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A Bayesian Approach to Decision-Making under Ambiguity

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  • Dobbs, Ian M

Abstract

A Bayesian characterization of ambiguity is proposed in which outcomes constitute information that modifies the retrospective evaluation of a course of action. Hindsight bias occurs when the ex ante valuation of a course of action is affected by contemplation of hindsight evaluation. The model provides an explanation of various valuations of the expected utility model, notably the Ellsberg paradox (and related effects), the common ratio, and common consequence effects. In contrast to recent theories concerning decision-making under ambiguity, probabilities are not required to be biased or to be sub- or superadditive. Some experimental tests of ambiguity coherence are also reported. Copyright 1991 by The London School of Economics and Political Science.

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Bibliographic Info

Article provided by London School of Economics and Political Science in its journal Economica.

Volume (Year): 58 (1991)
Issue (Month): 232 (November)
Pages: 417-40

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Handle: RePEc:bla:econom:v:58:y:1991:i:232:p:417-40

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Cited by:
  1. Baillon, Aurélien & Driesen, Bram & Wakker, Peter P., 2012. "Relative concave utility for risk and ambiguity," Games and Economic Behavior, Elsevier, vol. 75(2), pages 481-489.
  2. Oleg Eismont & Heinz Welsch, 1996. "Optimal greenhouse gas emissions under various assessments of climate change ambiguity," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 8(2), pages 129-140, September.
  3. Peter Wakker & Danielle Timmermans & Irma Machielse, 2007. "The effects of statistical information on risk ambiguity attitudes, and on rational insurance decisions," Natural Field Experiments 00338, The Field Experiments Website.
  4. Fraser, C.D., 1985. "Misperceived job hazards and welfare," CORE Discussion Papers 1985013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Yehuda Izhakian, 2012. "Ambiguity Measurement," Working Papers 12-01, New York University, Leonard N. Stern School of Business, Department of Economics.
  6. Lars Lefgren & Brennan Platt & Joseph Price, 2011. "Sticking with What (Barely) Worked," NBER Working Papers 17477, National Bureau of Economic Research, Inc.
  7. Biswas, Dipayan & Pechmann, Cornelia, 2012. "What do these clinical trial results mean? How product efficacy judgments are affected by data partitioning, framing, and quantification," Organizational Behavior and Human Decision Processes, Elsevier, vol. 117(2), pages 341-350.
  8. Théodora Dupont-Courtade, 2012. "Insurance demand under ambiguity and conflict for extreme risks: Evidence from a large representative survey," Documents de travail du Centre d'Economie de la Sorbonne 12020, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  9. repec:hal:journl:halshs-00718642 is not listed on IDEAS
  10. Welsch, Heinz, 1995. "Greenhouse gas abatement under ambiguity," Energy Economics, Elsevier, vol. 17(2), pages 91-100, April.
  11. Théodora Dupont-Courtade, 2012. "Insurance demand under ambiguity and conflict for extreme risks : Evidence from a large representative survey," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00718642, HAL.

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