1. It seems plausible to say that a language is a system of expressions the use of which is subject to certain rules. It would seem, thus, that learning to use a language is learning to obey the rules for the use of its expressions. However, taken as it stands, this thesis is subject to an obvious and devastating refutation. After formulating this refutation, I shall turn to the constructive task of attempting to restate the thesis in a way which avoids it. In doing so, I shall draw certain distinctions the theoretical elaboration of which will, I believe, yield new insight into the psychology of language and of what might be called “norm conforming behavior” generally. The present paper contains an initial attempt along these lines.

In this paper, I develop Mauricio Suárez's distinction between denotation, epistemic representation, and faithful epistemic representation. I then outline an interpretational account of epistemic representation, according to which a vehicle represents a target for a certain user if and only if the user adopts an interpretation of the vehicle in terms of the target, which would allow them to perform valid (but not necessarily sound) surrogative inferences from the model to the system. The main difference between the interpretational conception I defend here and Suárez's inferential conception is that the interpretational account is a substantial account—interpretation is not just a “symptom” of representation; it is what makes something an epistemic representation of a something else.

Contemporary philosophers interested in value theory appear to be largely concerned with questions of the following sort:

What is value?

What is the meaning of the word ‘good’?

Does the attribution of value to an object have a cognitive, or merely an emotive, significance?

The first question is metaphysical; to ask it is analogous to asking in physics:

What is matter?

What is electricity?

The others are generally treated as semantical questions; to ask them is analogous to asking in statistics:

What is the meaning of the word ‘probable’?

Does an attribution of probability express an objective fact, or merely a subjective attitude?

A basic aim of E. Beth's work in philosophy of science was to explore the use of formal semantic methods in the analysis of physical theories. We hope to show that a general framework for Beth's semantic analysis is provided by the theory of semi-interpreted languages, introduced in a previous paper. After developing Beth's analysis of nonrelativistic physical theories in a more general form, we turn to the notion of the ‘logic’ of a physical theory. Here we prove a result concerning the conditions under which semantic entailment in such a theory is finitary. We argue, finally, that Beth's approach provides a characterization of physical theory which is more faithful to current practice in foundational research in the sciences than the familiar picture of a partly interpreted axiomatic theory.

Recent efforts to argue that nonepistemic values have a legitimate role to play in assessing scientific models, theories, and hypotheses typically either reject the distinction between epistemic and nonepistemic values or incorporate nonepistemic values only as a secondary consideration for resolving epistemic uncertainty. Given that scientific representations can legitimately be evaluated not only based on their fit with the world but also with respect to their fit with the needs of their users, we show in two case studies that nonepistemic values can play a legitimate role as factors that override epistemic considerations in assessing scientific representations for practical purposes.

One task of logic, Peirce held, is to classify arguments so as to determine the validity of each kind. His own classification is interesting because it includes a novel type of argument (called abduction) in addition to the two traditionally recognized types (induction and deduction). It is the purpose of this paper to discuss what Peirce thought to be sufficiently distinctive about abduction to warrant calling it a new kind of argument. But since one finds in his writings on abduction a number of different views it is first necessary to make a few remarks concerning the unity of Peirce's thought.

We wish to give a precise formulation of the intuitive concept: The degree to which the known facts (the evidence) support a given hypothesis.

I examine the epistemic import of collaborative research in science. I develop and defend a functional explanation for its growing importance. Collaborative research is becoming more popular in the natural sciences, and to a lesser degree in the social sciences, because contemporary research in these fields frequently requires access to abundant resources, for which there is great competition. Scientists involved in collaborative research have been very successful in accessing these resources, which has in turn enabled them to realize the epistemic goals of science more effectively than other scientists, thus creating a research environment in which collaboration is now the norm.

Taxonomies of living things and the methods used to produce them changed little with the institutionalization of evolutionary thinking in biology. Instead, the relationships expressed in existing taxonomies were merely reinterpreted as the result of evolution, and evolutionary concepts were developed to justify existing methods. I argue that the delay of the Darwinian Revolution in biological taxonomy has resulted partly from a failure to distinguish between two fundamentally different ways of ordering identified by Griffiths (1974): classification and systematization. Classification consists of ordering entities into classes, groups defined by the attributes of their members; in contrast, systematization consists of ordering entities into systems, more inclusive entities whose existence depends on some natural process through which their parts are related. Evolutionary, or phylogenetic, systematics takes evolutionary descent to be the natural process of interest in biological taxonomy. I outline a general framework for a truly phylogenetic systematics and examine some of its consequences.

The evolutionary problem of the units of selection has elicited a good deal of conceptual work from philosophers. We review this work to determine where the issues now stand.

Critics of the ideal of value-free science often assume that they must reject the distinction between epistemic and nonepistemic values. I argue that this assumption is mistaken and that the distinction can be used to clarify and defend the argument from inductive risk, which challenges the value-free ideal. I develop the idea that the characteristic feature of epistemic values is that they promote, either intrinsically or extrinsically, the attainment of truths. This proposal is shown to answer common objections to the distinction and provide a principled basis for separating legitimate from illegitimate influences of nonepistemic values in scientific inference.

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessing a model’s adequacy-for-purpose differ from assessing its representational accuracy? In addition, responses are given to some objections that might be raised against an adequacy-for-purpose view.

What is commonly known as the Copenhagen interpretation of quantum mechanics, regarded as representing a unitary Copenhagen point of view, differs significantly from Bohr's complementarity interpretation, which does not employ wave packet collapse in its account of measurement and does not accord the subjective observer any privileged role in measurement. It is argued that the Copenhagen interpretation is an invention of the mid-1950s, for which Heisenberg is chiefly responsible, various other physicists and philosophers, including Bohm, Feyerabend, Hanson, and Popper, having further promoted the invention in the service of their own philosophical agendas.

In this article and its sequel, we derive Bayesianism from the following norm: Accuracy—an agent ought to minimize the inaccuracy of her partial beliefs. In this article, we make this norm mathematically precise. We describe epistemic dilemmas an agent might face if she attempts to follow Accuracy and show that the only measures of inaccuracy that do not create these dilemmas are the quadratic inaccuracy measures. In the sequel, we derive Bayesianism from Accuracy and show that Jeffrey Conditionalization violates Accuracy unless Rigidity is assumed. We describe the alternative updating rule that Accuracy mandates in the absence of Rigidity.

It is often claimed that there can be no such thing as a logic of scientific discovery, but only a logic of verification. By ‘logic of discovery’ is usually meant a normative theory of discovery processes. The claim that such a normative theory is impossible is shown to be incorrect; and two examples are provided of domains where formal processes of varying efficacy for discovering lawfulness can be constructed and compared. The analysis shows how one can treat operationally and formally phenomena that have usually been dismissed with fuzzy labels like ‘intuition’ and ‘creativity’.

By means of a critical examination of a number of theses as to the nature of game-playing, the following definition is advanced: To play a game is to engage in activity directed toward bringing about a specific state of affairs, using only means permitted by specific rules, where the means permitted by the rules are more limited in scope than they would be in the absence of the rules, and where the sole reason for accepting such limitation is to make possible such activity.

In this article and its prequel, we derive Bayesianism from the following norm: Accuracy—an agent ought to minimize the inaccuracy of her partial beliefs. In the prequel, we make the norm mathematically precise; in this article, we derive its consequences. We show that the two core tenets of Bayesianism follow from Accuracy, while the characteristic claim of Objective Bayesianism follows from Accuracy together with an extra assumption. Finally, we show that Jeffrey Conditionalization violates Accuracy unless Rigidity is assumed, and we describe the alternative updating rule that Accuracy mandates in the absence of Rigidity.

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power.

Two solutions are offered to the problem of distinguishing “historical” from “functional” associations in cross-cultural surveys. The underlying logic of the mathematical model is discussed and three kinds of association distinguished: hyperdiffusional or purely “historical” association, undiffusional or purely “functional” association, and semidiffusional or mixed “historical-functional” association. Two overland diffusion arcs constitute the test sample; the relationship of social stratification to political complexity constitutes the test problem. A sifting test establishes a bimodal distribution of interval lengths between like types and sifts out repetitions with a lesser interval length than the second mode. A cluster test shows that for the test problem, the “hits” cluster more than the “misses”.

Discovery proceeds in stages of construction, evaluation, and revision. Each of these stages is constrained by what is known or conjectured about what is being discovered. A new characterization of mechanism aids in specifying what is to be discovered when a mechanism is sought. Guidance in discovering mechanisms may be provided by the reasoning strategies of schema instantiation, modular subassembly, and forward/backward chaining. Examples are found in mechanisms in molecular biology, biochemistry, immunology, and evolutionary biology.