Event Categories: BSPS Choice Group Conjectures and Refutations Popper Seminar Sigma Club
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ONLINE: PhD Student Session: Margherita Harris and Dmitry Ananyev
24 June 2020, 4:30 pm – 6:00 pm
Due to the current COVID-19 situation this event will now take place online via Zoom.
Everyone is welcome to join using a computer with access to the internet and Zoom. To take part just follow these instructions:
- Download Zoom
- Join the event any time from 4:15pm via this link: https://lse.zoom.us/j/95392201287
In science, obtaining the same result through different means (i.e. obtaining a ‘robust’ result) is often seen as a valid way to further confirm that result. The Bayesian should of course have something to say about the logic underpinning this method of confirmation. But, as Schupbach (2018) persuasively argues, Bayesian accounts of robustness analysis (RA) which rely on probabilistic independence to explicate the notion of RA diversity are in many cases woefully inadequate. Given this, it seems evident that in order to capture those cases we must depart from independence-based accounts of RA diversity. Schupbach’s explanatory account is arguably a promising step in the right direction. Indeed, by having ‘as its central notions explanation and elimination’, this account seems to fit very nicely with many empirically driven cases of RA in science, while at the same time providing important normative implications. In this talk, however, I will assess Schupbach’s further claim that his account of RA ‘applies to model-based RAs just as well as it does to empirically driven RAs’, since when we arrive at this claim, he and I decisively part ways. And Schupbach is not alone in making this claim. Winsberg (2018), for instance, has also argued that Shupbach’s account can successfully be applied to climate model-based RAs. But in this talk, I will argue, contrary to both Schupbach and Winsberg, that this explanatory account of RA cannot be applied to model-based RAs in the way they suggest. Finally, I will comment on what lessons we might be able to learn from this fact, lessons about the viability of model-based robustness analysis as a method of confirmation.