Network representation and complex systems.
In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard pr...
| Publicado en: | Synthese Vol. 195; no. 1; pp. 55 - 79 |
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| Formato: | Artículo |
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Springer Nature
Jan2018
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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=126541330&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 126541330 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Jan2018 vid: 195 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 126541330 10.1007/s11229-015-0726-0 ppf: 55 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 612KB tig: atl: Network representation and complex systems. aug: au: Rathkopf, Charles affil: Graduate Center at the City University of New York, 5674 Frist Center, 08544, Princeton, NJ, USA su: Mechanism (Philosophy) Explanation (Linguistics) Social networks Mathematical decomposition Representation (Philosophy) sug: subj: Mechanism (Philosophy) Explanation (Linguistics) Social networks Mathematical decomposition Representation (Philosophy) keyword: Decomposition Explanation Idealization Mechanism Network Representation ab: In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2018. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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