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Unformatted text preview: 1 8 9 1 C A L I F O R N I A I N S T IT U T E O F T E C H N O L O G Y * This paper was prepared for D. Kahneman and A. Tversky (Eds.), Choices, Values and Frames (in press). The research was supported by NSF grant SBR9601236 and the hospitality of the Center for Advanced Study in Behavioral Sciences during 199798. Linda Babcock and Barbara Mellers gave helpful suggestions. 1 In rankdependent approaches, the weights attached to outcomes are differences in weighted cumulative probabilities. For example, if the outcomes are ordered x 1 > x 2 > ... > x n , the weight on outcome x i is &#3; (p 1 + p 2 + . .. p i ) &#3; (p 1 + p 2 + . ...p i1 ). (Notice that if &#3; (p)=p this weight is just the probability p i. ). In cumulative prospect theory gains and losses are ranked and weighted separately (by magnitude). 1 Prospect theory in the wild: Evidence from the field Colin Camerer * The workhorses of economic analysis are simple formal models which can explain naturallyoccurring phenomena. Reflecting this taste, economists often say they will incorporate more psychological ideas into economics if those ideas can parsimoniously account for field data better than standard theories do. Taking this statement seriously, this paper describes ten regularities in naturallyoccurring data which are anomalies for expected utility theory, but can all be explained by three simple elements of prospect theory lossaversion, reflection effects, and nonlinear weighting of probability along with the assumption that people isolate decisions (or edit them) from others they might be grouped with (Read, Loewenstein, and Rabin, 1998; cf. Thaler, this volume). I hope to show how much success has already been had applying prospect theory to field data, and to inspire economists and psychologists to spend more time in the wild. The 10 patterns are summarized in Table 1. To keep the paper brief, I sketch expected utility and prospect theory very quickly. (Readers who want to know more should look elsewhere in this volume or in Camerer, 1995, or Rabin, 1998a). In expected utility, gambles which yield risky outcomes x i with probabilities p i are valued according to "&#17; p i u(x i ) where u(x) is the &#24;utility &#25; of outcome x. In prospect theory they are valued by "&#17; &#3; (p i )v(x ir), where &#3; (p) is a function which weights probabilities nonlinearly, overweighting probabilities below .3 or so and underweighting larger probabilities. 1 The value function v(xr) exhibits diminishing marginal sensitivity to deviations from the reference point r, creating a &#24;reflection effect &#25; because v(xr) is convex for losses and concave for gains (i.e., v &#29;(xr)>0 for x<r and v &#29;(xr)<0 for x>r). The value function also exhibits &#24;lossaversion &#25; the value of a loss x is larger in magnitude than the value of an equalsized gain (i.e., v(x)>v(x) for x>0)....
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