A normative constraint on theories about objects which we take to be real is explored: such theories are required to track the properties of the objects which they are theories of. Epistemic views in which observation (and generalizations of it) play a central role, and holist views which see epistemic virtues as applicable only to whole theories, are contrasted in the light of this constraint. It's argued that global-style epistemic virtues can't meet the constraint, although (certain) epistemic views within which (...) observation (and, more generally, thick epistemic access) are centralized do meet it. (shrink)

The 20th century philosophy of science began on a positivistic note. Its focal point was scientific explanation and the hypothetico-deductive (HD) framework of explanation was proposed as the standard of what is meant by “science.” HD framework, its inductive and statistical variants, and other logic-based approaches to modeling scientific explanation were developed long before the dawn of the information age. Since that time, the volume of observational data and power of high performance computing have increased by several orders of magnitude (...) and reshaped the practice and concept of science, and indeed, the philosophy of science. A new observational-inductive (OI) framework for scientific research is emerging due to recent developments in sensors, data systems, computers, and knowledge discovery techniques. We examine the nature of these changes and their impact on the question of what is meant by “science” after discussing five examples of the OI framework, and conclude that the HD and OI frameworks are complementary and synergistic. (shrink)

A stalwart view in the philosophy of science holds that, even when broadly construed so as to include theoretical auxiliaries, theories cannot make direct contact with observations. This view owes much to Bogen and Woodward’s (1988) influential distinction between data and phenomena. According to them, data are typically the kind of things that are observable or measurable like "bubble chamber photographs, patterns of discharge in electronic particle detectors and records of reaction times and error rates in various psychological experiments" (p. (...) 306). Phenomena are physical processes that are typically unobservable. Examples of the latter category include "weak neutral currents, the decay of the proton, and chunking and recency effects in human memory" (ibid.). Theories, in Bogen and Woodward’s view, are utilised to systematically explain, infer and predict phenomena, not data (pp. 305-306). The relationship between theories and data is rather indirect. Data count as evidence for phenomena and the latter in turn count as evidence for theories. This view is becoming increasingly influential (e.g. Prajit K. Basu (2003), Stathis Psillos (2004) and Mauricio Suárez (2005)). In this paper I argue contrary to this view that in various significant and well-known cases theories do make direct contact with the help of suitable auxiliaries. (shrink)

In a recent paper James Bogen and James Woodward denounce a set of views on confirmation that they collectively brand ‘IRS’. The supporters of these views cast confirmation in terms of Inferential Relations between observational and theoretical Sentences. Against IRS accounts of confirmation, Bogen and Woodward unveil two main objections: (a) inferential relations are not necessary to model confirmation relations since many data are neither in sentential form nor can they be put in such a form and (b) inferential relations (...) are not sufficient to model confirmation relations because the former cannot capture evidentially relevant factors about the detection processes and instruments that generate the data. In this paper I have a two-fold aim: (i) to show that Bogen and Woodward fail to provide compelling grounds for the rejection of IRS models and (ii) to highlight some of the models’ neglected merits. (shrink)

The dangers of character reification for cladistic inference are explored. The identification and analysis of characters always involves theory-laden abstraction—there is no theory-free “view from nowhere.” Given theory-ladenness, and given a real world with actual objects and processes, how can we separate robustly real biological characters from uncritically reified characters? One way to avoid reification is through the employment of objectivity criteria that give us good methods for identifying robust primary homology statements. I identify six such criteria and explore each (...) with examples. Ultimately, it is important to minimize character reification, because poor character analysis leads to dismal cladograms, even when proper phylogenetic analysis is employed. Given the deep and systemic problems associated with character reification, it is ironic that philosophers have focused almost entirely on phylogenetic analysis and neglected character analysis. (shrink)