{"title":"Intertheoretic Reduction","authors":"J. Sprenger, S. Hartmann","doi":"10.1093/oso/9780199672110.003.0008","DOIUrl":"https://doi.org/10.1093/oso/9780199672110.003.0008","url":null,"abstract":"We reconsider the Generalized Nagel-Schaffner (GNS) model of reduction and argue that, contrary to a widely held view, it is the right analysis of intertheoretic reduction. In particular, it provides a convincing analysis of the reductive relationship between statistical mechanics and thermodynamics. Then we proceed to a Bayesian analysis of the epistemic value of reduction, showing that intertheoretic (GNS) reduction facilitates flow of confirmation between the reducing and the reduced theory. Specifically, we show that evidence which prior to reduction supported only one of the theories, comes to support the other theory as well. Moreover, a successful reduction increases both the prior and posterior probability of the conjunction of both theories.","PeriodicalId":140328,"journal":{"name":"Bayesian Philosophy of Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128051112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Models, Idealizations and Objective Chance","authors":"J. Sprenger, S. Hartmann","doi":"10.1093/oso/9780199672110.003.0012","DOIUrl":"https://doi.org/10.1093/oso/9780199672110.003.0012","url":null,"abstract":"How does Bayesian inference handle the highly idealized nature of many (statistical) models in science? The standard interpretation of probability as degree of belief in the truth of a model does not seem to apply in such cases since all candidate models are most probably wrong. Similarly, it is not clear how chance-credence coordination works for the probabilities generated by a statistical model. We solve these problems by developing a suppositional account of degree of belief where probabilities in scientific modeling are decoupled from our actual (unconditional) degrees of belief. This explains the normative pull of chance-credence coordination in Bayesian inference, uncovers the essentially counterfactual nature of reasoning with Bayesian models, and squares well with our intuitive judgment that statistical models provide “objective” probabilities.","PeriodicalId":140328,"journal":{"name":"Bayesian Philosophy of Science","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122365927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}