Classifying Behaviors in Risky Choices
This paper presents a nonparametric approach to classification of data from lottery experiments. Using very basic mathematical tools the paper endeavors to answer the questions: How to determine the “average” subject in a group? How to find a subject presenting the most similar behavior to a given one? How to detect outlier subject(s)? How to classify behaviors by their dissimilarity from the perfectly rational decision maker? How to rank subjects by risk attitudes? How to cluster subjects? This paper demonstrates that the answer to all of these questions may be found non-parametrically, without the use of any specific model.
|Date of creation:||12 Jul 2010|
|Date of revision:|
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Web page: https://mpra.ub.uni-muenchen.de
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- John D Hey & Andrea Morone & Ulrich Schmidt, 2007.
"Noise and Bias in Eliciting Preferences,"
07/04, Department of Economics, University of York.
- John D. Hey & Andrea Morone & Ulrich Schmidt, 2007. "Noise and Bias in Eliciting Preferences," Kiel Working Papers 1386, Kiel Institute for the World Economy.
- Hey, John Denis & Morone, Andrea & Schmidt, Ulrich, 2007. "Noise and bias in eliciting preferences," Kiel Working Papers 1386, Kiel Institute for the World Economy (IfW).
- Traub, Stefan & Schmidt, Ulrich, 2006.
"An Experimental Investigation of the Disparity between WTA and WTP for Lotteries,"
Economics Working Papers
2006,09, Christian-Albrechts-University of Kiel, Department of Economics.
- Ulrich Schmidt & Stefan Traub, 2009. "An Experimental Investigation of the Disparity Between WTA and WTP for Lotteries," Theory and Decision, Springer, vol. 66(3), pages 229-262, March.
- Schmidt, Ulrich & Traub, Stefan, 2009. "An experimental investigation of the disparity between WTA and WTP for lotteries," Open Access Publications from Kiel Institute for the World Economy 28786, Kiel Institute for the World Economy (IfW).
- Kontek, Krzysztof, 2010. "Multi-Outcome Lotteries: Prospect Theory vs. Relative Utility," MPRA Paper 22947, University Library of Munich, Germany.
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