The ArgumentIn its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior (...) of these systems. In many instances, these computer simulations are not simple number-crunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not with their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a simple, straightforward relation to the theories from which they stem. (shrink)

Evidence-Based Medicine (EBM) developed from the work of clinical epidemiologists at McMaster University and Oxford University in the 1970s and 1980s and self-consciously presented itself as a "new paradigm" called "evidence-based medicine" in the early 1990s. The techniques of the randomized controlled trial, systematic review and meta-analysis have produced an extensive and powerful body of research. They have also generated a critical literature that raises general concerns about its methods. This paper is a systematic review of the critical literature. It (...) finds the description of EBM as a Kuhnian paradigm helpful and worth taking further. Three kinds of criticism are evaluated in detail: criticisms of procedural aspects of EBM (especially from Cartwright, Worrall and Howick), data showing the greater than expected fallibility of EBM (Ioaanidis and others), and concerns that EBM is incomplete as a philosophy of science (Ashcroft and others). The paper recommends a more instrumental or pragmatic approach to EBM, in which any ranking of evidence is done by reference to the actual, rather than the theoretically expected, reliability of results. Emphasis on EBM has eclipsed other necessary research methods in medicine. With the recent emphasis on translational medicine, we are seeing a restoration of the recognition that clinical research requires an engagement with basic theory (e.g. physiological, genetic, biochemical) and a range of empirical techniques such as bedside observation, laboratory and animal studies. EBM works best when used in this context. (shrink)

The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated by a case study of (...) the general circulation models of meteorology, the major simulation models in climate research. (shrink)

Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general model (...) that allows us to assess the quality of numerical solutions in terms of measures of computational errors that are completely interpretable in terms of modeling error. Finally, we emphasize that this type of error analysis involves forms of perturbation analysis that go beyond the basic model-theoretical and statistical/probabilistic tools typically used to characterize the scientific method; this demands that we revise and complement our reconstructive toolbox in a way that can affect our normative image of science. (shrink)

The relatively recent increase in prominence of computer simulations in scientific inquiry gives us more reasons than ever before for asserting that mathematics is a wonderful tool. In fact, a practical knowledge of scientific computation has become essential for scientists working in all disciplines involving mathematics. Despite their incontestable success, it must be emphasized that the numerical methods subtending simulations provide at best approximate solutions and that they can also return very misleading results. Accordingly, epistemological sobriety demands that we clarify (...) the circumstances under which simulations can be relied upon. With this in mind, this paper articulates a general perspective to better understand and compare the strengths and weaknesses of various error-analysis methods. (shrink)

We claim that adjustable parameters play a crucial role in building and applying simulation models. We analyze that role and illustrate our findings using examples from equations of state in thermodynamics. In building simulation models, two types of experiments, namely, simulation and classical experiments, interact in a feedback loop, in which model parameters are adjusted. A critical discussion of how adjustable parameters function shows that they are boon and bane of simulation. They help to enlarge the scope of simulation far (...) beyond what can be determined by theoretical knowledge, but at the same time undercut the epistemic value of simulation models. (shrink)

We claim that adjustable parameters play a crucial role in building and applying simulation models. We analyze that role and illustrate our findings using examples from equations of state in thermodynamics. In building simulation models, two types of experiments, namely, simulation and classical experiments, interact in a feedback loop, in which model parameters are adjusted. A critical discussion of how adjustable parameters function shows that they are boon and bane of simulation. They help to enlarge the scope of simulation far (...) beyond what can be determined by theoretical knowledge, but at the same time undercut the epistemic value of simulation models. (shrink)