David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Philosophy of Science 64 (2):222-244 (1997)
I argue that the Bayesian Way of reconstructing Duhem's problem fails to advance a solution to the problem of which of a group of hypotheses ought to be rejected or "blamed" when experiment disagrees with prediction. But scientists do regularly tackle and often enough solve Duhemian problems. When they do, they employ a logic and methodology which may be called error statistics. I discuss the key properties of this approach which enable it to split off the task of testing auxiliary hypotheses from that of appraising a primary hypothesis. By discriminating patterns of error, this approach can at least block, if not also severely test, attempted explanations of an anomaly. I illustrate how this approach directs progress with Duhemian problems and explains how scientists actually grapple with them
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Aris Spanos & Deborah G. Mayo (2015). Error Statistical Modeling and Inference: Where Methodology Meets Ontology. Synthese 192 (11):3533-3555.
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Kent W. Staley (2010). Evidence and Justification in Groups with Conflicting Background Beliefs. Episteme 7 (3):232-247.
Aris Spanos (2013). A Frequentist Interpretation of Probability for Model-Based Inductive Inference. Synthese 190 (9):1555-1585.
Kristin Shrader-Frechette (2004). Comparativist Rationality And. Topoi 23 (2):153-163.
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