What You See is All There is.
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 pro...
| Publicado en: | Quarterly Journal of Economics Vol. 135; no. 3; pp. 1363 - 1399 |
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| Formato: | Artículo |
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Oxford University Press / USA
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=144383507&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 144383507 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00335533 QJE jtl: Quarterly Journal of Economics issn: 00335533 maglogo: N pubinfo: dt: Aug2020 vid: 135 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 144383507 10.1093/qje/qjaa012 ppf: 1363 ppct: 36 formats: tig: atl: What You See is All There is. aug: au: Enke, Benjamin affil: Harvard University and National Bureau of Economic Research su: Information sharing Signals & signaling Statistical errors Statistical decision making Computational complexity sug: subj: Information sharing Signals & signaling Statistical errors Statistical decision making Computational complexity ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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