Counterfactuals all the way down? Content Type Journal Article DOI 10.1007/s11016-010-9437-9 Authors Jim Woodward, History and Philosophy of Science, 1017 Cathedral of Learning, University of Pittsburgh, Pittsburgh, PA 15260, USA Barry Loewer, Department of Philosophy, Rutgers University, New Brunswick, NJ 08901, USA John W. Carroll, Department of Philosophy and Religious Studies, North Carolina State University, Raleigh, NC 27695-8103, USA Marc Lange, Department of Philosophy, University of North Carolina at Chapel Hill, CB#3125—Caldwell Hall, Chapel Hill, NC 27599-3125, USA Journal Metascience Online (...) ISSN 1467-9981 Print ISSN 0815-0796 Journal Volume Volume 20 Journal Issue Volume 20, Number 1. (shrink)
This paper makes use of recent empirical results, mainly from experimental economics, to expore the conditions under which people will cooperate and to assess competing explantions of this cooperation. It is argued that the evidence supports the claim that people differ in type, with some being conditional cooperators and others being motivated by more or less sophisticated forms of self-interest. Stable cooperation requires, among other things, rules and institutions that protect conditional cooperators from myopically self-interested types. Additional empirical features of (...) the behavior of conditional cooperators also imply that rules and institutions are required to produce stable cooperation. (shrink)
It is widely believed that robustness (of inferences, measurements, models, phenomena and relationships discovered in empirical investigation etc.) is a Good Thing. However, there are many different notions of robustness. These often differ both in their normative credentials and in the conditions that warrant their deployment. Failure to distinguish among these notions can result in the uncritical transfer of considerations which support one notion to contexts in which another notion is being deployed. This paper surveys several different notions of robustness (...) and tries to identify why (and in what circumstances) each is valuable or appealing. I begin by discussing the notion of robustness addressed in Aldrich's paper (robustness as insensitivity of the results of inference to alternative specifications) and then discuss how this relates to robustness of derivations, robustness of measurement results, and robustness as a mark of casual as opposed to (merely) correlational relationships. (shrink)
'IRS' is our term for the logical empiricist idea that the best way to understand the epistemic bearing of observational evidence on scientific theories is to model it in terms of Inferential Relations among Sentences representing the evidence, and sentences representing hypotheses the evidence is used to evaluate. Developing ideas from our earlier work, including 'Saving the Phenomena'(Phil Review 97, 1988, p.303-52 )we argue that the bearing of observational evidence on theory depends upon causal connections and error characteristics of the (...) processes by which data is produced and used to detect features of phenomena. Neither of these depends upon, or is greatly illuminated by a consideration of, formal relations among observation and theoretical sentences or propositions. By taking causal structures and error characteristics, you too can evade the IRS. In doing so, you can gain insight into Hempel’s raven paradox, theory loading, and other issues from the standard philosophical literature on confirmation theory. (shrink)
This paper develops an account of explanation in biology which does not involve appeal to laws of nature, at least as traditionally conceived. Explanatory generalizations in biology must satisfy a requirement that I call invariance, but need not satisfy most of the other standard criteria for lawfulness. Once this point is recognized, there is little motivation for regarding such generalizations as laws of nature. Some of the differences between invariance and the related notions of stability and resiliency, due respectively to (...) Sandra Mitchell and Brian Skyrms, are explored. (shrink)
This paper explores how data serve as evidence for phenomena. In contrast to standard philosophical models which invite us to think of evidential relationships as logical relationships, I argue that evidential relationships in the context of data-to-phenomena reasoning are empirical relationships that depend on holding the right sort of pattern of counterfactual dependence between the data and the conclusions investigators reach on the phenomena themselves.
This paper defends a counterfactual account of explanation, according to which successful explanation requires tracing patterns of counterfactual dependence of a special sort, involving what I call active counterfactuals. Explanations having this feature must appeal to generalizations that are invariant--stable under certain sorts of changes. These ideas are illustrated by examples drawn from physics and econometrics.
Standard philosophical discussions of theory-ladeness assume that observational evidence consists of perceptual outputs (or reports of such outputs) that are sentential or propositional in structure. Theory-ladeness is conceptualized as having to do with logical or semantical relationships between such outputs or reports and background theories held by observers. Using the recent debate between Fodor and Churchland as a point of departure, we propose an alternative picture in which much of what serves as evidence in science is not perceptual outputs or (...) reports of such outputs and is not sentential in structure. (shrink)