Parameter-Free Elicitation of Utility and Probability Weighting Functions

Abdellaoui, Mohammed

doi:10.1287/mnsc.46.11.1497.12080

Cited by 775 publications

(680 citation statements)

References 37 publications

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“…Our results regarding risk aversion for gains are similar to those of previous studies (Abdellaoui 2000;Abdellaoui et al 2008). An interesting finding is that weighting of probability 1/2 importantly explains this risk aversion, utility for gains being close to linear.…”

Section: Utilitysupporting

confidence: 91%

“…The weight was significantly higher for losses than for gains (p=0.03). Consistent with previous evidence, decision weights for losses departed much less from linearity than those for gains did (Abdellaoui, 2000, Booij et al 2010). …”

Section: Probability Weightingsupporting

confidence: 87%

“…PT's main deviations from EU are its reliance on a reference point (RP), a nonlinear transformation of probabilities into decision weights (probability weighting), and a higher sensitivity to losses than to gains as seen from this RP (loss aversion). Much research has been done to measure utility and/or probability weighting under PT, both for monetary outcomes (Abdellaoui, 2000;Fehr-Duda et al, 2006;Tversky and Kahneman, 1992;van de Kuilen and Wakker, 2011;Wu and Gonzalez, 1999) and for health outcomes (Bleichrodt and Pinto, 2000;Miyamoto and Eraker, 1989;Verhoef et al, 1994), but quantifications of loss aversion have been less widespread.…”

Section: Introductionmentioning

confidence: 99%

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Prospect theory in the health domain: A quantitative assessment

Attema

Brouwer

l’Haridon

2013

Journal of Health Economics

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1 We are grateful to Laurens Niëns and Matthijs Versteegh for advice on the experimental design. Mohammed Abdellaoui, Aurélien Baillon, Han Bleichrodt and Peter Wakker gave helpful comments on a previous version of this manuscript. The usual disclaimer applies. ABSTRACTIt is well-known that expected utility (EU) has empirical deficiencies. Prospect theory (PT) has developed as an alternative with more descriptive validity. However, PT's full function had not yet been quantified in the health domain. This paper is the first to simultaneously measure utility of life duration, probability weighting, and loss aversion for health.We observe loss aversion and risk aversion, which for gains can be explained by probabilistic pessimism. Utility for gains is almost linear. For losses, we find less weighting of probability 1/2 and concave utility. This contrasts with the common finding of convex utility for monetary losses.However, PT was proposed to explain choices among lotteries involving small outcomes. Life years are arguably not 'small' and need not generate convex utility for losses. Moreover, utility of life duration reflects discounting, causing concave utility. These results are a first step in fitting non-EU models for health-related decisions.

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Section: Utilitysupporting

confidence: 91%

Section: Probability Weightingsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Prospect theory in the health domain: A quantitative assessment

Attema

Brouwer

l’Haridon

2013

Journal of Health Economics

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“…The choice of y 0 will affect the shape of the NPRR relative to the diagonal but not the relative orderings of NPRR. An extensive behavioral literature on probability weighting in the financial domain suggests that the probability weighting function crosses the diagonal in the vicinity of p=0.4 (Abdellaoui, 2000;Fehr-Duda et al, 2006;Kahneman and Tversky, 1979;Tversky and Kahneman, 1992;Wu and Gonzalez, 1996). In our experiment, the probability prospect that came closest to meeting these requirements was p=1/3..…”

Section: Probability Weighting Functionsmentioning

confidence: 64%

Nonlinear neurobiological probability weighting functions for aversive outcomes

Berns¹,

Capra²,

Chappelow³

et al. 2008

NeuroImage

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While mainstream economic models assume that individuals treat probabilities objectively, many people tend to overestimate the likelihood of improbable events and underestimate the likelihood of probable events. However, a biological account for why probabilities would be treated this way does not yet exist. While undergoing fMRI, we presented individuals with a series of lotteries, defined by the voltage of an impending cutaneous electric shock and the probability with which the shock would be received. During the prospect phase, neural activity that tracked the probability of the expected outcome was observed in a circumscribed network of brain regions that included the anterior cingulate, visual, parietal, and temporal cortices. Most of these regions displayed responses to probabilities consistent with nonlinear probability weighting. The neural responses to passive lotteries predicted 79% of subsequent decisions when individuals were offered choices between different lotteries, and exceeded that predicted by behavior alone near the indifference point.

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“…We choose y 0 as the activation associated with p = 1/3 for our study in light of an extensive behavioral literature on probability weighting in the financial domain suggesting that the probability weighting function crosses the diagonal in the vicinity of p = 0.4 (Abdellaoui, 2000;Kahneman & Tversky, 1979;Tversky & Kahneman, 1992;Wu & Gonzalez, 1996). In our experiment, the probability prospect that came closest to meeting these requirements was p = 1/3.…”

Section: Probability Weightingmentioning

confidence: 99%

Three studies on the neuroeconomics of decision-making when payoffs are real and negative

Berns¹,

Capra²,

Moore³

et al.

Neuroeconomics

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Purpose-We summarize three previous neuroeconomic studies with two features that distinguish them from most others in experimental economics: (1) the use of physical pain to induce incentives and (2) acquisition of data on brain activation levels. By correlating behavior when payoffs are painful with brain activation, we are able to test for the neurobiological relevance of important phenomena previously observed in experimental studies that are at odds with classical economic theories of decision-making. These specific phenomena are (a) negative discounting of future payoffs; (b) nonlinear probability weighting; (c) the experience of regret and rejoice when making a decision under risk.Methodology/approach-The expectation of pain is created through the use of mild electric shocks to the top of the foot. Pain confers disutility, so decisions are made in the domain of losses relative to the status quo. Simultaneous with these decisions, brain activation data is acquired through functional magnetic resonance imaging (fMRI).Findings-We find evidence for negative time discounting of electric shocks. Participants who exhibited the most extreme forms of this discounting were distinguished by early and robust activation of a subset of the cortical pain matrix. We also find evidence for probability weighting in the domain of electric shocks, which is manifest at the neural level. We find evidence both behaviorally and neurally for regret and rejoice functions for painful outcomes.Originality/value of chapter-Previous experimental economic studies in the domain of losses have typically used monetary rewards. Here, we report behavioral effects and neural correlates using pain.

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Parameter-Free Elicitation of Utility and Probability Weighting Functions

Cited by 775 publications

References 37 publications

Prospect theory in the health domain: A quantitative assessment

Prospect theory in the health domain: A quantitative assessment

Nonlinear neurobiological probability weighting functions for aversive outcomes

Three studies on the neuroeconomics of decision-making when payoffs are real and negative

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