Probability Weighting Functions

al‐Nowaihi, Ali; Dhami, Sanjit

doi:10.1002/9780470400531.eorms0681

Cited by 15 publications

(10 citation statements)

References 45 publications

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“…The BT-based MCDM method of recalculating the criteria weights was used in a study where the quality of a school's classes was assessed. MCDM methods such as SAW, TOPSIS, EDAS, and COPRAS were used for the evaluation [2], including the combination of Fuzzy Theory [7,10,[15][16][17][19][20][21].…”

Section: Methods Of Selecting Multi-criteria Decision Making For Contractor Selectionmentioning

confidence: 99%

“…CCPT modifies the curves of low probability and high probability in CPT [15]. The Prelec function, which is presented in the middle part of Figure 5, is usually not conformed to in BT and other relevant theories of probability in which uncertain results are expected [9].…”

Section: Derivation Of the Bayesian Probability Weight Functionmentioning

confidence: 96%

“…For example, when investors aim to predict the future movements of a stock's price by referencing its price history, they do not consider the possibility that the history is the result of pure randomness and thus may indicate nothing meaningful. Consequently, Ali and Sanjit [15] proposed composite cumulative prospect theory (CCPT) to modify the curves of low and high probabilities in CPT. Similar to studies using BT, the research analyzed a high-probability zone.…”

Section: Introductionmentioning

confidence: 99%

“…CCPT: Ali and Dhami [15] (pp. 14-16) proposed a probability weighting function which uses the composite Prelec probability weighting function (CPF) [49] to correct the curve function of high-and low-probability zones.…”

mentioning

confidence: 99%

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Multi-Criteria Decision Making of Contractor Selection in Mass Rapid Transit Station Development Using Bayesian Fuzzy Prospect Model

2020

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In Taiwan, the most advantageous tender in governmental procurement is the selection of a general contractor based on a score or ranking evaluated by a committee. Due to personal, subjective preferences, the contractor selection of committee members may be different, causing cognitive difference between the results of the members’ selection and the preliminary opinions provided by the working group. Integrated, multi-criteria decision making techniques, combined with preference relation, Bayesian, fuzzy utility, and prospect theories are used to assess factors weighing up the duration/cost/quality, probability of external information, and utility function system. The paper proposes a Bayesian fuzzy prospect model for group decision making, based on probability and utility multiplied relation, and taking the sustainable development factors into consideration. This study aims to provide committees with an objective model to select the best contractor for public construction projects. The results of this study can avoid the lowest bidder being selected; besides, the score gap of contractor selection can be increased, and the difference between the top three contractors’ scores can be decreased as well. In addition to proposing an innovative decision-making system of contractor selection and an index weight-assessing system for sustainable development, this model will be widely applied and sustainably updated for other cases.

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Section: Methods Of Selecting Multi-criteria Decision Making For Contractor Selectionmentioning

confidence: 99%

Section: Derivation Of the Bayesian Probability Weight Functionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-Criteria Decision Making of Contractor Selection in Mass Rapid Transit Station Development Using Bayesian Fuzzy Prospect Model

2020

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“…17 See Kahneman and Tversky (1979), Starmer (2000), Wakker (2010) and alNowaihi and Dhami (2011b). 18 Readers interested in the axiomatic developments can follow up the references in Wakker (2010).…”

Section: Remark 1 An Outcome Is In the Domain Of Gains If X I ≥ 0 Anmentioning

confidence: 98%

An extension of the Becker proposition to non-expected utility theory

Dhami

al‐Nowaihi

2013

Mathematical Social Sciences

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a b s t r a c tIn a seminal paper, Becker (1968) showed that the most efficient way to deter crime is to impose the severest possible penalty (to maintain adequate deterrence) with the lowest possible probability (to economize on costs of enforcement). We shall call this the Becker proposition (BP). The BP is derived under the assumptions of expected utility theory (EU). However, EU is heavily rejected by the evidence. A range of non-expected utility theories have been proposed to explain the evidence. The two leading alternatives to EU are rank dependent utility (RDU) and cumulative prospect theory (CP). The main contributions of this paper are: (1) we formalize the BP in a more satisfactory manner; (2) we show that the BP holds under RDU and CP; and (3) we give a formal behavioral approach to crime and punishment that could have applicability to a wide range of problems in the economics of crime.

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Quantifiers, Questions and Quantum Physics

Kolak¹,

Symons²

2004

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Probability Weighting Functions

Cited by 15 publications

References 45 publications

Multi-Criteria Decision Making of Contractor Selection in Mass Rapid Transit Station Development Using Bayesian Fuzzy Prospect Model

Multi-Criteria Decision Making of Contractor Selection in Mass Rapid Transit Station Development Using Bayesian Fuzzy Prospect Model

An extension of the Becker proposition to non-expected utility theory

Quantifiers, Questions and Quantum Physics

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