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Probability weighting functions

Author

Listed:
  • Martina Nardon

    (Department of Economics, C� Foscari University Of Venice)

  • Paolo Pianca

    (Department of Economics, C� Foscari University Of Venice)

Abstract

Cumulative prospect theory (CPT) has been proposed as an alternative to expected utility theory to explain irregular behavior by economic agents. CPT comprises two key transformations: one of outcome values and the other of objective probabilities. Risk attitudes are derived from the shapes of these transformations as well as their interaction. The focus of this contribution is on the transformation of objective probability, which is commonly referred as probability weighting function. We review different families of weighting functions proposed in the literature and study their features.

Suggested Citation

  • Martina Nardon & Paolo Pianca, 2015. "Probability weighting functions," Working Papers 2015:29, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2015:29
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    References listed on IDEAS

    as
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    3. Ali al-Nowaihi & Sanjit Dhami, 2010. "Composite Prospect Theory: A proposal to combine ‘prospect theory’ and ‘cumulative prospect theory’," Discussion Papers in Economics 10/11, Division of Economics, School of Business, University of Leicester.
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    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Marie Pfiffelmann, 2011. "Solving the St. Petersburg Paradox in cumulative prospect theory: the right amount of probability weighting," Theory and Decision, Springer, vol. 71(3), pages 325-341, September.
    7. Diecidue, Enrico & Schmidt, Ulrich & Zank, Horst, 2009. "Parametric weighting functions," Journal of Economic Theory, Elsevier, vol. 144(3), pages 1102-1118, May.
    8. Marc Rieger & Mei Wang, 2006. "Cumulative prospect theory and the St. Petersburg paradox," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(3), pages 665-679, August.
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    11. Mohammed Abdellaoui & Olivier l’Haridon & Horst Zank, 2009. "Separating Curvature and Elevation: A Parametric Weighting Function," Economics Discussion Paper Series 0901, Economics, The University of Manchester.
    12. Jonathan Ingersoll, 2008. "Non‐Monotonicity of the Tversky‐Kahneman Probability‐Weighting Function: A Cautionary Note," European Financial Management, European Financial Management Association, vol. 14(3), pages 385-390, June.
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    Cited by:

    1. Martina Nardon & Paolo Pianca, 2019. "Insurance premium calculation under continuous cumulative prospect theory," Working Papers 2019:03, Department of Economics, University of Venice "Ca' Foscari".
    2. Li, Baibing & Hensher, David A., 2017. "Risky weighting in discrete choice," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 1-21.

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    More about this item

    Keywords

    Cumulative prospect theory; probability weighting function.;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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