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Cumulative Prospect Theory (CPT), the leading behavioral account of decisionmaking under uncertainty, avoids the dominance violations implicit in Prospect Theory (PT) by assuming that the probability weight applied to a given outcome depends on its ranking. We devise a simple and direct nonparametric method for measuring the change in relative probability weights resulting from a change in payoff ranks. We find no evidence that these weights are even modestly sensitive to ranks. Conventional calibrations of CPT preferences imply that the percentage change in probability weights should be an order of magnitude larger than we observe. It follows either that probability weighting is not rank-dependent, or that the weighting function is nearly linear. Nonparametric measurement of the change in relative probability weights resulting from changes in probabilities rules out the second possibility. Additional tests nevertheless indicate that the dominance patterns predicted by PT do not arise. We reconcile these findings by positing a form of complexity aversion that generalizes the well-known certainty effect.

The Probabilistic Hesitant Fuzzy Sets (PHFS) based on the hesitant fuzzy sets has been paid great attention. Though numerous methods have been applied in this environment since the PHFS has been introduced, there are still new fields to be explored. The EDAS method which is the abbreviation of the evaluation based on distance from average solution is one of the practical methods in circumstances which is with contradictory attributes. Considering the uncertain character of the PHF condition and the psychological factors which influence decision makers’ behaviors such as the character and risk reference, the probabilistic hesitant fuzzy EDAS integrating with cumulative prospect theory (PHF-CPT-EDAS) is built for multiple attributes group decision making (MAGDM) problem. Meanwhile, the information of entropy is also utilized to calculate the unknown weighting vector of attributes. At last, we utilize two case studies to compare the designed method with other MADM methods. Through this article, we learn that the PHF-CPT-EDAS method is effective and stable to solve the MAGDM issues.

In rent years, commercial insurance selection is a hot issue in multiple attribute group decision making (MAGDM). Spherical fuzzy sets (SFSs) can better express the vague factors to commercial insurance selection. In this paper, spherical fuzzy TODIM approach based on cumulative prospect theory (SF-CPT-TODIM) is presented for MAGDM issues. It can display the psychological perception of decision makers (DMs) very well. Furthermore, when the attribute weights are unknown, we obtain the attribute weights through CRITIC method under SFSs to heighten the reasonability of weight information. Finally, this article gives a practical example of the raised approach for the hot issue about commercial insurance selection to certify the availability and superiority for the raised method via comparing with some existing approaches.

Purpose This research paper aims to explore the concerns and determinants of travelers’ behavior toward ChatGPT in the hospitality and tourism context. It also examines the weight of risk factors versus that of motivation and innovation characteristics influencing travelers’ approach behaviors toward ChatGPT. Design/methodology/approach A cumulative prospect theory was used to determine travelers’ responses to ChatGPT. This study, using a fuzzy-set qualitative approach, explored risk, motivation and innovation factors as determinants of approach behaviors for ChatGPT. Findings Findings revealed that risk, motivation and innovation factors were the key triggers of approach behaviors for ChatGPT. An intricate combination effect of the perceived risk, motivation and innovation characteristics was found, and the necessary predictors were determined. Practical implications The findings of this study will expand our current knowledge and offer practical insights for the development of ChatGPT in the hospitality and tourism sector. Originality/value This study makes a significant contribution to the existing literature by providing a nuanced understanding of the intricate interplay between the various factors that shape customer behavior in the context of technology adoption in hospitality and tourism studies.

We formulate and carry out an analytical treatment of a single-period portfolio choice model featuring a reference point in wealth, S-shaped utility (value) functions with loss aversion, and probability weighting under Kahneman and Tversky's cumulative prospect theory (CPT). We introduce a new measure of loss aversion for large payoffs, called the large-loss aversion degree (LLAD), and show that it is a critical determinant of the well-posedness of the model. The sensitivity of the CPT value function with respect to the stock allocation is then investigated, which, as a by-product, demonstrates that this function is neither concave nor convex. We finally derive optimal solutions explicitly for the cases in which the reference point is the risk-free return and those in which it is not (while the utility function is piecewise linear), and we employ these results to investigate comparative statics of optimal risky exposures with respect to the reference point, the LLAD, and the curvature of the probability weighting. This paper was accepted by Wei Xiong, finance.

Failure mode and effect analysis (FMEA) method is widely utilized as a powerful reliability management tool to effectively evaluate and prevent risk problems that occur in all aspects of production, service and transportation. Since FMEA experts have different professional backgrounds and the particularity of the risk assessment environment, they may show different risk attitudes and bounded rational behavior. Thus, this paper develops an FMEA framework based on the dynamic reference point cumulative prospect theory considering risk attitude. The linguistic distribution assessment (LDA) and linguistic scale function are utilized to indicate the risk attitude based personalized FMEA experts’ evaluation information. Further, based on the idea of maximizing deviation and the LDA-EMD (earth mover’s distance) formula, the criteria weight determination method considering the different risk attitudes of the FMEA expert is constructed. Then, a dynamic reference point cumulative prospect theory considering different risk attitudes is developed to obtain the prospect value of the failure modes (FMs) under each FMEA expert. The comprehensive expert weight determination method is established which takes the subjective and objective aspects of expert evaluation and revision factor into account. Finally, the numerical example is carried out to verify the validity and superiority performance of the proposed FMEA method. The improved FMEA method can enhance the flexibility and reliability of risk assessment. Findings proved that it is necessary to consider the different and dynamic risk attitudes of experts in the practical risk assessment.

The three-way decision method is a new developing uncertain decision theory. The method divides a set of decision alternatives into three regions, called the acceptance, rejection, and uncertainty regions constructed from a pair of thresholds. However, seldom researches about three-way decisions take psychological attitudes and preferences of the decision-makers into consideration. In this paper, we propose a novel three-way group decision method, which considers the psychological attitudes from the decision-maker. Firstly, we provide the concept of three-way group decisions. The three-way approximation is constructed by many decision-makers with respect to a single critical value. The objects are still divided into acceptance, rejection, and uncertainty regions, respectively. Then, for the proper region (uncertain region, in general), we propose a decision method, which considers the psychological preferences through fusing prospect theory into three-way group decision methods. The alternatives in the proper region are ranked in accordance with their weighted prospect values. Finally, the optimal choices are made. The decision steps are presented in detail, and the practicality of the proposed methods is illustrated through an example.

When people make risky choices, two kinds of information are crucial: outcome values and outcome probabilities. Here, we demonstrate that the juncture at which value and probability information is provided has a fundamental effect on choice. Across four experiments involving 489 participants, we compared two decision-making scenarios: one in which value information was revealed during sampling (standard) and one in which value information was revealed after sampling (value ignorance). On average, participants made riskier choices when value information was provided after sampling. Moreover, parameter estimates from a hierarchical Bayesian implementation of cumulative-prospect theory suggested that participants overweighted rare events when value information was absent during sampling but did not overweight such events in the standard condition. This suggests that the impact of rare events on choice relies crucially on the timing of probability and value integration. We provide paths toward mechanistic explanations of our results based on frameworks that assume different underlying cognitive architectures.

We study the effects of time pressure on risky decisions for pure gain prospects, pure loss prospects, and mixed prospects involving both gains and losses. In two experiments we find that time pressure has no effect on risk attitudes for gains, but increases risk aversion for losses. For mixed prospects, subjects become simultaneously more loss averse and more gain seeking under time pressure, depending on the framing of the prospects. The results suggest the importance of aspiration levels, and thus the overall probability to break even, under time pressure. We discuss the implications of our findings for decision-making situations that involve time pressure. This paper was accepted by Uri Gneezy, behavioral economics.

Whereas original prospect theory was introduced over 40 years ago, its formula for multi-outcome prospects has never yet been published, resulting in many misunderstandings. This note provides that formula.