Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296357 
Year of Publication: 
2024
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 15 [Issue:] 1 [Year:] 2024 [Pages:] 175-211
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We experimentally study how people form predictive models of simple data generating processes (DGPs), by showing subjects data sets and asking them to predict future outputs. We find that subjects: (i) often fail to predict in this task, indicating a failure to form a model, (ii) often cannot explicitly describe the model they have formed even when successful, and (iii) tend to be attracted to the same, simple models when multiple models fit the data. Examining a number of formal complexity metrics, we find that all three patterns are well organized by metrics suggested by Lipman (1995) and Gabaix (2014) that describe the information processing required to deploy models in prediction.
Subjects: 
Complexity
mental models
inference
bounded rationality
behavioral economics
economics experiments
JEL: 
C0
C91
D91
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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