This paper discusses Simpson's paradox and the problem of positive relevance in probabilistic causality. It is argued that Cartwright's solution to Simpson's paradox fails because it ignores one crucial form of the paradox. After clarifying different forms of the paradox, it is shown that any adequate solution to the paradox must allow a cause to be both a negative cause and a positive cause of the same effect. A solution is then given that can handle the form of the paradox that Cartwright's solution ignored, and allows causes to be both a positive and a negative cause of an effect.

First, a comment on a pessimistic note: Salmon says we can't be sure there is any such thing as inductive inference: in demanding that some explanations have the form of correct inductive inferences, “we may be laying down a requirement which cannot be fulfilled.” To doubt that we can fulfill that requirement is to doubt that we can formalize inductive logic. It may be true, but why begin the fight by throwing in the sponge ? It is also true that there are difficulties involved in formalizing acceptance rules for inductive conclusions, but these difficulties may be overcome. It is false to claim that we ‘cannot claim to know exactly’ what these acceptance rules are. Only two years ago I did just that. It was true that my claim was shown to be erroneous a year later, but that fate can befall almost any worthwhile claim.

This essay attempts to provide additional motivation for the data/phenomena framework advocated in Bogen and Woodward, “Saving the Phenomena” (1988).

A conservation law in physics can be either a constraint on the kinds of interaction there could be or a coincidence of the kinds of interactions there actually are. This is an important, unjustly neglected distinction. Only if a conservation law constrains the possible kinds of interaction can a derivation from it constitute a scientific explanation despite failing to describe the causal/mechanical details behind the result derived. This conception of the relation between “bottom-up” scientific explanations and one kind of “top-down” scientific explanation is motivated by several examples from classical and modern physics.

After sorting different structuralist claims, I argue that structural realist ideas are instantiated in the social sciences, providing both clarification of social science research and support for some components of structural realism. My main focus is on three distinct ways that the social sciences can be about structural relations—exemplified by claims about social structure, reduced form structures in causal modeling, and equilibrium explanations—and on the implication of structuralist ideas for thinking about issues concerning causal explanation and nonreductive pictures of the unity of the science.

Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are in general neither predictive nor explanatory and introduce avoidable conceptual confusions. I conclude that a correct understanding of natural selection requires explicitly causal models of reproductive success.

In Executive Order No. 10521, March 17, 1954, President Eisenhower stated: “…only a small fraction of the Federal Funds is being used to stimulate and support the vital basic research which makes possible our practical scientific progress. I believe strongly that this Nation must extend its support of research in basic science.”

In Part I (Philosophy of Science, Vol. 41 No. 2, June, 1974) it was argued that in order to rebut Humean sceptical arguments, and thus show that it is possible for pure science to be rational, we need to reject standard empiricism and adopt in its stead aim oriented empiricism. Part II seeks to articulate in more detail a theory of rational scientific discovery within the general framework of aim oriented empiricism. It is argued that this theory (a) exhibits pure science as a rational enterprise (b) enables us to resolve problems associated with the key notions of simplicity and intelligibility (c) has important implications both for philosophy of science and for scientific practice itself.

I propose a way of formulating scientific laws and magnitude attributions which eliminates ontological commitment to mathematical entities. I argue that science only requires quantitative sentences as thus formulated, and hence that we ought to deny the existence of sets and numbers. I argue that my approach cannot plausibly be extended to the concrete “theoretical” entities of science.

The prescriptive force of methodological rules rests, I argue, on the acceptance of scientific theories; that of the most general methodological rules rests on theories in the philosophy of science, which differ from theories in the several sciences only in generality and abstraction. I illustrate these claims by reference to methodological disputes in social science and among philosophers of science. My conclusions substantiate those of Laudan except that I argue for the existence of transtheoretical goals common to all scientists and concrete enough actually to have bearing on methodology. And I argue that Laudan is committed to such goals himself, willy nilly.

I attempt to complement my earlier critiques of human sociobiology, by offering an account of how evolutionary ideas might legitimately be employed in the study of human social behavior. The main emphasis of the paper is the need to integrate studies of proximate mechanisms and their ontogenesis with functional/evolutionary research. Human psychological complexity makes it impossible to focus simply on specific types of human behavior and ask for their functional significance. For any of the kinds of behavior patterns that have occupied human sociobiologists, the underlying proximate mechanisms are very likely to be linked to a broad spectrum of types of behavior, and we cannot expect that natural selection will have acted directly on any individual element from this spectrum. I illustrate this general point with a specific example, considering the traditional sociobiological account of human incest-avoidance and outlining an alternative approach to the phenomena. The example is intended to show the possibility of a more rigorous and sophisticated successor to human sociobiology, which I call “human behavioral ecology”.

Many philosophers, both past and present, object to materialism not from any romantic anti-scientific bent, but from sheer inability to understand the thesis. It seems utterly inconceivable to some that qualia should exist in a world which is entirely material. This paper investigates the grain objection, a much neglected argument which purports to prove that sensations could not be brain events. Three versions are examined in great detail. The plausibility of the first version is shown to depend crucially on whether one holds a direct or non-direct theory of perception. Only on the latter is this version plausible. An analysis of the second “semantic” version concludes that a materialist description and explanation of the world should not be expected to transparently convey all that would be of interest or importance to human beings. The final version explicitly makes use of Grover Maxwell's non-direct perceptual theory of structural realism. Although a confusion is charged to Maxwell between phenomenal and objective properties, the critical difficulty for the grain objection is its failure to characterize “structure” from a non-percipient point of view. As the grain objection is ultimately found wanting, the real difficulty for materialism crystallizes as its irreconciliability with the mere existence of sentience, which seems to force some sort of emergence upon us.

Experimental modeling in biology involves the use of living organisms (not necessarily so-called model organisms) in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call “in vivo representations,” where one kind of causal process is used to stand in for a physically different kind of process. I discuss the advantages of this approach in the context of evolutionary biology.

This paper evaluates a general argument for the conclusion that visual representations in science must play the role of truth bearers if they are to figure as legitimate contributors to scientific arguments and explanations. The argument is found to be unsound. An alternative approach to assessing the role of visual representations in science is exemplified by an examination of the role of structural formulas in organic chemistry. Structural formulas are found not to play the role of truth bearers; nonetheless, they contribute to the arguments and explanations of organic chemistry. An early success of conformational analysis is presented in order to illustrate the role of structural formulas in the discourse of organic chemistry.

A condition is formulated in terms of the probabilities of two pairs of correlated events in a classical probability space which is necessary for the two correlations to have a single (Reichenbachian) common-cause and it is shown that there exists pairs of correlated events, probabilities of which violate the necessary condition. It is concluded that different correlations do not in general have a common common-cause. It is also shown that this conclusion remains valid even if one weakens slightly Reichenbach's definition of common-cause. The significance of the difference between common-causes and common common-causes is emphasized from the perspective of Reichenbach's Common Cause Principle.

Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims.

Quantum mechanics has sometimes been taken to be an empiricist (vs. realist) theory. I state the empiricist's argument, then outline a recently noticed type of measurement—protective measurement—that affords a good reply for the realist. This paper is a reply to scientific empiricism (about quantum mechanics), but is neither a refutation of that position, nor an argument in favor of scientific realism. Rather, my aim is to place realism and empiricism on an even score in regards to quantum theory.

A twofold taxonomy for emergence is presented into which a variety of contemporary accounts of emergence fit. The first taxonomy consists of inferential, conceptual, and ontological emergence; the second of diachronic and synchronic emergence. The adequacy of weak emergence, a computational form of inferential emergence, is then examined and its relationship to conceptual emergence and ontological emergence is detailed.

Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to refer to is a collection of propositions.