Fuller, Gareth (2022) Robustness and Replication: Models, Experiments, and Confirmation. In: UNSPECIFIED.

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Abstract

In this paper I take up a criticism of robustness analysis. Robustness analysis is a method of confirming idealized models where several models with a shared core causal mechanism but different idealizations are constructed. If this set of models produce a shared result, then this is supposed to show that the core mechanism is what is responsible for this response. If this shared result is empirically confirmed, it is argued that this then provides confirmation for the shared core. Robustness analysis has faced several criticisms, one being that, since each model in the robust set is false, no confirmation can be had. Even if they all agree, each model is still false, and agreement among false models confirms nothing. I argue that this concern can be assuaged by understanding the role fo some idealizations as controls for causal factors. I draw an analogy between such idealizations and contrived experimental conditions. From there, I extend the analogy so that robustness analysis can play a similar role to some cases of experimental replication. I finish by considering some concerns about this analogy and some residual fears about idealizations.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Fuller, Gareth gfuller2@ku.edu 0000-0002-0879-8198
Keywords:	Modeling, Idealizations, Robustness Analysis, Confirmation, Replication
Subjects:	General Issues > Confirmation/Induction General Issues > Evidence General Issues > Models and Idealization
Depositing User:	Dr. Gareth Fuller
Date Deposited:	21 Jun 2022 12:59
Last Modified:	21 Jun 2022 12:59
Item ID:	20771
Subjects:	General Issues > Confirmation/Induction General Issues > Evidence General Issues > Models and Idealization
Date:	2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20771

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• Robustness and Replication: Models, Experiments, and Confirmation. (deposited 21 Jun 2022 12:59) [Currently Displayed]
  • Robustness and Replication: Models, Experiments, and Confirmation. (deposited 01 Nov 2022 16:20)

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