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Ambiguity and uncertainty in Ellsberg and Shackle

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  • Marcello Basili
  • Carlo Zappia

Abstract

This paper argues that the similarities between Ellsberg's and Shackle's frameworks for discussing the limits of the probabilistic approach to decision theory are more important than usually admitted. The paper discusses the grounds on which the ambiguity surrounding the decision-maker in Ellsberg's urn experiments can be deemed analogous to the uncertainty faced by Shackle's entrepreneur taking 'crucial decisions'. The two authors' insights are assessed, and special attention is paid to the criteria for decision under uncertainty they put forward. The paper establishes a link between Ellsberg's and Shackle's perspectives and the non-additive probability approach of Gilboa and Schmeidler, an approach that offers an alternative to standard probability calculus, which can be of use to analyse both ambiguity and uncertainty. The comparison between Ellsberg and Shackle draws on an interpretation of Keynes's Treatise on Probability emphasising Keynes's rejection of both well-defined probability functions and maximisation as a guide to human conduct. It is shown that Keynes's viewpoint implies a reconsideration of the boundaries of probability theory 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. Copyright The Author 2009. Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved., Oxford University Press.

Suggested Citation

  • Marcello Basili & Carlo Zappia, 2010. "Ambiguity and uncertainty in Ellsberg and Shackle," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 34(3), pages 449-474.
  • Handle: RePEc:oup:cambje:v:34:y:2010:i:3:p:449-474
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    File URL: http://hdl.handle.net/10.1093/cje/bep008
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    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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.
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    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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    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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