Does Decision Quality (Always) Increase With the Size of Information Samples? Some Vicissitudes in Applying the Law of Large Numbers.
Adaptive decision making requires that contingencies between decision options and their relative assets be assessed accurately and quickly. The present research addresses the challenging notion that contingencies may be more visible from small than from large samples of observations. An algorithmic...
| Publicado en: | Journal of Experimental Psychology. Learning, Memory & Cognition Vol. 32; no. 4; pp. 883 - 904 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Psychological Association
July 2006
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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=507898014&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507898014 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02787393 EXL jtl: Journal of Experimental Psychology. Learning, Memory & Cognition issn: 02787393 maglogo: N pubinfo: dt: July 2006 vid: 32 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 507898014 10.1037/0278-7393.32.4.883 ppf: 883 ppct: 21 formats: tig: atl: Does Decision Quality (Always) Increase With the Size of Information Samples? Some Vicissitudes in Applying the Law of Large Numbers. aug: au: Fiedler, Klaus Kareev, Yaakov su: Choice (Psychology) Statistical sampling sug: subj: Choice (Psychology) Statistical sampling ab: Adaptive decision making requires that contingencies between decision options and their relative assets be assessed accurately and quickly. The present research addresses the challenging notion that contingencies may be more visible from small than from large samples of observations. An algorithmic account for such a seemingly paradoxical effect is offered within a satisficing-choice framework. Accordingly, a choice is only made when the sample contingency describing the relative evaluation of the 2 options exceeds a critical threshold. Small samples, because of the high dispersion of their sampling distribution, facilitate above-threshold contingencies. Across a broad range of parameters, the resulting small-sample advantage in terms of hits is stronger than their disadvantage in false alarms. Computer simulations and experiments support the model predictions. The relative advantage of small samples is most apparent when information loss is low, when the threshold is high relative to the ecological contingency, and when the sampling process is self-truncated. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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