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Classifying Behaviors in Risky Choices

Author

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  • Kontek, Krzysztof

Abstract

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.

Suggested Citation

  • Kontek, Krzysztof, 2010. "Classifying Behaviors in Risky Choices," MPRA Paper 23845, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:23845
    as

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    File URL: https://mpra.ub.uni-muenchen.de/23862/1/MPRA_paper_23862.pdf
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    References listed on IDEAS

    as
    1. John Hey & Andrea Morone & Ulrich Schmidt, 2009. "Noise and bias in eliciting preferences," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 213-235, December.
    2. 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.
    3. Kontek, Krzysztof, 2010. "Multi-Outcome Lotteries: Prospect Theory vs. Relative Utility," MPRA Paper 22947, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Alexei Kolokolov, 2011. "Futures hedging: Multivariate GARCH with dynamic conditional correlations (in Russian)," Quantile, Quantile, issue 9, pages 61-75, July.

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    More about this item

    Keywords

    Lottery experiments; Certainty Equivalents; Risk Attitude; Cluster Analysis; Nonparametric Methods; Relative Utility Function.;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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