This paper is inter-disciplinary. Its disadvantage is that the author is not sufficiently conversant with the disciplines it is inter. He may however, like Lord Wavell, claim that at least the thread that binds them is his own.

The paper is of philosophic interest in that it is inspired by, and hopes to shed some light on, the notion of an ideal language. It is of interest to social anthropology in that its main subject is kinship structure. It may be of interest to mathematicians in setting a task.

The ubiquitous probabilities of evolutionary theory (ET) spark the question: Which interpretation of probability is the most appropriate for ET? There is reason to think that, whatever we take probabilities in ET to be, they must be consistent with both determinism and indeterminism. I argue that the probabilities used in ET are objective in a realist sense, if not in an indeterministic sense. Furthermore, there are a number of interpretations of probability that are objective and would be consistent with deterministic evolution and indeterministic evolution. However, I suggest that evolutionary probabilities are best understood as propensities of population-level kinds.

The method of Ramsey sentences has been proposed for handling theoretical constructs within a scientific system. Essentially it consists of constructing a certain “monolithic” sentence for an entire theory. In this present paper several improvements are suggested which help to overcome some of the awkward features of the method. In particular we have here many Ramsey sentences rather than just one, each erstwhile primitive theoretical term giving rise to a Ramsey sentence. Such a sentence in effect defines what we call a Ramsey constant. Using Ramsey constants, we attempt to improve the method in important logical and semantical respects. It is suggested also that such constants are of interest for the philosophy of mathematics.

This essay sketches an account of extrapolation in terms of analogical reasoning that generalizes the notion of comparative process tracing. The account offered here relies on something known as chain graphs, which are used to explicate the role of “fingerprints,” or distinctive markers, as a strategy for avoiding an underdetermination problem having to do with spurious analogies. Moreover, I show how the extrapolator's circle, one of the central challenges for extrapolation, is closely tied to distinctive markers and the Markov condition as it applies to chain graphs.

Naive deductivist accounts of confirmation have the undesirable consequence that if E confirms H, then E also confirms the conjunction H·X, for any X—even if X is completely irrelevant to E and H. Bayesian accounts of confirmation may appear to have the same problem. In a recent article in this journal Fitelson (2002) argued that existing Bayesian attempts to resolve of this problem are inadequate in several important respects. Fitelson then proposes a new-and-improved Bayesian account that overcomes the problem of irrelevant conjunction, and does so in a more general setting than past attempts. We will show how to simplify and improve upon Fitelson's solution.

Using H. Whitney's algebra of physical quantities and his definition of a similarity transformation, a family of similar systems (R. L. Causey [3] and [4]) is any maximal collection of subsets of a Cartesian product of dimensions for which every pair of subsets is related by a similarity transformation. We show that such families are characterized by dimensionally invariant laws (in Whitney's sense, [10], not Causey's). Dimensional constants play a crucial role in the formulation of such laws. They are represented as a function g, known as a system measure, from the family into a certain Cartesian product of dimensions and having the property gφ = φg for every similarity φ. The dimensions involved in g are related to the family by means of certain stability groups of similarities. A one-to-one system measure is a proportional representing function, which plays an analogous role in Causey's theory, but not conversely. The present results simplify and clarify those of Causey.

I shall attempt in this paper to give a rounded, if schematic, account of the concept of probability. My central concern will be to clarify the sense in which law-like statements (including ‘statistical’ law-like statements) are made probable by observational data which, in a sense equally demanding analysis, ‘accord’ with them.

Relying on an analysis of the case of gravitational lensing, Hacking argues for a “modest antirealism” in astronomy. It is shown here that neither his scientific arguments nor his philosophical doctrines imply an antirealist conclusion. An alternative, realistic interpretation of gravitational lensing, and of the nature and history of astronomy more generally, is suggested.

A common complaint against the increasing privatization of research is that research that is conducted with the immediate purpose of producing applicable knowledge will not yield knowledge as valuable as that generated in more curiosity-driven, academic settings. In this paper, I make this concern precise and reconstruct the rationale behind it. Subsequently, I examine the case of industry research on the giant magnetoresistance effect in the 1990s as a characteristic example of research undertaken under considerable pressure to produce applicable results. The example permits one to arrive at a more optimistic assessment of the epistemic merits of private, application-driven research. I attempt to specify the conditions that, in this case, advanced the production of interesting and reliable knowledge.

Scientific explanations must bear the proper relationship to the world: they must depict what, out in the world, is responsible for the explanandum. But explanations must also bear the proper relationship to their audience: they must be able to create human understanding. With few exceptions, philosophical accounts of explanation either ignore entirely the relationship between explanations and their audience or else demote this consideration to an ancillary role. In contrast, I argue that considering an explanation’s communicative role is crucial to any satisfactory account of explanation.

Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population biology.

In this article we argue for the importance of studying interdisciplinary collaborations by focusing on the role that good choice and design of model-building frameworks and strategies can play overcoming the inherent difficulties of collaborative research. We provide an empirical study of particular collaborations between economists and ecologists in resource economics. We discuss various features of how models are put together for interdisciplinary collaboration in these cases and show how the use of a coupled-model framework in this case to coordinate and combine background models from ecology and economics provided particular collaborative affordances and clear collaborative gain.

I argue that four of the fundamental claims of those calling themselves ‘genic pluralists’—Philip Kitcher, Kim Sterelny, and Ken Waters—are defective. First, they claim that once genic selectionism is recognized, the units of selection problems will be dissolved. Second, Sterelny and Kitcher claim that there are no targets of selection (interactors). Third, Sterelny, Kitcher, and Waters claim that they have a concept of genic causation that allows them to give independent genic causal accounts of all selection processes. I argue that each one of these claims is either false or misleading. Moreover, the challenge that arises from the availability of genic causal accounts, namely, the inability to choose on rational grounds among genic and higher-level accounts, is unsupported.

Inevitably, much of this reply will concern points on which I disagree with one or other of the commentators. I should start, therefore, by saying that I am delighted to find that their thinking is in many ways similar to my own, and that a serious attempt is under way to clarify the concept of information in biology.

To infer the state of a cause from the states of its effects, independent lines of evidence are preferable to dependent ones. This familiar idea is here investigated, the goal being to identify its presuppositions. Connections are drawn with Reichenbach's (1956) and Salmon's (1984) discussions of the principle of the common cause.

In 2012, the Geological Time Scale, which sets the temporal framework for studying the timing and tempo of all major geological, biological, and climatic events in Earth’s history, had one-quarter of its boundaries moved in a widespread revision of radiometric dates. The philosophy of metrology helps us understand this episode, and it, in turn, elucidates the notions of calibration, coherence, and consilience. I argue that coherence testing is a distinct activity preceding calibration and consilience, and I highlight the value of discordant evidence and trade-offs scientists face in calibration. The iterative nature of calibration, moreover, raises the problem of legacy data.

Using the occasion of the publication of a Blackwell anthology in the philosophy of technology, Philosophy of Technology: The Technological Condition (2003), as a key to the contemporary role of this subdiscipline, this article reviews the current state-of-this-art. Both philosophy of science and philosophy of technology are twentieth century inventions, but each has followed a somewhat different set of philosophical traditions and pursued sometimes divergent questions. Here the primary developments of recent philosophy of technology are examined with emphasis upon issues which might also be of greater interest to philosophers of science. These include epistemological, but also environmental and cultural issues. The bibliographical spread includes references to some fifty recent books in the field.

In this paper, I will develop a nontrivial interpretation of Feyerabend's concept of a hidden anomalous fact. Feyerabend's claim is that some anomalous facts will remain hidden in the absence of alternatives to the theories to be tested. The case of Brownian motion is given by Feyerabend to support this claim. The essential scientific difficulty in this case was the justification of correct and relevant descriptions of Brownian motion. These descriptions could not be simply determined from the available observational data. An examination, however, of this case shows that no alternative theory is or historically was thought to be necessary in order to justify descriptions of Brownian motion that “directly” refute thermodynamics. While Feyerabend's appraisal of this case therefore is incorrect, a sense is developed in which successful alternatives lend inductive support to the correctness of refuting experimental descriptions. Crucial though to the explanation of this support is the notion of arguments that show the possibilities for improving experimental fit.

The main argument of this paper is that an evaluation of the overall explanatory power of a theory is less problematic and more relevant as an assessment of the state of knowledge than evaluation of statistical explanations of single occurrences in terms of likelihoods that are assigned to explananda.