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Alternation Bias and the Parameterization of Cumulative Prospect Theory

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  • Kaivanto, Kim

Abstract

Two recently published studies argue that conventional parameterizations of cumulative prospect theory (CPT) fail to resolve the St. Petersburg Paradox. Yet as a descriptive theory CPT is not intended to account for the local representativeness effect, which is known to induce 'alternation bias' on binary iid sequences such as those generated by coin tossing in St. Petersburg gambles. Once alternation bias is controlled for, conventional parameterizations of CPT yield finite certainty equivalents for the St. Petersburg gamble, negating the suggested need for reparameterization. Moreover, the associated willingness to pay estimates fall within the generally accepted empirical range.

Suggested Citation

  • Kaivanto, Kim, 2008. "Alternation Bias and the Parameterization of Cumulative Prospect Theory," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 91-107.
  • Handle: RePEc:zbw:espost:52592
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    File URL: https://www.econstor.eu/bitstream/10419/52592/1/Kaivanto%282007%29AlternationBiasAndTheParameterizationofCPT.pdf
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    References listed on IDEAS

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    1. 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.
    2. Mohammed Abdellaoui & Frank Vossmann & Martin Weber, 2005. "Choice-Based Elicitation and Decomposition of Decision Weights for Gains and Losses Under Uncertainty," Management Science, INFORMS, vol. 51(9), pages 1384-1399, September.
    3. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    4. Rachel Croson & James Sundali, 2005. "The Gambler’s Fallacy and the Hot Hand: Empirical Data from Casinos," Journal of Risk and Uncertainty, Springer, vol. 30(3), pages 195-209, May.
    5. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    6. Han Bleichrodt & Jose Luis Pinto, 2000. "A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis," Management Science, INFORMS, vol. 46(11), pages 1485-1496, November.
    7. Matthew Rabin, 2002. "Inference by Believers in the Law of Small Numbers," The Quarterly Journal of Economics, Oxford University Press, vol. 117(3), pages 775-816.
    8. Camerer, Colin F & Ho, Teck-Hua, 1994. "Violations of the Betweenness Axiom and Nonlinearity in Probability," Journal of Risk and Uncertainty, Springer, vol. 8(2), pages 167-196, March.
    9. George Wu & Richard Gonzalez, 1996. "Curvature of the Probability Weighting Function," Management Science, INFORMS, vol. 42(12), pages 1676-1690, December.
    10. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
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    Citations

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    Cited by:

    1. Tibor Neugebauer, 2010. "Moral Impossibility in the Petersburg Paradox : A Literature Survey and Experimental Evidence," LSF Research Working Paper Series 10-14, Luxembourg School of Finance, University of Luxembourg.
    2. Kaivanto, Kim & Kroll, Eike B., 2012. "Negative recency, randomization device choice, and reduction of compound lotteries," Economics Letters, Elsevier, vol. 115(2), pages 263-267.
    3. José Antonio Robles-Zurita, 2015. "Alternation Bias and Sums of Identically Distributed Monetary Lotteries," Working Papers 15.08, Universidad Pablo de Olavide, Department of Economics.
    4. Kim Kaivanto & Eike Kroll, 2014. "Alternation bias and reduction in St. Petersburg gambles," Working Papers 65600286, Lancaster University Management School, Economics Department.

    More about this item

    Keywords

    St. Petersburg Paradox; Cumulative Prospect Theory; Local Representativeness Effect; Alternation Bias; Law of Small Numbers;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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