The Robust Beauty of Ordinary Information.
Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to ma...
| Publicado en: | Psychological Review Vol. 117; no. 4; pp. 1259 - 1267 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
October 2010
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| Materias: | |
| 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=511531772&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511531772 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: October 2010 vid: 117 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 511531772 10.1037/a0020418 ppf: 1259 ppct: 8 formats: tig: atl: The Robust Beauty of Ordinary Information. aug: au: Katsikopoulos, Konstantinos V. Schooler, Lael J. Hertwig, Ralph su: Heuristic Prompts (Psychology) Theory of knowledge Inference (Logic) sug: subj: Heuristic Prompts (Psychology) Theory of knowledge Inference (Logic) ab: Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues that do not entail individual learning. Then we propose and test the thesis that when orders are learned individually, people's necessarily limited knowledge will curtail computational complexity while also achieving robustness. Using computer simulations, we compare the performance of the take-the-best heuristic — with dichotomized or undichotomized cues — to benchmarks such as the naïve Bayes algorithm across 19 environments. Even with minute sizes of training sets, take-the-best using undichotomized cues excels. For 10 environments, we probe people's intuitions about the direction of the correlation between cues and criterion. On the basis of these intuitions, in most of the environments take-the-best achieves the level of performance that would be expected from learning cue orders from 50% of the objects in the environments. Thus, ordinary information about cues — either gleaned from small training sets or intuited — can support robust performance without requiring Herculean computations. 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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