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Weighting Competing Models

Author

Listed:
  • Chiara Aina
  • Florian H. Schneider

Abstract

We study how individuals update their beliefs in the presence of competing data-generating processes, or models, that could explain observed data. Through experiments, we identify the weights participants assign to different models and find that the most common updating rule gives full weight to the model that best fits the data. While some participants assign positive weights to multiple models—consistent with Bayesian updating—they often do so in a systematically biased manner. Moreover, these biases in model weighting frequently lead participants to become more certain about a state regardless of the data, violating a core property of Bayesian updating.

Suggested Citation

  • Chiara Aina & Florian H. Schneider, 2025. "Weighting Competing Models," CESifo Working Paper Series 11757, CESifo.
    • Handle: RePEc:ces:ceswps:_11757
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11757.pdf
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    More about this item

    Keywords

    belief updating; narratives; mental models; experiments;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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