Correcting expected utility for comparisons between alternative outcomes: A unified parameterization of regret and disappointment

Laciana, Carlos E.; Weber, Elke U.

doi:10.1007/s11166-007-9027-4

Cited by 62 publications

(43 citation statements)

References 32 publications

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“…Assume A and B are two choices whose utility consequence are x 1 and x 2 . If we take x 2 as a reference point associated with A, then the regret-rejoice function is represented as follows [24,25]:…”

Section: Regret Theorymentioning

confidence: 99%

“…Later on, based on regret theory, some tools for measuring regret or rejoice degrees are proposed subsequently [23][24][25]. Regret-rejoice function is a key point in regret theory.…”

Section: Regret Theorymentioning

confidence: 99%

“…If we take 2 x as a reference point associated with A , then the regret-rejoice function is represented as follows [24,25]: From regret-rejoice function above, some interesting characteristics are summarized as follows: Property 1. The regret-rejoice function R(∆x) is a monotonically increasing concave function, with R (∆x) > 0 and R (∆x) < 0.…”

Section: Regret Theorymentioning

confidence: 99%

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Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings

2018

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Abstract:With the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to trade online. Considering the decision makers are bounded rational, this paper proposes a novel decision support model for product selection based on online ratings, in which the regret aversion behavior of consumers is formulated. Massive online ratings provided by experienced consumers for alternative products associated with several evaluation attributes are obtained by software finders. Then, the evaluations of alternative products in format of stochastic variables are conducted. To select a desirable alternative product, a novel method is introduced to calculate gain and loss degrees of each alternative over others. Considering the regret behavior of consumers in the product selection process, the regret and rejoice values of alternative products for consumer are computed to obtain the perceived utility values of alternative products. According to the prior order of the evaluation attributes provided by the consumer, the prior weights of attributes are determined based on the perceived utility values of alternative products. Furthermore, the overall perceived utility values of alternative products are obtained to generate a ranking result. Finally, a practical example from Zol.com.cn for tablet computer selection is used to demonstrate the feasibility and practically of the proposed model.

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Section: Regret Theorymentioning

confidence: 99%

“…Later on, based on regret theory, some tools for measuring regret or rejoice degrees are proposed subsequently [23][24][25]. Regret-rejoice function is a key point in regret theory.…”

Section: Regret Theorymentioning

confidence: 99%

Section: Regret Theorymentioning

confidence: 99%

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Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings

2018

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“…The emergence of Allais Paradox and Ellsberg Paradox proves that the Expected Utility Theory has a big loophole in derivation of theoretical model. Laciana and Weber [21] introduce the Regret and Disappointment Theory into expected utility function, which explain the Allais Paradox and Ellsberg Paradox. Generally speaking, the main problem of Expected Utility Theory is lacking practical application.…”

Section: The Expected Utility Theory and Mean-variance Modelmentioning

confidence: 99%

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

2017

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Abstract:The portfolio optimization problem is the central problem of modern economics and decision theory; there is the Mean-Variance Model and Stochastic Dominance Model for solving this problem. In this paper, based on the second order stochastic dominance constraints, we propose the improved biogeography-based optimization algorithm to optimize the portfolio, which we called εBBO. In order to test the computing power of εBBO, we carry out two numerical experiments in several kinds of constraints. In experiment 1, comparing the Stochastic Approximation (SA) method with the Level Function (LF) algorithm and Genetic Algorithm (GA), we get a similar optimal solution by εBBO in [0, 0.6] and [0, 1] constraints with the return of 1.174% and 1.178%. In [−1, 2] constraint, we get the optimal return of 1.3043% by εBBO, while the return of SA and LF is 1.23% and 1.26%. In experiment 2, we get the optimal return of 0.1325% and 0.3197% by εBBO in [0, 0.1] and [−0.05, 0.15] constraints. As a comparison, the return of FTSE100 Index portfolio is 0.0937%. The results prove that εBBO algorithm has great potential in the field of financial decision-making, it also shows that εBBO algorithm has a better performance in optimization problem.

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“…21 Delquie and Cillo (2006). 22 Laciana and Weber (2008). 23 In contrast to the other literature, the reference outcome with respect to disappointment in Gollier and Muermann (2010) is optimally chosen by the individual by trading off anticipatory utility against disappointment.…”

Section: Introductionmentioning

confidence: 99%

Disappointment and the Optimal Insurance Contract

Huang

Shih

Tzeng

2012

Geneva Risk Insur Rev

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This paper studies the optimal insurance contract under disappointment theory. We show that, when the individuals anticipate disappointment, there are two types of optimal insurance contract. The first type contains a deductible and a coinsurance above the deductible. We find that zero marginal cost is just a sufficient but not a necessary condition for a zero deductible. The second type has no deductible and the optimal insurance starts with full coverage for small losses and includes a coinsurance above an upper value of the full coverage. The Geneva Risk and Insurance Review (2012) 37, 258-284. doi:10.1057/grir.2012.2; published online 31 July 2012Keywords: disappointment; optimal insurance contract; deductible; coinsurance IntroductionSince Borch 1 , many researchers have studied the optimal risk sharing rules between individuals and insurance companies. A well-established result comes from Arrow's 2 findings. He showed that full coverage will be optimal when the insurance companies are risk neutral and the premium is actuarially fair. He also found that if the premium includes an insurance loading, then the optimal insurance contract will contain a deductible and a coinsurance above the deductible. Raviv 3 adopted the assumption of risk-averse insurers, solved the optimal insurance contract, and found the necessary and sufficient conditions for the existence of a deductible. He showed that the deductible is positive if and only if the marginal cost of insurance is positive.1 Borch (1962). 2 Arrow (1971). 3 Raviv (1979). The Geneva Risk and Insurance Review, 2012, 37, (258-284) r 2012 The International Association for the Study of Insurance Economics 1554-964X/12 www.palgrave-journals.com/grir/ Following Raviv's 3 paper, many researchers have studied optimal insurance contracts in alternative situations. For example, Schlesinger 4 and Gollier and Schlesinger 5 examined the optimal level of a deductible. Gollier and Breuer 6 discussed the optimal insurance contract by relaxing the non-negativity constraint. They found the conditions for a contract with negative coverage being optimal. Huberman et al. and Garratt and Marshall 7 respectively found that a contract with an upper-limit of indemnity could be optimal when the individual is able to file for bankruptcy and the insured has the option to convert the insured property. In addition, Gollier and Eeckhoudt et al. 8 indicated that a fixed reimbursement contract will be optimal if the indemnity can only be contingent upon an approximate loss amount, while Huang and Tzeng 9 found that a fixed reimbursement contract could be optimal if the insured commodities are irreplaceable. Furthermore, Dana and Scarsini 10 discussed optimal risk sharing under the assumption that individuals face a non-insurable background risk in addition to an insurable risk. They found that the correlation between the non-insurable and insurable risk is crucial to the form of the optimal insurance contract.Although the literature on the optimal insurance contract has provided many findi...

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Correcting expected utility for comparisons between alternative outcomes: A unified parameterization of regret and disappointment

Abstract: Regret, Disappointment, Alternative outcomes, D8, D81,

Cited by 62 publications

References 32 publications

Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings

Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

Disappointment and the Optimal Insurance Contract

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