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Is data interpretation in utility and prospect theories unquestionably correct?

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  • Harin, Alexander

Abstract

This is a very draft version of the report "The random-lottery incentive system. Can p~1 experiments deductions be correct?". It is published to extend the abstract of the report. Aczél and Luce emphasized a fundamental question: whether W(1)=1 (whether the Prelec weighting function equals 1 at p=1). A purely mathematical theorem proves W(1)

Suggested Citation

  • Harin, Alexander, 2014. "Is data interpretation in utility and prospect theories unquestionably correct?," MPRA Paper 53880, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:53880
    as

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    File URL: https://mpra.ub.uni-muenchen.de/53880/1/MPRA_paper_53880.pdf
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    References listed on IDEAS

    as
    1. Alexander Harin, 2012. "Data Dispersion in Economics (I) - Possibility of Restrictions," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 59-70, August.
    2. Alexander W. Cappelen & James Konow & Erik ?. S?rensen & Bertil Tungodden, 2013. "Just Luck: An Experimental Study of Risk-Taking and Fairness," American Economic Review, American Economic Association, vol. 103(4), pages 1398-1413, June.
    3. Alexander Harin, 2013. "Data dispersion near the boundaries: can it partially explain the problems of decision and utility theories?," Working Papers hal-00851022, HAL.
    4. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    5. repec:ebl:ecbull:v:28:y:2004:i:11:p:a0 is not listed on IDEAS
    6. Harin, Alexander, 2010. "Theorem of existence of ruptures in probability scale. Preliminary short version," MPRA Paper 23319, University Library of Munich, Germany.
    7. Alexander Harin, 2004. "Arrangement infringement possibility approach: some economic features of large-scale events," Economics Bulletin, AccessEcon, vol. 28(11), pages 1.
    8. Alexander Harin, 2012. "Data Dispersion in Economics(II) - Inevitability and Consequences of Restrictions," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 24-36, November.
    9. James Andreoni & Charles Sprenger, 2012. "Risk Preferences Are Not Time Preferences," American Economic Review, American Economic Association, vol. 102(7), pages 3357-3376, December.
    10. Harin, Alexander, 2009. "Ruptures in the probability scale? Calculation of ruptures’ dimensions," MPRA Paper 19348, University Library of Munich, Germany.
    11. Tversky, Amos & Wakker, Peter, 1995. "Risk Attitudes and Decision Weights," Econometrica, Econometric Society, vol. 63(6), pages 1255-1280, November.
    12. Harin, Alexander, 2008. "Solution of the Ellsberg paradox by means of the principle of uncertain future," MPRA Paper 8168, University Library of Munich, Germany.
    13. Daniel Kahneman & Richard H. Thaler, 2006. "Anomalies: Utility Maximization and Experienced Utility," Journal of Economic Perspectives, American Economic Association, vol. 20(1), pages 221-234, Winter.
    14. Harin, Alexander, 2009. "General correcting formula of forecasting?," MPRA Paper 15746, University Library of Munich, Germany.
    15. Harin, Alexander, 2007. "Principle of uncertain future and utility," MPRA Paper 1959, University Library of Munich, Germany.
    16. Alexander Harin, 2005. "A Rational Irrational Man," Public Economics 0511005, University Library of Munich, Germany.
    17. David J. Butler & Graham C. Loomes, 2007. "Imprecision as an Account of the Preference Reversal Phenomenon," American Economic Review, American Economic Association, vol. 97(1), pages 277-297, March.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    utility; prospect theory; random-lottery incentive system; Prelec weighting function; experiments;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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