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Reverse Bayesianism: Revising Beliefs in Light of Unforeseen Events

Author

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  • Christoph K. Becker
  • Tigran Melkonyan
  • Eugenio Proto
  • Andis Sofianos
  • Stefan T. Trautmann

Abstract

Bayesian updating is the dominant theory of learning. However, the theory is silent about how individuals react to events that were previously unforeseeable or unforeseen. Building on a recently developed axiomatic framework to analyze such situations, we test if subjects update their beliefs according to “reverse Bayesianism”, under which the relative likelihoods of prior beliefs remain unchanged after an unforeseen event materializes. We develop two experiments that entail unforeseen events and find that participants do not systematically deviate from reverse Bayesianism. However, we do find well-known violations of Bayesian updating. Decision makers seem to be ex ante unaware - they do not expect outcomes that they have not yet observed or have not been informed about. At the same time, we find instances of both increased and decreased awareness after exposure to unforeseen events.

Suggested Citation

  • Christoph K. Becker & Tigran Melkonyan & Eugenio Proto & Andis Sofianos & Stefan T. Trautmann, 2020. "Reverse Bayesianism: Revising Beliefs in Light of Unforeseen Events," CESifo Working Paper Series 8662, CESifo.
    • Handle: RePEc:ces:ceswps:_8662
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    More about this item

    Keywords

    reverse Bayesianism; unforeseen; unawareness; Bayesian updating;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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