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Ambiguity and uncertainty in Ellsberg and Shackle

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  • Marcello Basili
  • Carlo Zappia

Abstract

This paper argues that Ellsberg’s and Shackle’s frameworks for discussing the limits of the (subjective) probabilistic approach to decision theory are not as different as they may appear. To stress the common elements in their theories Keynes’s Treatise on Probability provides an essential starting point. Keynes’s rejection of well-defined probability functions, and of maximisation as a guide to human conduct, is shown to imply a reconsideration of what probability theory can encompass, that is in the same vein of Ellsberg’s and Shackle’s concern in the years of the consolidation of Savage’s new probabilistic mainstream. The parallel between Keynes and the two decision theorists is drawn by means of a particular assessment of Shackle’s theory of decision, namely, it is interpreted in the light of Ellsberg’s doctoral dissertation. In this thesis, published only as late as 2001, Ellsberg developed the details and devised the philosophical background of his criticism of Savage as first put forward in the famed 1961 QJE article. The paper discusses the grounds on which the ambiguity surrounding the decision maker in Ellsberg’s urn experiment can be deemed analogous to the uncertainty faced by Shackle’s entrepreneur taking “unique decisions.” The paper argues also that the insights at the basis of the work of both Shackle and Ellsberg, as well as the criteria for decision under uncertainty they put forward, are relevant to understand the development of modern decision theory.

Suggested Citation

  • Marcello Basili & Carlo Zappia, 2005. "Ambiguity and uncertainty in Ellsberg and Shackle," Department of Economics University of Siena 460, Department of Economics, University of Siena.
  • Handle: RePEc:usi:wpaper:460
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    Cited by:

    1. Carlo Zappia, 2012. "Re-reading Keynes after the crisis: probability and decision," Department of Economics University of Siena 646, Department of Economics, University of Siena.
    2. Elise Payzan-LeNestour & Peter Bossaerts, 2011. "Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-14, January.
    3. James Derbyshire, 2020. "Answers to questions on uncertainty in geography: Old lessons and new scenario tools," Environment and Planning A, , vol. 52(4), pages 710-727, June.
    4. Aldred, Jonathan, 2013. "Justifying precautionary policies: Incommensurability and uncertainty," Ecological Economics, Elsevier, vol. 96(C), pages 132-140.
    5. Helena Gaspars-Wieloch, 2018. "The Impact of the Structure of the Payoff Matrix on the Final Decision made Under Uncertainty," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(01), pages 1-27, February.
    6. Élise PAYZAN LE NESTOUR, 2010. "Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem," Swiss Finance Institute Research Paper Series 10-28, Swiss Finance Institute.
    7. Helena Gaspars-Wieloch, 2014. "Modifications of the Hurwicz’s decision rule," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 779-794, December.
    8. Derbyshire, James, 2017. "Potential surprise theory as a theoretical foundation for scenario planning," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 77-87.
    9. Helena Gaspars-Wieloch, 2015. "On a decision rule supported by a forecasting stage based on the decision maker’s coefficient of optimism," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(3), pages 579-594, September.
    10. Carlo Zappia, 2015. "Daniel Ellsberg on the Ellsberg Paradox," Department of Economics University of Siena 716, Department of Economics, University of Siena.
    11. Derbyshire, James & Giovannetti, Emanuele, 2017. "Understanding the failure to understand New Product Development failures: Mitigating the uncertainty associated with innovating new products by combining scenario planning and forecasting," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 334-344.

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    More about this item

    Keywords

    uncertainty; weight of argument; non-additive probability;
    All these keywords.

    JEL classification:

    • B21 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Microeconomics
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory

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