Abstract:
Recently, Dardashti, Hartmann, Thébault, and Winsberg (2019) proposed a Bayesian model for analogical inference. In this paper we investigate how their model performs when varying the degree of certainty about the similarity between the source system and the target system. We show that there are circumstances in which the degree of confirmation for the hypothesis about the target system obtained by collecting evidence from the source system goes down when increasing the degree of certainty about the similarity between the source and the target system. We then develop an alternative Bayesian model for analogical inference and show that the direction of the variation of the degree of confirmation always coincides with the direction of the degree of certainty about the similarity between the two systems in this model. We argue that both models are in fact two sides of the same coin. They reflect two different kinds of analogical inference which are suitable in different situations.
Speaker:
Alexander is principal investigator of the DFG funded research project Interventions and Mechanistic Hierarchies (441311834) at the Munich Center for Mathematical Philosophy (MCMP) at the Ludwig Maximilian University of Munich. Before, he was a postdoc at the Department of Theoretical Philosophy at the University of Groningen, Netherlands and at the Düsseldorf Center for Logic and Philosophy of Science (DCLPS) at the Heinrich Heine University Düsseldorf. His research interests lie in philosophy of science and its intersection with epistemology, metaphysics, and philosophy of mind. He is especially interested in causation and related topics. For more information, see Alexander's personal website: www.alexandergebharter.com
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