•This study examines the influence of regret aversion on asset pricing.•It proposes a regret-based capital asset pricing model.•Individuals maximize expected returns and minimize anticipated ...regrets.•A closed-form pricing formula with a regret-based beta is derived.•The proposed conceptual framework helps understand the aggregate effects of regret.
This study examines the influence of regret aversion on asset pricing by proposing a regret-based capital asset pricing model in which individuals maximize the expected returns from chosen portfolios of assets while minimizing anticipated regrets. In equilibrium, a closed-form pricing formula is derived, whereby a risky asset's excess return is proportional to its “regret beta” that measures the exposure to investors’ emotions. The market as a whole pays investors a positive “regret premium” as compensation for regret aversion. As such, this study proposes a conceptual framework to understand the aggregate effects of regret. The model indicates that employing a regret-related beta can help explain cross-sectional returns. It also implies that regret aversion is a possible reason for the flat security market line and high equity premium.
Objective: Risk beliefs are central to most theories of health behavior, yet many unanswered questions remain about an increasingly studied risk construct, anticipated regret. The authors sought to ...better understand anticipated regret's role in motivating health behaviors. Method: The authors systematically searched electronic databases for studies of anticipated regret and behavioral intentions or health behavior. They used random effects meta-analysis to synthesize effect sizes from 81 studies (n = 45,618). Results: Anticipated regret was associated with both intentions (r+ = .50, p < .001) and health behavior (r+ = .29, p < .001). Greater anticipated regret from engaging in a behavior (i.e., action regret) predicted weaker intentions and behavior, whereas greater anticipated regret from not engaging in a behavior (i.e., inaction regret) predicted stronger intentions and behavior. Anticipated action regret had smaller associations with behavioral intentions related to less severe and more distal hazards, but these moderation findings were not present for inaction regret. Anticipated regret generally was a stronger predictor of intentions and behavior than other anticipated negative emotions and risk appraisals. Conclusions: Anticipated inaction regret has a stronger and more stable association with health behavior than previously thought. The field should give greater attention to understanding how anticipated regret differs from similar constructs, its role in health behavior theory, and its potential use in health behavior interventions.
Decision makers can become trapped by
myopic regret avoidance in which rejecting feedback to avoid short-term
outcome regret (regret associated with counterfactual outcome comparisons) leads to ...reduced learning and greater long-term regret over continuing poor decisions. In a series of laboratory experiments involving repeated choices among uncertain monetary prospects, participants primed with outcome regret tended to decline feedback, learned the task slowly or not at all, and performed poorly. This pattern was reversed when decision makers were primed with
self-blame regret (regret over an unjustified decision). Further, in a final experiment in which task learning was unnecessary, feedback was more often rejected in the self-blame regret condition than in the outcome regret condition. We discuss the findings in terms of a distinction between two regret components, one associated with outcome evaluation, the other with the justifiability of the decision process used in making the choice.
Anticipated regret, the feeling that we might regret a decision in the future, has been identified as a strong predictor of vaccination behavior, and the proliferation of anticipated regret appeals ...underscores the need for the empirical study of messages that target regret. The current study evaluated the persuasiveness of narrative depictions of regret and character death on COVID-19 booster vaccine intention. Data were collected from 944 adults in a 2 (no depicted regret, depicted regret) × 2 (character survives, dies) between-participants online message experiment. Results demonstrated that depicting regret had a positive effect on booster vaccine intention, especially among Republicans. Moderated serial mediation analysis supported a model where depicted regret had a positive effect on booster vaccine intention via audience replotting of story events and anticipated regret. While this persuasive process occurred for both Republicans and Democrats, the pathway was stronger for Republicans. Additionally, messages depicting character death produced greater anticipated regret. We discuss the theoretical and practical implications of these results.
•Regret tweets resulted in greater anticipated regret compared to the tweets without.•Regret tweets prompted thoughts of alternative storylines (i.e., replotting).•Replotting was related to greater anticipated regret, and vaccine intention.•These relationships were stronger for Republicans compared to Democrats.
This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to allocate a binary ...treatment to members of a population, based on their observable characteristics. We model this problem as a statistical decision problem where the policy maker must choose a subset of the covariate space to assign to treatment, out of a class of potential subsets. We focus on settings in which the policy maker may want to select amongst a collection of constrained subset classes: examples include choosing the number of covariates over which to perform best-subset selection, and model selection when approximating a complicated class via a sieve. We adapt and extend results from statistical learning to develop the Penalized Welfare Maximization (PWM) rule. We establish an oracle inequality for the regret of the PWM rule which shows that it is able to perform model selection over the collection of available classes. We then use this oracle inequality to derive relevant bounds on maximum regret for PWM. An important consequence of our results is that we are able to formalize model-selection using a “holdout” procedure, where the policy maker would first estimate various policies using half of the data, and then select the policy which performs the best when evaluated on the other half of the data.
Permanent makeup (PMU) is a popular form of tattooing used to replace or enhance the use of daily makeup. The purpose of this literature review is to provide an overview of PMU, with a particular ...focus on its use, regulation, and its potential complications reported in the literature. In the United States, there is significant variation in the regulation and training required to perform PMU. Adverse outcomes of PMU include infectious, allergic, and inflammatory complications. These complications may be more common if proper hygiene and aftercare practices are not followed. Cosmetically, PMU may shift or may have an altered appearance if underlying skin is treated with cosmetic fillers or local anesthetics. Given the popularity of PMU and its cosmetic uses, dermatologists should be aware of the PMU industry, potential complications, and how best to manage complications.
Capsule Summary:■ Permanent makeup (PMU), also called “cosmetic tattooing”, “permanent cosmetics”, or “micropigmentation,” is a form of tattooing to replace or enhance the use of daily makeup.■ The oversight of PMU consists of a patchwork of state-based regulations.■ The most common complications of PMU are patient dissatisfaction and regret.
There are many factors affecting the social propagation of adoption behavior, and the traditional propagation model is not enough to study the influence of human psychological factors. We introduce ...the psychological mechanism of regret and hesitation to explain the complexity of individual decision making and information propagation. In this paper, a method based on edge division theory is used to analyze the propagation process on multiple networks, and the experimental results show the phenomenon of mixed phase transition. When the system is dominated by regret psychology, the system can achieve global eruption, but when it is dominated by hesitation psychology, the system cannot achieve global eruption. The results of theoretical analysis and experimental simulation agree well. It is of great significance to the study of sociology, psychology and information propagation.
•The impact of a new regret-hesitation psychology on behavioral propagation.•The adoption threshold function rises firstly, then falls, then remains unchanged, and finally rises again.•The propagation process shows a cross phase transition.
In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other ...functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.
Min–max and min–max regret criteria are commonly used to define robust solutions. After motivating the use of these criteria, we present general results. Then, we survey complexity results for the ...min–max and min–max regret versions of some combinatorial optimization problems: shortest path, spanning tree, assignment, min cut, min
s–
t cut, knapsack. Since most of these problems are
NP-hard, we also investigate the approximability of these problems. Furthermore, we present algorithms to solve these problems to optimality.
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