Amalgamating evidence of dynamics.
Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the sys...
| Publicado en: | Synthese Vol. 196; no. 8; pp. 3213 - 3231 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Aug2019
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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=hlh&AN=137558403&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 137558403 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Aug2019 vid: 196 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 137558403 10.1007/s11229-017-1568-8 ppf: 3213 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 600KB tig: atl: Amalgamating evidence of dynamics. aug: au: Danks, David Plis, Sergey affil: Departments of Philosophy and Psychology, 161 Baker Hall, Carnegie Mellon University, Pittsburgh, PA, USA The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM, USA su: Scientific method Evidence Time series analysis Time measurements sug: subj: Scientific method Evidence Time series analysis Time measurements keyword: Causal discovery Causal inference Dynamical systems Latent variables Timescale ab: Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system's behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially none of them correspond to the underlying system's causal timescale. Second, missing variables have a significantly different impact on time series measurements than they do in the traditional static setting; in particular, they make causal and structural inference much more difficult. In this paper, we argue that amalgamation should proceed by integrating causal knowledge, rather than at the level of "raw" evidence. We defend this claim by first outlining both of these problems, and then showing that they can be solved only if we operate on causal structures. We therefore must use causal discovery methods that are reliable given these problems. Such methods do exist, but their successful application requires careful consideration of the problems that we highlight. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2019. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2019 holdings: @attributes: islocal: N |
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