Inductive knowledge under dominance.
Inductive reasoning aims at constructing rules and models of general applicability from a restricted set of observations. Induction is a keystone in natural sciences, and it influences diverse application fields such as engineering, medicine and economics. More generally, induction plays a major rol...
| Publicado en: | Synthese Vol. 201; no. 6; pp. 1 - 30 |
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| Formato: | Artículo |
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Springer Nature
Jun2023
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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=hlh&AN=163740226&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 163740226 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Jun2023 vid: 201 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 163740226 10.1007/s11229-023-04172-9 ppf: 1 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 617KB tig: atl: Inductive knowledge under dominance. aug: au: Campi, Marco C. affil: Center for the Study of Inductive Methods c/o Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy su: Social dominance Learning Everyday life sug: subj: Social dominance Learning Everyday life keyword: Dominance Generalization Inductive methods Quantitative reliability ab: Inductive reasoning aims at constructing rules and models of general applicability from a restricted set of observations. Induction is a keystone in natural sciences, and it influences diverse application fields such as engineering, medicine and economics. More generally, induction plays a major role in the way humans learn and operate in their everyday life. The level of reliability that a model achieves depends on how informative the observations are relative to the flexibility of the process by which the model is constructed. When the process is articulated so that the model can incorporate descriptive details and subtleties, a large set of informative observations are required to reliably tune the model, whereas models obtained from simple procedures can be tuned with fewer observations. This article introduces the concept of "dominance", which refers to the situation in which a reduced subset of observations suffices to reconstruct the model. A mathematical framework is presented to quantify the reliability of learning procedures as a function of the size of the subset of dominant observations. Although limited in scope, we believe that our study can contribute to the understanding of some fundamental mechanisms by which knowledge is generated from observations in inductive reasoning. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2023. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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