Kontek, Krzysztof (2010): MultiOutcome Lotteries: Prospect Theory vs. Relative Utility.

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Abstract
This paper discusses two approaches for the analysis of multioutcome lotteries. The first uses Cumulative Prospect Theory. The second is the Relative Utility Function, which strongly resembles the utility function hypothesized by Markowitz (1952). It is shown that the relative utility model follows Expected Utility Theory with a transformed outcome domain. An illustrative example demonstrates that not only it is a simpler model, but it also provides more sound predictions regarding certainty equivalents of multioutcome lotteries. The paper discusses estimation procedures for both models. It is noted that Cumulative Prospect Theory has been derived using twooutcome lotteries only, and it is hard to find any evidence in the literature of its parameters ever having been estimated by using lotteries with more than two outcomes. Least squares (mean) and quantile (including median) regression estimations are presented for the relative utility model. It turns out that the estimations for two and threeoutcome lotteries are essentially the same. This confirms the correctness of the model and vindicates the homogeneity of responses given by subjects. An additional advantage of the relative utility model is that it allows multioutcome lotteries, together with the estimation results, to be presented on a single graph. This is not possible using Cumulative Prospect Theory.
Item Type:  MPRA Paper 

Original Title:  MultiOutcome Lotteries: Prospect Theory vs. Relative Utility 
Language:  English 
Keywords:  MultiPrize Lotteries, Lottery / Prospect Valuation, Markowitz Hypothesis, Prospect / Cumulative Prospect Theory, Aspiration / Relative Utility Function. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation D  Microeconomics > D0  General > D03  Behavioral Microeconomics: Underlying Principles D  Microeconomics > D8  Information, Knowledge, and Uncertainty > D81  Criteria for DecisionMaking under Risk and Uncertainty C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C21  CrossSectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions C  Mathematical and Quantitative Methods > C9  Design of Experiments > C91  Laboratory, Individual Behavior D  Microeconomics > D8  Information, Knowledge, and Uncertainty > D87  Neuroeconomics 
Item ID:  22947 
Depositing User:  Krzysztof Kontek 
Date Deposited:  30. May 2010 06:35 
Last Modified:  10. Mar 2015 10:53 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/22947 