Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/316871 
Year of Publication: 
2025
Series/Report no.: 
CESifo Working Paper No. 11757
Publisher: 
CESifo GmbH, Munich
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.
Subjects: 
belief updating
narratives
mental models
experiments
JEL: 
D83
D90
C90
Document Type: 
Working Paper
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