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Operationalizing Reverse Bayesianism

Author

Listed:
  • Surajeet Chakravarty

    (University of Exeter)

  • David Kelsey

    (University of Exeter, University of Nottingham)

  • Joshua C. Teitelbaum

    (Georgetown University)

Abstract

Karni and Viero (2013) propose a model of belief revision under growing awareness reverse Bayesianism which posits that as a person becomes aware of new acts, consequences, or act-consequence links, she revises her beliefs over an expanded state space in a way that preserves the relative likelihoods of events in the original state space. A key limitation of the model is that reverse Bayesianism alone does not fully determine the revised probability distribution. We provide an assumption act independence that imposes additional restrictions on reverse Bayesian belief revision. We show that under act independence, knowledge of the probabilities of new events in the expanded state space is sufficient to fully determine the revised probability distribution in each case of growing awareness. We thereby operationalize the reverse Bayesian model for applications. To illustrate how act independence operationalizes reverse Bayesianism, we consider the law and economics problem of optimal safety regulation.

Suggested Citation

  • Surajeet Chakravarty & David Kelsey & Joshua C. Teitelbaum, 2020. "Operationalizing Reverse Bayesianism," Discussion Papers 2020-18, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    • Handle: RePEc:not:notcdx:2020-18
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    act independence; reverse Bayesianism; safety regulation; unawareness;
    All these keywords.

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