The physical and/or intrinsic connection approach to causation has become prominent in the recent literature, with Salmon, Dowe, Menzies, and Armstrong among its leading proponents. I show that there is a type of causation, causation by disconnection, with no physical or intrinsic connection between cause and effect. Only Hume-style conditions approaches and hybrid conditions-connections approaches appear to be able to handle causation by disconnection. Some Hume-style, extrinsic, absence-relating, necessary and/or sufficient condition component of the causal relation proves to be needed.

An introduction to the model-theoretic approach in the philosophy of science is given and it is argued that this program is further enhanced by the introduction of partial structures. It is then shown that this leads to a natural and intuitive account of both “iconic” and mathematical models and of the role of the former in science itself.

This article introduces and defends a probabilistic measure of the explanatory power that a particular explanans has over its explanandum. To this end, we propose several intuitive, formal conditions of adequacy for an account of explanatory power. Then, we show that these conditions are uniquely satisfied by one particular probabilistic function. We proceed to strengthen the case for this measure of explanatory power by proving several theorems, all of which show that this measure neatly corresponds to our explanatory intuitions. Finally, we briefly describe some promising future projects inspired by our account.

A remarkable hypothesis has recently been advanced by Libet and promoted by Eccles which claims that there is standardly a backwards referral of conscious experiences in time, and that this constitutes empirical evidence for the failure of identity of brain states and mental states. Libet's neurophysiological data are critically examined and are found insufficient to support the hypothesis. Additionally, it is argued that even if there is a temporal displacement phenomenon to be explained, a neurophysiological explanation is most likely.

Functionalism faces a problem in accounting for the semantic powers of beliefs and other mental states. Simple causal considerations will not solve this problem, nor will any appeal to the social utility of semantic interpretations. The correct analysis of semantic representation is a teleological one, in terms of the biological purposes of mental states: whereas functionalism focuses, so to speak, only on the structure of the cognitive mechanism, the semantic perspective requires in addition that we consider the purposes of the cognitive mechanism's parts.

The prevalence of optimality models in the literature of evolutionary biology is testimony to their popularity and importance. Evolutionary biologist R. C. Lewontin, whose criticisms of optimality models are considered here, reflects that “optimality arguments have become extremely popular in the last fifteen years, and at present represent the dominant mode of thought.”

Although optimality models have received little attention in the philosophical literature, these models are very interesting from a philosophical point of view. As will be argued, optimality models are central to evolutionary thought, yet they are not readily accomodated by the traditional view of scientific theories. According to the traditional view, we would expect optimality models to employ general, empirical laws of nature, but they do not. Fortunately, the semantic view of scientific theories, a recent alternative to the traditional view, more readily accomodates optimality models. As we would expect on the semantic view, optimality models can be construed as specifications of ideal systems. These specifications may be used to describe empirical systems—that is, the specifications may have empirical instances. But the specifications are not empirical claims, much less general, empirical laws.

Although philosophers have yet to discuss the general features and uses of optimality models, these topics have stimulated much recent discussion among evolutionary biologists. Their discussions raise a number of precautions concerning the proper use of optimality models. Moreover, many of their caveats can be interpreted as general reminders that 1) optimality models specify ideal systems whose empirical instantiations may be quite restricted, and that 2) optimality models should not be construed as general, empirical laws. As G. F. Oster and E. O. Wilson caution, “the prudent course is to regard optimality models as provisional guides to further empirical research and not necessarily as the key to deeper laws of nature.” It seems, then, that the semantic view of theories is more sensitive to the nature and limitations of optimality models than is the more traditional view of theories.

The distinction between discovery and justification is ambiguous. This obscures the debate over a logic of discovery. For the debate presupposes the distinction. Real discoveries are well established. What is well established is justified. The proper distinctions are three: initial thinking, plausibility, and acceptability. Logic is not essential to initial thinking. We do not need good supporting reasons to initially think of an hypothesis. Initial thoughts need be neither plausible nor acceptable. Logic is essential, as Hanson noted, to both plausibility and acceptability. An hypothesis needs good supporting reasons to be either plausible or acceptable. Such reasons need not be relative to the particular scientific theory undergoing test at the time. There is no fundamental difference between reasons relevant to plausibility and acceptability. The difference is one of degree. Acceptability requires more than plausibility.

Normative naturalism is a view about the status of epistemology and philosophy of science; it is a meta-epistemology. It maintains that epistemology can both discharge its traditional normative role and nonetheless claim a sensitivity to empirical evidence. The first sections of this essay set out the central tenets of normative naturalism, both in its epistemic and its axiological dimensions; later sections respond to criticisms of that species of naturalism from Gerald Doppelt, Jarrett Leplin and Alex Rosenberg.

Philosophers of experiment have acknowledged that experiments are often more than mere hypothesis-tests, once thought to be an experiment's exclusive calling. Drawing on examples from contemporary biology, I make an additional amendment to our understanding of experiment by examining the way that `wide' instrumentation can, for reasons of efficiency, lead scientists away from traditional hypothesis-directed methods of experimentation and towards exploratory methods.

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have suggested treating opaque systems instrumentally, but computer scientists developing strategies for increasing transparency are correct in finding this unsatisfying. Instead, I propose an analysis of transparency as having three forms: transparency of the algorithm, the realization of the algorithm in code, and the way that code is run on particular hardware and data. This targets the transparency most useful for a task, avoiding instrumentalism by providing partial transparency when full transparency is impossible.

“Adaptation” has several meanings which have often been confused, including relations, processes, states, and intrinsic properties. It is used in comparative and historical contexts. “Adaptation” and “environment” may designate probabilistic concepts. Recognition of these points refutes arguments for the notions that: 1) all organisms are perfectly adapted; 2) organisms cannot be ill-adapted and survive or well-adapted and die; 3) adaptation is necessarily relative to the environment; 4) change in environment is necessary for evolution; 5) preadaptation implies teleology. Such notions are associated with metaphysical ideas, and may affect the thinking of biologists.

Stuart Glennan, and the team of Peter Machamer, Lindley Darden, and Carl Craver have recently provided two accounts of the concept of a mechanism. The main difference between these two versions rests on how the behavior of the parts of the mechanism is conceptualized. Glennan considers mechanisms to be an interaction of parts, where the interaction between parts can be characterized by direct, invariant, change-relating generalizations. Machamer, Darden, and Craver criticize traditional conceptualizations of mechanisms which are based solely on parts interacting and introduce a new concept—activity. This essay is an attempt at carving out a relationship between these two philosophical interpretations of a mechanism. I will claim that, rather than being in conflict, Glennan's concept of interaction and Machamer, Darden, and Craver's notion of activity actually complement one another, each emphasizing a missing element of the other.

This essay is, primarily, a discussion of four results about the principle of maximizing entropy (MAXENT) and its connections with Bayesian theory. Result1 provides a restricted equivalence between the two: where the Bayesian model for MAXENT inference uses an “a priori“ probability that is uniform, and where all MAXENT constraints are limited to 0–1 expectations for simple indicator-variables. The other three results report on an inability to extend the equivalence beyond these specialized constraints. Result2 established a sensitivity of MAXENT inference to the choice of the algebra of possibilities even though all empirical constraints imposed on the MAXENT solution are satisfied in each measure space considered. The resulting MAXENT distribution is not invariant over the choice of measure space. Thus, old and familiar problems with the Laplacian principle of Insufficient Reason also plague MAXENT theory. Result3 builds upon the findings of Friedman and Shimony (1971; 1973) and demonstrates the absence of an exchangeable, Bayesian model for predictive MAXENT distributions when the MAXENT constraints are interpreted according to Jaynes's (1978) prescription for his (1963) Brandeis Dice problem. Lastly, Result4 generalizes the Friedman and Shimony objection to cross-entropy (Kullback-information) shifts subject to a constraint of a new odds-ratio for two disjoint events.

The idea of phenotypic novelty appears throughout the evolutionary literature. Novelties have been defined so broadly as to make the term meaningless and so narrowly as to apply only to a limited number of spectacular structures. Here I examine some of the available definitions of phenotypic novelty and argue that the modern synthesis is ill equipped at explaining novelties. I then discuss three frameworks that may help biologists get a better insight of how novelties arise during evolution but warn that these frameworks should be considered in addition to, and not as potential substitutes of, the modern synthesis.

The Analysis of Matter is perhaps best known for marking Russell's rejection of phenomenalism (in both its classical and methodological forms) and his development of a variety of Lockean representationalism–-Russell's causal theory of perception. This occupies Part 2 of the work. Part 1, which is certainly less well known, contains many observations on twentieth-century physics. Unfortunately, Russell's discussion of relativity and the foundations of physical geometry is carried out in apparent ignorance of Reichenbach's and Carnap's investigations in the same period. The issue of conventionalism in its then contemporary form is simply not discussed. The only writers of the period who appear to have had any influence on Russell's conception of the philosophical issues raised by relativity were Whitehead and Eddington. Even the work of A. A. Robb fails to receive any extended discussion;1 although Robb's causal theory is certainly relevant to many of Russell's concerns, especially those voiced in Part 3, regarding the construction of points and the topology of space-time. In the case of quantum mechanics, the idiosyncrasy of Russell's selection of topics is more understandable, since the Heisenberg and Schrödinger theories were only just discovered. Nevertheless, it seems bizarre to a contemporary reader that Russell should have given such emphasis2 to G. N. Lewis's suggestion that an atom emits light only when there is another atom to receive it–-a suggestion reminiscent of Leibniz, and one to which Russell frequently returns. In short, the philosophical problems of modern physics with which Russell deals seem remote from the perspective of post-positivist philosophy of physics.

A proof is given that there does not exist an event, that is not already in the past for some possible distant observer at the (our) moment that the latter is “now” for us. Such event is as “legally” past for that distant observer as is the moment five minutes ago on the sun for us (irrespective of the circumstance that the light of the sun cannot reach us in a period of five minutes). Only an extreme positivism: “that which cannot yet be observed does not yet exist”, can possibly withstand the conclusion concerned. Therefore, there is determinism, also in micro-physics.

Polarization is a topic of intense interest among social scientists, but there is significant disagreement regarding the character of the phenomenon and little understanding of underlying mechanics. A first problem, we argue, is that polarization appears in the literature as not one concept but many. In the first part of the article, we distinguish nine phenomena that may be considered polarization, with suggestions of appropriate measures for each. In the second part of the article, we apply this analysis to evaluate the types of polarization generated by the three major families of computational models proposing specific mechanisms of opinion polarization.

Proponents of the value ladenness of science rely primarily on arguments from underdetermination or inductive risk, which share the premise that we should only consider values where the evidence runs out or leaves uncertainty; they adopt a criterion of lexical priority of evidence over values. The motivation behind lexical priority is to avoid reaching conclusions on the basis of wishful thinking rather than good evidence. This is a real concern, however, that giving lexical priority to evidential considerations over values is a mistake and unnecessary for avoiding the wishful thinking. Values have a deeper role to play in science.

One promising way to investigate the genealogy of norms is by considering not the origin of norms, but rather what makes certain norms more likely to prevail. Emotional responses, I maintain, constitute one important set of mechanisms that affects the cultural viability of norms. To corroborate this, I exploit historical evidence indicating that sixteenth-century etiquette norms prohibiting disgusting actions were much more likely to survive than other sixteenth-century etiquette norms. This case suggests more broadly that research on cultural evolution should pay greater attention to the role of emotion systems in cultural transmission.

This paper investigates what molecular biology has done for our understanding of the gene. I base a new account of the gene concept of classical genetics on the classical dogma that gene differences cause phenotypic differences. Although contemporary biologists often think of genes in terms of this concept, molecular biology provides a second way to understand genes. I clarify this second way by articulating a molecular gene concept. This concept unifies our understanding of the molecular basis of a wide variety of phenomena, including the phenomena that classical genetics explains in terms of gene differences causing phenotypic differences.