The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...) are obscured largely because the received view fails to adequately separate psychology from methodology or to recognise ambiguities in the use of ''ad hoc''. (shrink)

Chaos-related obstructions to predictability have been used to challenge accounts of theory validation based on the agreement between theoretical predictions and experimental data (Rueger & Sharp, 1996. The British Journal for the Philosophy of Science, 47, 93–112; Koperski, 1998. Philosophy of Science, 40, 194–212). These challenges are incomplete in two respects: (a) they do not show that chaotic regimes are unpredictable in principle (i.e., with unbounded resources) and, as a result, that there is something conceptually wrong with idealized expectations of (...) correct predictions from acceptable theories, and (b) they do not explore whether chaos-induced predictive failures of deterministic models can be remedied by stochastic modeling. In this paper we appeal to an asymptotic analysis of state space trajectories and their numerical approximations to show that chaotic regimes are deterministically unpredictable even with unbounded resources. Additionally, we explain why stochastic models of chaotic systems, while predictively successful in some cases, are in general predictively as limited as deterministic ones. We conclude by suggesting that the way in which scientists deal with such principled obstructions to predictability calls for a more comprehensive approach to theory validation, on which experimental testing is augmented by a multifaceted mathematical analysis of theoretical models, capable of identifying chaos-related predictive failures as due to principled limitations which the world itself imposes on any less-than-omniscient epistemic access to some natural systems. (shrink)

Our aim in this paper is to bring the woefully neglected literature on predictive modeling to bear on some central questions in the philosophy of science. The lesson of this literature is straightforward: For a very wide range of prediction problems, statistical prediction rules (SPRs), often rules that are very easy to implement, make predictions than are as reliable as, and typically more reliable than, human experts. We will argue that the success of SPRs forces us to reconsider our views (...) about what is involved in understanding, explanation, good reasoning, and about how we ought to do philosophy of science. (shrink)

In earlier work ( Cleland [2001] , [2002]), I sketched an account of the structure and justification of ‘prototypical’ historical natural science that distinguishes it from ‘classical’ experimental science. This article expands upon this work, focusing upon the close connection between explanation and justification in the historical natural sciences. I argue that confirmation and disconfirmation in these fields depends primarily upon the explanatory (versus predictive or retrodictive) success or failure of hypotheses vis-à-vis empirical evidence. The account of historical explanation that (...) I develop is a version of common cause explanation. Common cause explanation has long been vindicated by appealing to the principle of the common cause. Many philosophers of science (e.g., Sober and Tucker) find this principle problematic, however, because they believe that it is either purely methodological or strictly metaphysical. I defend a third possibility: the principle of the common cause derives its justification from a physically pervasive time asymmetry of causation (a.k.a. the asymmetry of overdetermination). I argue that explicating the principle of the common cause in terms of the asymmetry of overdetermination illuminates some otherwise puzzling features of the practices of historical natural scientists. (shrink)

According to the no miracles argument, scientific realism provides the only satisfactory explanation of the predictive success of science. It is argued in the present article that a different explanatory strategy, based on the posit of limitations to the underdetermination of scientific theory building by the available empirical data, offers a more convincing understanding of scientific success.

The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the paradox (...) fail to account for the predictive utility of universal laws like All ravens are black. (shrink)

This paper investigates whether there is a discrepancy between the stated and actual aims in biomechanical research, particularly with respect to hypothesis testing. We present an analysis of one hundred papers recently published in The Journal of Experimental Biology and Journal of Biomechanics, and examine the prevalence of papers which (a) have hypothesis testing as a stated aim, (b) contain hypothesis testing claims that appear to be purely presentational (i.e. which seem not to have influenced the actual study), and (c) (...) have exploration as a stated aim. We found that whereas no papers had exploration as a stated aim, 58% of papers had hypothesis testing as a stated aim. We had strong suspicions, at the bare minimum, that presentational hypotheses were present in 31% of the papers in this latter group. (shrink)

One of the fundamental questions of life sciences is one of whether there are genuinely random biological processes. An affirmative or negative answer to this question may have important methodological consequences. It appears that a number of biological processes are explicitly classified as random. One of them is the so-called somatic hypermutation. However, closer analysis of somatic hypermutation reveals that it is not a genuinely random process. Somatic hypermutation is called random because the exact outcome of this process is difficult (...) to predict in practice. The case of somatic hypermutation suggests that there may be no scientific evidence of a single case of ontologically random process in the biological world. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli and the origin of eukaryotic cells through endosymbiosis. †To contact the author, please write to: Philosophy Department, University of California, Santa Cruz, (...) 1156 High St., Santa Cruz, CA 95064; e‐mail: rgw@ucsc.edu ; rgwinther@gmail.com. (shrink)