I begin by distinguishing two notions of model, the notion of a truth-making structure and the notion of a mathematical model (in one specific sense). I then argue that although the models of the semantic view have often been taken to be both truth-making structures and mathematical models, this is in part due to a failure to distinguish between two ways of truth-making; in fact, the talk of truth-making is best excised from the view altogether. The result is a version of the semantic view which is better supported by the direct evidence offered for it, better equipped to achieve its avowed aims, and, I think, closer to the intentions of the original proponents of the view in many ways, despite some of their own declarations to the contrary.

The question, what a given statement is “about,” often occurs in philosophic discussion. I shall use this question in the hope of illustrating how a relatively simple application of symbolic logic can clarify a problem which might otherwise turn into a maze of complications.

The propagation of causal influences through space-time seems to play a fundamental role in scientific explanation. Taking as a point of departure a basic distinction between causal interactions (which are localized in space-time) and causal processes (which may extend through vast regions of space-time), this paper attempts an analysis of the concept of causal propagation on the basis of the ability of causal processes to transmit “marks.” The analysis rests upon the “at-at” theory of motion which has figured prominently in the resolution of Zeno's arrow paradox. It is argued that this explication does justice to the concept of the ability of causal processes to transmit causal influence without invoking anti-Humean “powers” or “necessary connections.”

This paper argues against the deductive reconstruction of scientific prediction, that is, against the view that in prediction the predicted event follows deductively from the laws and initial conditions that are the basis of the prediction. The major argument of the paper is intended to show that the deductive reconstruction is an inaccurate reconstruction of actual scientific procedure. Our reason for maintaining that it is inaccurate is that if the deductive reconstruction were an accurate reconstruction, then scientific prediction would be impossible.

The problem of idealization in empirical sciences is very rarely taken up in works concerned with the methodology of those sciences. It seems to be common knowledge that in advanced natural sciences references are made to concepts such as “perfectly rigid body,” “material point,” “perfect gas,” etc., but it remains a fact that the most important methodological concepts, concepts which have determined the present-day form of the philosophy of science, have been advanced without regard to the peculiarities of the procedure mentioned above. And yet a moment's consideration suffices to raise doubts as to Carnap's physicalism and/or Popper's falsificationism if we just realize the trivial and commonly known truth that empirical sciences resort to idealizationism. For how are we to imagine a reduction of idealizational concepts to observation terms? And how are we to imagine that theorems which include idealizational concepts imply observation statements which refute, or at least are at variance with, those theorems? But it is a fact that such doubts have neither been raised by the authors of the conceptions mentioned above, nor have they been raised (as far as the present writer is informed) against them.

Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence (OI)—when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, OI is not a sufficient independence condition for warranting robustness arguments. We show that OI evidence can collectively confirm a hypothesis to a lower degree than individual lines of evidence, contrary to the standard assumption undergirding usual robustness arguments. We employ Bayesian networks to represent the ideal empirical scenario for a robustness argument and a variety of ways in which empirical scenarios can fall short of this ideal.

In their penetrating and admirably lucid discussion, “Purposeful and Non-purposeful Behavior,” Professors Rosenblueth and Wiener have considerably clarified the point of view expressed in their earlier paper dealing with the conception of purpose, and recently criticized by me. But while their discussion thus removes some of the difficulties which, I think, stood in the way of acceptance of their position, there yet remain fundamental questions which I do not believe have been adequately dealt with.

These authors rebuke me, with justice, for not indicating my own conception of purposiveness, and they add that this omission weakens my criticisms of their view. That the omission detracts from the interest of my paper is doubtless correct, but that it weakens my criticisms of their position is not similarly obvious. I shall, at any rate, attempt to rectify this defect in the present discussion.

Current theories of the division of cognitive labor are confined to the “context of justification,” assuming exogenous theories. But new theories are made from the same labor that is used for developing existing theories, and if none of this labor is ever allocated to create new alternatives, then scientific progress is impossible. A unified model is proposed in which theories are no longer given but a function of the division of labor in the model itself. The interactions of individuals balancing the exploitation of existing theories and the exploration of new theories results in a robust cyclical pattern.

The variety-of-evidence thesis has been criticized by Bovens and Hartmann. This article points to two limitations of their Bayesian model: the conceptualization of unreliable evidential sources as randomizing and the restriction to comparing full independence to full dependence. It is shown that the variety-of-evidence thesis is rehabilitated when unreliable sources are reconceptualized as systematically biased. However, it turns out that allowing for degrees of independence leads to a qualification of the variety-of-evidence thesis: as Bovens and Hartmann claimed, more independence does not always imply stronger confirmation.

Putnam and Finkelstein have proposed the abandonment of distributivity in the logic of quantum theory. This change results from defining the connectives, not truth-functionally, but in terms of a certain empirical ordering of propositions. Putnam has argued that the use of this ordering (“implication”) to govern proofs resolves certain paradoxes. But his resolutions are faulty; and in any case, the paradoxes may be resolved with no changes in logic. There is therefore no reason to regard the partially ordered set of propositions as a logic—i.e. as embodying a criterion for soundness of proofs. Its role in quantum theory ought to be understood in an entirely different way.

Imagination is important for many things in science: solving problems, interpreting data, designing studies, and much else. Philosophers of imagination typically account for the productive role played by imagination in science by focusing on how imagination is constrained, for example, by self-imposed rules to infer logically or model events accurately. But the constraints offered by these philosophers constrain either too much or not enough, and they can never account for uses of imagination that are needed to break today’s constraints in order to make progress tomorrow. Thus, epistemology of imagination needs to make room for an element of epistemological anarchy.

Heritability estimates obtained from genome-wide association studies (GWAS) are much lower than those of traditional quantitative methods. This phenomenon has been called the “missing heritability problem.” By analyzing and comparing GWAS and traditional quantitative methods, we first show that the estimates obtained from the latter involve some terms other than additive genetic variance, while the estimates from the former do not. Second, GWAS, when used to estimate heritability, do not take into account additive epigenetic factors transmitted across generations, while traditional quantitative methods do. Given these two points we show that the missing heritability problem can largely be dissolved.

Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macrobehavior when undergoing phase transitions. I argue—in agreement with Robert Batterman—that RG explanations are noncausal explanations. However, Batterman misidentifies the reason that RG explanations are noncausal: it is not the case that an explanation is noncausal if it ignores causal details. I propose an alternative argument, according to which RG explanations are noncausal explanations because their explanatory power is due to the application of mathematical operations, which do not serve the purpose of representing causal relations.

This essay endorses the conclusion of Sklar's “Dappled Theories in a Uniform World” that he announces in his abstract, that notwithstanding recent attacks foundational theories are universal in their scope. But Sklar's rejection of a “pluralist ontology” is questioned. It is concluded that so called “foundational” and “phenomenological” theories are on a much more equal footing as sources of knowledge than Sklar would allow, that “giving an ontology” generally involves dealing in idealizations, and that a transfigured “ficitonalism” provides an (in many respects) better model of scientific knowledge than the model of “foundational truths.”

In ‘Hempel and Oppenheim on Explanation’, (see preceding article) Eberle, Kaplan, and Montague criticize the analysis of explanation offered by Hempel and Oppenheim in their ‘Studies in the Logic of Explanation’. These criticisms are shown to be related to the fact that Hempel and Oppenheim's analysis fails to satisfy simultaneously three newly proposed criteria of adequacy for any analysis of explanation. A new analysis is proposed which satisfies these criteria and thus is immune to the criticisms brought against the earlier analysis.

In this paper I investigate two views of theoretical explanation in quantum chemistry, advocated by John Clarke Slater and Charles Coulson. Slater argued for quantum-mechanical rigor, and the primacy of fundamental principles in models of chemical bonding. Coulson emphasized systematic explanatory power within chemistry, and continuity with existing chemical explanations. I relate these views to the epistemic contexts of their disciplines.

This paper explores the relationship between psychology and neurobiology in the context of cognitive science. Are the sciences that constitute cognitive science independent and theoretically autonomous, or is there a necessary interaction between them? I explore Fodor's Multiple Realization Thesis (MRT) which starts with the fact of multiple realization and purports to derive the theoretical autonomy of special sciences (such as psychology) from structural sciences (such as neurobiology). After laying out the MRT, it is shown that, on closer inspection, the argument is either circular or self-undermining—the argument either assumes the very autonomy it seeks to demonstrate or the concluded autonomy is contradicted by the theoretical interdependence invoked by the premises of the argument. Next, I explore a concrete example of multiple realization in the explanation of animal behavior: the convergent evolution of jamming avoidance behaviors in three genera of weakly electric fish. Contrary to the image painted by the MRT, the work on these animals involves a high degree of interaction between the various levels of investigation. The fact that our understanding of electric fish behavior involves functional theories and multiple realization without the kind of disunified science that is supposed to follow from such a situation indicates that the mere fact of multiple realization cannot be the basis for an autonomous psychology.

There have been two principal paradigms for the formulation of the causal relation–logical implication and functional relationship. In this paper, I present a case for preferring the functional relationship formulation and then discuss how the functional relationship formulation may be implemented in the probabilistic case in a manner analogous to the way others have implemented the logical implication formulation in the probabilistic case. I show how the “local independence” assumption found in many models used in the behavioral and social sciences is a consequence of the functional relation concept of probabilistic causality. I also discuss why causal relations are underdetermined in experience and are to be dealt with pragmatically in a hypothesis-testing framework.

Experimental realism aims at striking a middle ground between scientific realism and anti-realism, between the success of experimental physics it would explain and the realism about scientific theories it would supplant. This middle ground reinstates the engineering idea that belief in scientific entities is justified on purely experimental grounds, without any commitment to scientific theories and laws. This paper argues that there is no defensible middle ground to be staked out when it comes to justifying physicists’ belief in colored quarks, and that experimental realism shifts, under analysis, into scientific realism.