Algorithmic randomness in empirical data
According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that ind...
| Publicado en: | Studies in History & Philosophy of Science Part A Vol. 34; no. 3; pp. 633 - 647 |
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| Formato: | Artículo |
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Elsevier B.V.
Sep2003
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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=10694278&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 10694278 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00393681 HPS jtl: Studies in History & Philosophy of Science Part A issn: 00393681 maglogo: N pubinfo: dt: Sep2003 vid: 34 iid: 3 pid: 2410 pub: Elsevier B.V. artinfo: ui: 10694278 10.1016/S0039-3681(03)00047-5 ppf: 633 ppct: 14 formats: tig: atl: Algorithmic randomness in empirical data aug: au: McAllister, James W. affil: Faculty of Philosophy, University of Leiden, P.O. Box 9515, 2300 RA, Leiden, Netherlands su: Algorithms Perturbation theory Information theory Science sug: subj: Algorithms Perturbation theory Information theory Science keyword: Algorithmic randomness Compression Empirical data Information Law Pattern ab: According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2003 holdings: @attributes: islocal: N |
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