The Predictive Utility of Generalized Expected Utility Theories

Harless, David W.; Camerer, Colin F.

doi:10.2307/2951749

Cited by 631 publications

(457 citation statements)

References 47 publications

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“…First, the high degree of predictive accuracy suggests that people possess intrinsic preferences that are stable between passive experience and active decision making (von Neumann and Morgenstern, 1944). Second, the poor predictive power of lottery ranks compared to brain activation when the ranks are similar suggests that the reliance on choice alone to characterize an individual's preferences may give the appearance that choice is probabilistic or that preferences are unstable (Harless and Camerer, 1994;Hey and Orme, 1994;Machina, 1985). The fact that passive brain activations can predict behavior significantly better than the ranking suggests that although choices may be probabilistic or inconsistent, preferences may not be.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

Nonlinear neurobiological probability weighting functions for aversive outcomes

Berns¹,

Capra²,

Chappelow³

et al. 2008

NeuroImage

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show abstract

“…Moreover, reliance on the simpler model would be more justified if the accuracy of the empirical descriptions provided by the two models did not differ substantially (even if they differed significantly). That is, the underlying issue in such decisions is often the trade-off between fit and parsimony (Harless & Camerer, 1994). Nevertheless, it would seem important at least to know which type of model provides the better description (and whether the fit is significantly better), and on this point the results seem clear: Although the size of the increase in the proportion of explained variance may vary, at the group level a hyperbola-like function provides significantly better fits to discounting data than the simple hyperbola.…”

Section: Mathematical Descriptions Of Temporal Discountingmentioning

confidence: 99%

A Discounting Framework for Choice With Delayed and Probabilistic Rewards.

Green¹,

Myerson²

2004

Psychological Bulletin

1,441

1,337

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When choosing between delayed or uncertain outcomes, individuals discount the value of such outcomes on the basis of the expected time to or the likelihood of their occurrence. In an integrative review of the expanding experimental literature on discounting, the authors show that although the same form of hyperbola-like function describes discounting of both delayed and probabilistic outcomes, a variety of recent findings are inconsistent with a single-process account. The authors also review studies that compare discounting in different populations and discuss the theoretical and practical implications of the findings. The present effort illustrates the value of studying choice involving both delayed and probabilistic outcomes within a general discounting framework that uses similar experimental procedures and a common analytical approach.Choice is relatively predictable when the alternatives differ on only one dimension. For example, if individuals are offered a choice between two rewards that differ only in amount, they generally choose the larger rather than the smaller reward. Similarly, if offered a choice between two rewards that differ only in delay, individuals tend to choose the reward available sooner rather than the one available later, and if offered a choice between two alternatives that differ only in probability, they tend to choose the more certain reward. These same general principles, and a complementary set of principles for negative outcomes (e.g., smaller punishments will be chosen over larger ones), apply both to humans and other animals. These behavioral tendencies obviously make both economic and evolutionary sense.Problems arise, however, when choice options differ on more than one dimension-for example, when an individual must choose between a smaller reward available sooner and a larger reward available later. It is also unclear, in general, which alternative an individual would (or should) select when choosing between a smaller, more certain reward and a larger, less certain one or between a less certain reward available sooner and a more certain, but more delayed, reward. These problems are only compounded when rewards differ on all three dimensions (i.e., amount, delay, and probability). The central issue is how individuals make trade-offs among their preferences on these dimensions (Keeney & Raiffa, 1993).This issue is not only of theoretical interest; it also has implications for everyday decision making, which often involves outcomes that differ on multiple dimensions. Examples of such decisions include deciding whether to purchase a less expensive item that can be enjoyed now or to save for a more expensive one; whether to choose a risky investment that potentially could pay off at a high rate or one that pays a low but guaranteed rate of return; and whether to buy

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“…Unfortunately, no single best fitting model has been identified so far (Harless and Camerer, 1994;Starmer, 2000) and, depending on the individual, one or the other model fits better. This finding poses a serious problem for applied economics.…”

Section: Introductionmentioning

confidence: 99%

Risk and Rationality: Uncovering Heterogeneity in Probability Distortion

2009

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It has long been recognized that there is considerable heterogeneity in individual risk taking behavior but little is known about the distribution of risk taking types. We present a parsimonious characterization of risk taking behavior by estimating a finite mixture regression model for three different experimental data sets, two Swiss and one Chinese, over a large number of real gains and losses. We find two distinct types of individuals: In all three data sets, the choices of roughly 80% of the subjects exhibit significant deviations from linear probability weighting, consistent with prospect theory. 20% of the subjects weight probabilities near linearly and behave essentially as expected value maximizers. Moreover, individuals are cleanly assigned to one type with probabilities close to unity. The reliability and robustness of our classification suggest using a mix of preference theories in applied economic modeling.

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The Predictive Utility of Generalized Expected Utility Theories

Cited by 631 publications

References 47 publications

Nonlinear neurobiological probability weighting functions for aversive outcomes

Nonlinear neurobiological probability weighting functions for aversive outcomes

A Discounting Framework for Choice With Delayed and Probabilistic Rewards.

Risk and Rationality: Uncovering Heterogeneity in Probability Distortion

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