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Robust Bayesian Choice

Author

Listed:
  • Stanca Lorenzo

    (Department of Economics, Social Studies, Applied Mathematics and Statistics (ESOMAS) and Collegio Carlo Alberto, University of Torino, Italy;)

Abstract

A major concern with Bayesian decision making under uncertainty is the use of a single probability measure to quantify all relevant uncertainty. This paper studies prior robustness as a form of continuity of the value of a decision problem. It is shown that this notion of robustness is characterized by a form of stable choice over a sequence of perturbed decision problems, in which the available acts are perturbed in a precise fashion. Subsequently, a choice-based measure of prior robustness is introduced and applied to portfolio choice and climate mitigation.

Suggested Citation

  • Stanca Lorenzo, 2023. "Robust Bayesian Choice," Working papers 079, Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    • Handle: RePEc:tur:wpapnw:079
    as

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    File URL: https://www.bemservizi.unito.it/repec/tur/wpapnw/m79.pdf
    File Function: First version, 2023
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Risk; Uncertainty; Robustness; Ambiguity; Robust Statistics; Prior Selection.;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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