the privacy and data processing cost. We formulate the problem as a Stackelberg game and show there exists a Nash equilibrium for any user given the fixed reward value. Then, CovidCrowd computes the optimal reward value which could maximize the utility of the system. Finally, we conduct a large-scale simulation with thousands of users and evaluation with real-world data set. Both results show that CovidCrowd outperforms the benchmarks, e.g., the user participating level is improved by at least 13.2% for all evaluation scenarios.

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Food safety remains a major issue to many consumers. Previous studies examining the economic impact of food safety recalls have focused on Class I recalls. Antibiotic residue in meat products, a Class II recall, has increased in consumer importance yet little is known about how much research and development expenditure should be allocated to reduce antibiotic residue pre- and post-harvest. This study compares demand elasticities and the decrease in willingness to pay in response to either an E. coli (Class I) or antibiotic residue (Class II) recall. We compare and contrast two competing behavioral frameworks, Random Utility and Regret Minimizing. Modeling behavior using the random regret framework is found to be more powerful for assessing consumer responses. In addition, we explore if different groups of consumers exist that either maximize utility or minimize regret. Consumer devaluations of E. coli (Class I) are 40-65% larger than antibiotic residue (Class II). Approximately 60% of consumers are identified as regret minimizers and 40% were identified as utility maximizers. While industry response and government policy recommendations differed conditional on modeling framework, the regret minimizing framework required smaller price discounts than regret minimizing to maintain the same level of market share.

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We propose a novel approach of model selection for probability estimates that may be applied in time evolving setting. Specifically, we show that any discrepancy between different probability estimates opens a possibility to compare them by trading on a hypothetical betting market that trades probabilities. We describe the mechanism of such a market, where agents maximize some utility function which determines the optimal trading volume for given odds. This procedure produces supply and demand functions, that determine the size of the bet as a function of a trading probability. These functions are closed form for the choice of logarithmic and exponential utility functions. Having two probability estimates and the corresponding supply and demand functions, the trade matching these estimates happens at the intersection of the supply and demand functions. We show that an agent using correct probabilities will realize a profit in expectation when trading against any other set of probabilities. The expected profit realized by the correct view of the market probabilities can be used as a measure of information in terms of statistical divergence.

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Classical network utility maximization (NUM) models fail to capture network dynamics, which are of increasing importance for modeling network behaviors. In this paper, we consider the NUM with delivery contracts, which are constraints to the classical model to describe network dynamics. This paper investigates a method to distributively solve the given problem. We first transform the problem into an equivalent model of linear equations by dual decomposition theory, and then use Gaussian belief propagation algorithm to solve the equivalent issue distributively. The proposed algorithm has faster convergence speed than the existing first-order methods and distributed Newton method. Experimental results have demonstrated the effectiveness of our proposed approach.

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As IP video services have emerged to be the predominant Internet application, how to optimize the Internet resource allocation, while satisfying the quality of experience (QoE) for users of video services and other Internet applications becomes a challenge. This is because the QoE perceived by a user of video services can be characterized by a staircase function of the data rate, which is nonconcave and hence it is "hard" to find the optimal operating point. The work in this paper aims at tackling this challenge. It considers the packet routing problem among multiple end points in packet switching networks based on a connectionless, hop-by-hop forwarding paradigm. We model this traffic allocation problem using a fluid flow model and let the link bandwidth be the only resource to be shared. To maximize the utilization of resources and avoid congestion, we formulate the problem as a network utility maximization problem. More precisely, the objective of this paper is to design a Fully Distributed Traffic Allocation Algorithm (FDTAA) that is applicable to a large class of nonconcave utility functions. Moreover, FDTAA runs in a fully distributed way: it enables each router to independently address and route each data unit using immediate local information in parallel, without referring to any global information of the communication network. FDTAA requires minimum computation workload, since the routing decision made at each router is solely based on the destination information carried in each unit. In addition, the network utility values corresponding to the FDTAA iterate sequence converge to the optimal network utility value at the rate of (1/K), where K is the iteration counter. These theoretical results are exemplified by the simulation performed on an example communication network.

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We explore the behavioral methodology and "revolution" in economics through the lens of medical economics. We address two questions: (1) Are mainstream economic assumptions of utility-maximization realistic approximations of people's actual behavior? (2) Do people maximize subjective expected utility, particularly in choosing from among the available options? In doing so, we illustrate-in terms of a hypothetical experimental sample of patients with dry eye diagnosis-why and how utility in pharmacoeconomic assessments might be valued differently by patients when subjective psychological, social, cognitive, and emotional factors are considered. While experimentally-observed or surveyed behavior yields stated (rather than revealed) preferences, behaviorism offers a robust toolset in understanding drug, medical device, and treatment-related decisions compared to the optimizing calculus assumed by mainstream economists. It might also do so more perilously than economists have previously understood, in light of the intractable uncertainties, information asymmetries, insulated third-party agents, entry barriers, and externalities that characterize healthcare. Behavioral work has been carried out in many sub-fields of economics. Only recently has it been extended to healthcare. This offers medical economists both the challenge and opportunity of balancing efficiency presumptions with relatively autonomous patient choices, notwithstanding their predictable, yet seemingly consistent, irrationality. Despite its comparative youth and limitations, the scientific contributions of behaviorism are secure and its future in medical economics appears to be promising.

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Based upon the Behavioral Perspective Model (BPM), previous analysis has shown that consumers tend to maximize utility as a function of the level of utilitarian (functional) and informational (social) reinforcement offered by brands. A model of consumer brand choice was developed, which applied a Cobb-Douglas utility function to the parameters that constitute the BPM, using consumer panel data. The present paper tested a variation of the previous model, which allows for measures of consumer utility at the level of aggregate household, in addition to utility per consumed product unit (e.g., gram), and examined the relations of obtained utility with consumers' social class and age. Results indicate that the model fitted the data well, generating consistent parameters, and that utility per product unit, but not total household utility, was positively correlated to social class. These findings suggest that, in the case of supermarket food items, higher-income households obtain higher levels of utility than lower-income households by purchasing brands that offer more utilitarian and informational reinforcement per product unit rather than their buying larger quantities of brands offering lower reinforcement levels.

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It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. The experiments use a no-choice/choice utility learning paradigm where the no-choice phase is used to elicit a profile of each participant's performance across the strategy space and the choice phase is used to test predicted choices within this space. They show that the majority of individuals select strategies that are boundedly optimal. Further, individual differences in what people choose to do are successfully predicted by the analysis. Two issues are discussed: (a) the performance of the minority of participants who did not find boundedly optimal adaptations, and (b) the possibility that individuals anticipate what, with practice, will become a bounded optimal strategy, rather than what is boundedly optimal during training.

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In resource-limited settings, the choice between utilizing biomedical health services and/or traditional healers is critical to the success of the public health mission. In the literature, this choice has been predicted to be influenced by three major factors: knowledge about biomedical etiologies; cultural modernization; and rational choice. The current study investigated all three of these predicted determinants, applying data from a general household survey conducted in 2010 in Zambézia Province of Mozambique involving 1045 randomly sampled rural households. Overall, more respondents (N = 802) intended to continue to supplement their biomedical healthcare with traditional healer services in comparison with those intending to utilize biomedical care exclusively (N = 243). The findings strongly supported the predicted association between rational utility (measured as satisfaction with the quality of service and results from past care) with the future intention to continue to supplement or utilize biomedical care exclusively. Odds of moving away from supplementation increase by a factor of 2.5 if the respondent reported seeing their condition improve under government/private biomedical care. Odds of staying with supplementation increase by a factor 3.1 if the respondent was satisfied with traditional care and a factor of 16 if the condition had improved under traditional care. Modernization variables (education, income, religion, and Portuguese language skills) were relevant and provided a significant component of the best scientific model. Amount of biomedical knowledge was not a significant predictor of choice. There was a small effect on choice from knowing the limitations of biomedical care. The findings have implications for public healthcare promotion activities in areas where biomedical care is introduced as an alternative to traditional healing.

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Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.

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Utility maximization is a key element of a number of theoretical approaches to explaining human behavior. Among these approaches are rational analysis, ideal observer theory, and signal detection theory. While some examples of these approaches define the utility maximization problem with little reference to the bounds imposed by the organism, others start with, and emphasize approaches in which bounds imposed by the information processing architecture are considered as an explicit part of the utility maximization problem. These latter approaches are the topic of this issue of the journal.

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