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Pragmatic Constraints, Experiments, and Tradeoffs in Scientific Model Building
Ever since Richard Levins’s (1966) influential article “The Strategy of Model Building in Population Biology” there has been a growing discussion surrounding the types of tradeoffs that confront scientific model builders (Orzack and Sober 1993; Odenbaugh 2003; Weisberg 2006, 2012). However, almost all the discussion of Levins’s modeling tradeoffs has focused on what Michael Weisberg calls the ‘representational ideals’ of scientific models. In this paper I argue that this emphasis on representational aims misses many of the pragmatic tradeoffs that scientific modelers confront due to limited experimental data, measurement tools, modeling frameworks, and other modeling resources. In response, this paper aims to investigate the pragmatic modeling tradeoff between (1) having a model be constructable from, and testable against, the available experimental data and (2) building models that are able to generalize across a wide range of contexts of application. I argue that this experiment-applicability tradeoff is a relationship of attenuation rather than a strict or necessary tradeoff between model properties. I then use three case studies to show that, rather than a strict theoretical limit, how this tradeoff is best navigated is highly context sensitive. I then explore the philosophical implications of this tradeoff for how we ought to think about theories as collections of models, how models connect with experiments, and how modelers balance various modeling aims.
Two Arguments for a Counterfactual Account of Model Explanation
In this paper, I argue for the counterfactual account of model explanations by drawing on the recent literature regarding different kinds of explanation in science, the nature of scientific understanding, and the use of idealizations in science. I first identify three important desiderata that arise from considering the explanations provided by highly idealized models. First, a satisfactory account of model explanations must tell us the kind of information provided by models that explain. Second, it must explicate how that information is related to the cognitive achievement of understanding. Finally, the account must show how ineliminable idealizations can make positive contributions to explanations. I contend that the counterfactual account of model explanations satisfies these three desiderata better than other prominent views of how models explain. Consequently, we ought to adopt and continue to develop the counterfactual account.
The Social Organization of Science and the Maintenance of Diversity
The central questions within the study of the social organization of science are what structure and distribution of resources will optimize the outputs of the scientific community as a whole? Several recent accounts assume that different scientific groups have a common target(s)—e.g. certain significant truths—that they aim to achieve (or discover) either competitively, cooperatively, or collectively (Kitcher 1990, 1993; Strevens 2003; Weisberg and Muldoon 2009). Furthermore, several of these accounts assume that making the same discovery through different methods adds nothing of value to scientific inquiry. The optimal organization of science is highly dependent on the epistemic situation we find ourselves in—e.g. the shape of the epistemic landscape and our access to parts of that landscape. In this paper, I use considerations from model-based science to argue that science’s epistemic situation is often in conflict with the assumptions described above. In most instances, different research groups (even those working in similar domains) do not have the same goals, nor should they. In addition, it is often beneficial to discover the same truths through alternative research methods. Consequently, I argue that a more effective organization of science is one that recognizes that multiple incompatible research programs can grant us access to a wider range of scientific truths—a corpus of truths that is inaccessible from any single research paradigm.