Reconciling introspective utility with revealed preference: Experimental arguments based on prospect theory

Abdellaoui, Mohammed; Barrios, Carolina; Wakker, Peter P.

doi:10.1016/j.jeconom.2006.05.025

Cited by 135 publications

(113 citation statements)

References 72 publications

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“…These functions often have a slightly inferior fit. Their properties are described extensively in Abdellaoui et al (2007a). Unfortunately, a commonly accepted definition of loss aversion does not exist in the literature (Abdellaoui et al 2007b).…”

Section: Parametric Specificationsmentioning

confidence: 99%

A parametric analysis of prospect theory’s functionals for the general population

2009

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This article presents the results of an experiment that completely measures the utility function and probability weighting function for different positive and negative monetary outcomes, using a representative sample of N = 1,935 from the general public. The results confirm earlier findings in the lab, suggesting that utility is less pronounced than what is found in classical measurements where expected utility is assumed. Utility for losses is found to be convex, consistent with diminishing sensitivity, and the obtained loss-aversion coefficient of 1.6 is moderate but in agreement with contemporary evidence. The estimated probability weighting functions have an inverse-S shape and they imply pessimism in both domains. These results show that probability weighting is also an important phenomenon in the general population. Women and lower educated individuals are found to be more risk averse, in agreement with common findings. In contrast to previous studies that ascribed gender differences in risk attitudes solely to differences in the degree utility curvature, however, our results show that this finding is primarily driven by loss aversion and, for women, also by a more pessimistic psychological response toward the probability of obtaining the best possible outcome.

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Section: Parametric Specificationsmentioning

confidence: 99%

A parametric analysis of prospect theory’s functionals for the general population

2009

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“…Fox 1995, Prelec 1998). Apparently, this consideration motivated Daneffe (1996), Fennema andvan Assen (1998), Abdellaoui et al (2002Abdellaoui et al ( , 2004 and Etchart-Vincent (2004) …”

Section: Tradeoff Methodsmentioning

confidence: 93%

“…Bleichrodt and Pinto (2000), Abdellaoui (2000) and Abdellaoui et al (2002Abdellaoui et al ( , 2003 use…”

Section: Propositionmentioning

confidence: 99%

Error Propagation in the Elicitation of Utility and Probability Weighting Functions

Blavatskyy

2006

Theor Decis

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Blavatskyy, P Blavatskyy, P (2006) AbstractElicitation methods in decision-making under risk allow us to infer the utilities of outcomes as well as the probability weights from the observed preferences of an individual. An optimally efficient elicitation method is proposed, which takes the inevitable distortion of preferences by random errors into account and minimizes the effect of such errors on the inferred utility and probability weighting functions. Under mild assumptions, the optimally efficient method for eliciting utilities and probability weights is the following three-stage procedure. First, a probability is elicited whose subjective weight is one half. Second, the utility function is elicited through the midpoint chaining certainty equivalent method using the probability elicited at the first stage. Finally, the probability weighting function is elicited through the probability equivalent method. Efficient elicitation of utility and probability weighting functionsPavlo Blavatskyy Abstract:Elicitation methods in decision making under risk allow a researcher to infer the subjective utilities of outcomes as well as the subjective weights of probabilities from the observed preferences of an individual. An optimally efficient elicitation method is proposed, which takes into account the inevitable distortion of preferences by random errors and minimizes the effect of such errors on the inferred utility and probability weighting functions. Under mild assumptions, the optimally efficient method for eliciting utilities (weights) of many outcomes (probabilities) is the following three-stage procedure. First, a probability is elicited whose subjective weight is one half. Second, an individual's utility function is elicited through the midpoint chaining certainty equivalent method employing the probability elicited at the first stage as an input. Finally, an individual's probability weighting function is elicited through the probability equivalent method.

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“…Our data set is taken from Abdellaoui et al [2]. These authors estimate the parameters of three utility functions (power, exponential and expo-power) with only one parameter.…”

Section: Estimation Of the Parametersmentioning

confidence: 99%

“…Since the elicitation of preferences is not a goal of this research, we use data from Abdellaoui et al [2]. These authors elicit the preferences of 47 decision-makers using a choice-based method (trade-off method) and a choiceless method (strength-of-preference method) and estimate the parameters of three utility functions (power, exponential, and expo-power).…”

Section: Introductionmentioning

confidence: 99%

Estimating utility functions using generalized maximum entropy

Pires

Dionísio

Coelho

2012

Journal of Applied Statistics

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This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.

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Reconciling introspective utility with revealed preference: Experimental arguments based on prospect theory

Cited by 135 publications

References 72 publications

A parametric analysis of prospect theory’s functionals for the general population

A parametric analysis of prospect theory’s functionals for the general population

Error Propagation in the Elicitation of Utility and Probability Weighting Functions

Estimating utility functions using generalized maximum entropy

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