Epistemic diversity and industrial selection bias.
Philosophers of science have argued that epistemic diversity is an asset for the production of scientific knowledge, guarding against the effects of biases, among other advantages. The growing privatization of scientific research, on the contrary, has raised important concerns for philosophers of sc...
| Publicado en: | Synthese Vol. 201; no. 5; pp. 1 - 19 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
May2023
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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=163753054&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 163753054 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: May2023 vid: 201 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 163753054 10.1007/s11229-023-04158-7 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: Epistemic diversity and industrial selection bias. aug: au: Pinto, Manuela Fernández Pinto, Daniel Fernández affil: Department of Philosophy and Center of Applied Ethics, Universidad de los Andes, Cra 1 No. 18a-12, G531, Bogotá, Colombia Independent researcher, Valencia, Spain sug: keyword: Epistemic diversity Industrial selection bias Privatization of science ab: Philosophers of science have argued that epistemic diversity is an asset for the production of scientific knowledge, guarding against the effects of biases, among other advantages. The growing privatization of scientific research, on the contrary, has raised important concerns for philosophers of science, especially with respect to the growing sources of biases in research that it seems to promote. Recently, Holman and Bruner (2017) have shown, using a modified version of Zollman (2010) social network model, that an industrial selection bias can emerge in a scientific community, without corrupting any individual scientist, if the community is epistemically diverse. In this paper, we examine the strength of industrial selection using a reinforcement learning model, which simulates the process of industrial decision-making when allocating funding to scientific projects. Contrary to Holman and Bruner’s model, in which the probability of success of the agents when performing an action is given a priori, in our model the industry learns about the success rate of individual scientists and updates the probability of success on each round. The results of our simulations show that even without previous knowledge of the probability of success of an individual scientist, the industry is still able to disrupt scientific consensus. In fact, the more epistemically diverse the scientific community, the easier it is for the industry to move scientific consensus to the opposite conclusion. Interestingly, our model also shows that having a random funding agent seems to effectively counteract industrial selection bias. Accordingly, we consider the random allocation of funding for research projects as a strategy to counteract industrial selection bias, avoiding commercial exploitation of epistemically diverse communities. 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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