The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
What You See Is All There Is*
Abstract
News reports and communication are inherently constrained by space, time, and attention. As a result, news sources often condition the decision of whether to share a piece of information on the similarity between the signal and the prior belief of the audience, which generates a sample selection problem. This article experimentally studies how people form beliefs in these contexts, in particular the mechanisms behind errors in statistical reasoning. I document that a substantial fraction of experimental participants follows a simple “what you see is all there is” heuristic, according to which participants exclusively consider information that is right in front of them, and directly use the sample mean to estimate the population mean. A series of treatments aimed at identifying mechanisms suggests that for many participants, unobserved signals do not even come to mind. I provide causal evidence that the frequency of such incorrect mental models is a function of the computational complexity of the decision problem. These results point to the context dependence of what comes to mind and the resulting errors in belief updating.
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Fully adjusting for biases is a challenge even for extremely well-informed and sophisticated thinkers.
“Unfortunately, fully adjusting for biases is a challenge even for extremely well-informed and sophisticated thinkers.”
From Measuring Democratic Backsliding (2024) · cites this paper as Enke 2020
There is strong evidence across a variety of topics and subject pools that people make substantial errors when faced with selection problems in the samples that they observe.
“There is strong evidence across a variety of topics and subject pools that people make substantial errors when faced with selection problems in the samples that they observe (see Enke 2020 for a prominent recent example and Brundage, Little, and You 2022 for a review).”
From Measuring Democratic Backsliding (2024) · cites this paper as Enke 2020
Citations
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Cited by
Sorted by citations
The Internet and Children’s Psychological Wellbeing
McDool, E., Powell, P., Roberts, J., & Taylor, K. (2020) · Journal of Health Economics · doi:10.1016/j.jhealeco.2019.102274 · Cited by 189
Measuring Democratic Backsliding
Little, A. T., & Meng, A. (2024) · PS: Political Science & Politics · doi:10.1017/s104909652300063x · Cited by 121
Metadata
- Authors
- Enke, B.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Enke, Benjamin
- ORCID
- Enke, Benjamin
- Venue
- The Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-17
- Volume
- 135
- Issue
- 3
- Pages
- 1363-1398
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W3123502129
- Cited by
- 186
- References
- 1
- Citations by year
- 2026: 18 · 2025: 27 · 2024: 54 · 2023: 28 · 2022: 19 · 2021: 21 · 2020: 8
- FWCI
- 13.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- The Quarterly Journal of Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Keywords
- belief updating, prior beliefs, mental models, computational complexity, context sensitivity
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