{"title":"稳健性分析确认:贝叶斯论述。","authors":"Lorenzo Casini, Jürgen Landes","doi":"10.1007/s10670-022-00537-7","DOIUrl":null,"url":null,"abstract":"<p><p>Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models <i>can</i> confirm, but its confirmatory value depends on concrete circumstances.</p>","PeriodicalId":13596,"journal":{"name":"Innovation in Aging","volume":"6 1","pages":"367-409"},"PeriodicalIF":4.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10827917/pdf/","citationCount":"0","resultStr":"{\"title\":\"Confirmation by Robustness Analysis: A Bayesian Account.\",\"authors\":\"Lorenzo Casini, Jürgen Landes\",\"doi\":\"10.1007/s10670-022-00537-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models <i>can</i> confirm, but its confirmatory value depends on concrete circumstances.</p>\",\"PeriodicalId\":13596,\"journal\":{\"name\":\"Innovation in Aging\",\"volume\":\"6 1\",\"pages\":\"367-409\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10827917/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Innovation in Aging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s10670-022-00537-7\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/5/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"GERIATRICS & GERONTOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Innovation in Aging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10670-022-00537-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/5/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
Confirmation by Robustness Analysis: A Bayesian Account.
Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances.
期刊介绍:
Innovation in Aging, an interdisciplinary Open Access journal of the Gerontological Society of America (GSA), is dedicated to publishing innovative, conceptually robust, and methodologically rigorous research focused on aging and the life course. The journal aims to present studies with the potential to significantly enhance the health, functionality, and overall well-being of older adults by translating scientific insights into practical applications. Research published in the journal spans a variety of settings, including community, clinical, and laboratory contexts, with a clear emphasis on issues that are directly pertinent to aging and the dynamics of life over time. The content of the journal mirrors the diverse research interests of GSA members and encompasses a range of study types. These include the validation of new conceptual or theoretical models, assessments of factors impacting the health and well-being of older adults, evaluations of interventions and policies, the implementation of groundbreaking research methodologies, interdisciplinary research that adapts concepts and methods from other fields to aging studies, and the use of modeling and simulations to understand factors and processes influencing aging outcomes. The journal welcomes contributions from scholars across various disciplines, such as technology, engineering, architecture, economics, business, law, political science, public policy, education, public health, social and psychological sciences, biomedical and health sciences, and the humanities and arts, reflecting a holistic approach to advancing knowledge in gerontology.