On the complexity of forming mental models

Chad Kendall and Ryan Oprea

Quantitative Economics, 2024, vol. 15, issue 1, 175-211

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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.3982/QE2264

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:15:y:2024:i:1:p:175-211

Ordering information: This journal article can be ordered from
https://www.econometricsociety.org/membership
econometrica@econometricsociety.org

Access Statistics for this article

More articles in Quantitative Economics from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery (contentdelivery@wiley.com).