Explaining the characteristics of the power (CRRA) utility family

Wakker, Peter P.

doi:10.1002/hec.1331

Cited by 227 publications

(161 citation statements)

References 43 publications

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“…This function owes its popularity to the ease with which it can be manipulated and fit to data, and the good fit it has been found to provide in many cases (Wakker 2008). This functional form was for instance used by Tversky and Kahneman (1992) to estimate prospect theory parameters, and also constitutes the main functional form employed by Fehr-Duda et al (2010) in their investigation of separability (although they also tested the stability of the phenomenon to other functional forms, an issue to which we will return below).…”

Section: The Utility Function and Probability-outcome Separabilitymentioning

confidence: 99%

Accommodating stake effects under prospect theory

Bouchouicha

Vieider

2017

J Risk Uncertain

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One of the stylized facts underlying prospect theory is a fourfold pattern of risk preferences. People have been shown to be risk seeking for small probability gains and large probability losses, while being risk averse for large probability gains and small probability losses. Another fourfold pattern of risk preferences over outcomes, postulated by Harry Markowitz in 1952, has received much less attention and is currently not integrated into prospect theory. In two experiments, we show that risk preferences may change over outcomes. While we find people to be risk seeking for small outcomes, this turns to risk neutrality and later risk aversion as stakes increase. We then show how a one-parameter logarithmic utility function fits such stake effects significantly better under prospect theory than the power or exponential functions mostly used when fitting prospect theory models. We further investigate the extent to which the use of ill-suited functional forms to represent utility may result in violations of prospect theory, and whether such violations disappear when using logarithmic utility.

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Section: The Utility Function and Probability-outcome Separabilitymentioning

confidence: 99%

Accommodating stake effects under prospect theory

Bouchouicha

Vieider

2017

J Risk Uncertain

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“…The utility function determines individuals' attitudes toward additional monetary gains and losses. The curvature of this function for gains is often modeled by a power function because of its simplicity and its good fit to (experimental) data (Wakker 2008). 3 Tversky and Kahneman (1992) introduced this function for prospect theory, written as U(…”

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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“…Furthermore the utility function, for each attribute of the decision problem, is assumed to be the same for the r geographical locations and that the overall score of an attribute is equal to the sum of the scores of each region for that attribute. The cubic utility function is a member of the family of constant relative risk aversion utilities, used in the literature to model risk aversion (see Wakker, 2008). Now that the IDSS has been fully defined for this example, we can show how the algorithm works symbolically when the overarching structure is the linear MDM.…”

Section: A Multiregression Dynamic Model For a Nuclear Emergencymentioning

confidence: 99%

Bayesian decision support for complex systems with many distributed experts

Leonelli

Smith

2015

Ann Oper Res

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Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems.

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Explaining the characteristics of the power (CRRA) utility family

Cited by 227 publications

References 43 publications

Accommodating stake effects under prospect theory

Accommodating stake effects under prospect theory

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

Bayesian decision support for complex systems with many distributed experts

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