Various senses in which laws of nature are supposed to be “universal” are distinguished. Conditions designed to capture the content of the more important of these senses are proposed and the relations among these conditions are examined. The status of universality requirements is briefly discussed.

Biological species have been treated traditionally as spatiotemporally unrestricted classes. If they are to perform the function which they do in the evolutionary process, they must be spatiotemporally localized individuals, historical entities. Reinterpreting biological species as historical entities solves several important anomalies in biology, in philosophy of biology, and within philosophy itself. It also has important implications for any attempt to present an “evolutionary” analysis of science and for sciences such as anthropology which are devoted to the study of single species.

In formal theories of measurement meaningfulness is usually formulated in terms of numerical statements that are invariant under admissible transformations of the numerical representation. This is equivalent to qualitative relations that are invariant under automorphisms of the measurement structure. This concept of meaningfulness, appropriately generalized, is studied in spaces constructed from a number of conjoint and extensive structures some of which are suitably interrelated by distribution laws. Such spaces model the dimensional structures of classical physics. It is shown that this qualitative concept corresponds exactly with the numerical concept of dimensionally invariant laws of physics.

Bayesian decision theory can be viewed as the core of psychological theory for idealized agents. To get a complete psychological theory for such agents, you have to supplement it with input and output laws. On a Bayesian theory that employs strict conditionalization, the input laws are easy to give. On a Bayesian theory that employs Jeffrey conditionalization, there appears to be a considerable problem with giving the input laws. However, Jeffrey conditionalization can be reformulated so that the problem disappears, and in fact the reformulated version is more natural and easier to work with on independent grounds.

The Conventionality of Simultaneity espoused by Reichenbach, Grünbaum, Edwards, and Winnie is herein extended to mechanics and electrodynamics. The extension is seen to be a special case of a generally covariant formulation of physics, and therefore consistent with Special Relativity as the geometry of flat space-time. Many of the quantities of classical physics, such as mass, charge density, and force, are found to be synchronization dependent in this formulation and, therefore, in Reichenbach's terminology, “metrogenic.” The relationship of these quantities to 4-vectors and their physical significance is discussed.

Question: What is a (or the) scientific theory V based on a set B of syntactical L -formulas, interpreted according to the intended interpretations of the language L? What probably corresponds to the traditional candidate for V is found to be inadequate for use in deductively explaining experimental facts of a certain form. A second candidate for V, called a strong scientific theory (SST), does not suffer such an inadequacy because it is existentially strong, i.e., it has considerable existential import. It even satisfies the particle physicist's dictum: WHAT IS NOT FORBIDDEN WILL HAPPEN.

It is argued that the intuitively sanctioned distinction between beliefs and non-belief states that play a role in the proximate causal history of beliefs is a distinction worth preserving in cognitive psychology. The intuitive distinction is argued to rest on a pair of features exhibited by beliefs but not by subdoxastic states. These are access to consciousness and inferential integration. Harman's view, which denies the distinction between beliefs and subdoxastic states, is discussed and criticized.

Hintikka has criticized psychologists for “hasty epistemologizing,” which he takes to be an unwarranted transfer of ideas from psychology (a discipline dealing with questions of fact) into epistemology (a discipline dealing with questions of method and theory). Hamlyn argues, following Hintikka, that Gibson's theory of perception is an example of such an inappropriate transfer, especially insofar as Hamlyn feels Gibson does not answer several important questions. However, Gibson's theory does answer the relevant questions, albeit in a new and radical way, which suggests that the alleged distinction between psychology and epistemology is suspect. In fact, contrary to Hintikka and Hamlyn's claims, Gibson's theory of perception appears to be a valuable source of epistemological as well as psychological ideas.

It has been the dominant view that probabilistic explanations of particular facts must be inductive in character. I argue here that this view is mistaken, and that the aim of probabilistic explanation is not to demonstrate that the explanandum fact was nomically expectable, but to give an account of the chance mechanism(s) responsible for it. To this end, a deductive-nomological model of probabilistic explanation is developed and defended. Such a model has application only when the probabilities occurring in covering laws can be interpreted as measures of objective chance, expressing the strength of physical propensities. Unlike inductive models of probabilistic explanation, this deductive model stands in no need of troublesome requirements of maximal specificity or epistemic relativization.

The purported failures of ergodic theory (seen in its often proved ineptitude to ground a mechanical explanation of thermodynamics) are shown to arise from misconception of the functions served by scientific explanation. In fact, the predictive failures of ergodic theory are precisely its points of greatest physical utility, where genuinely new knowledge about actual physical systems can be obtained, once the links between explanation and reconstructive estimation are recognized.

In this paper the concept of supervenience is employed to explain the relationship between fitness as employed in the theory of natural selection and population biology and the physical, behavioral and ecological properties of organisms that are the subjects of lower level theories in the life sciences. The aim of this analysis is to account simultaneously for the fact that the theory of natural selection is a synthetic body of empirical claims, and for the fact that it continues to be misconstrued, even by biologists, for a tautological system. The notion of supervenience is then employed to provide a new statement of the relation of Mendelian predicates to molecular ones in order to provide for the commensurability and potential reducibility of Mendelian to molecular genetics in a way that circumvents the theoretical complications which appear to stand in the way of such a reduction.

I invoke the conceptual machinery of contemporary possible-world semantics to provide an account of the metaphysical status of “bridge laws” in intertheoretic reductions. I argue that although bridge laws are not definitions, and although they do not necessarily reflect attribute-identities, they are supervenient. I.e., they are true in all possible worlds in which the reducing theory is true.

This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic difference. On Greeno's view, the essence of explanation lies in the relevance relations expressed by the conditional probabilities that relate the explanans variables to the explanandum variables; on my view, in contrast, the essence of explanation lies in theories viewed as hypothetical structures which deductively entail conditional probability distributions linking the explanans variables and the explanandum variables. The explanatory power of a stochastic theory is identified with information (regarding the values of explanandum variables) which is “absorbed from” the explanans variables. While other information which is “absorbed from” the explanandum variables (through the process of parameter estimation, for example) reflects descriptive power of the theory. I prove that Greeno's measure of transmitted information is a limiting special case of the E-I model, but that the former, unlike the latter, makes no distinction between explanatory power and descriptive power.

This paper consists of two parts. In Part I, an attempt to get around certain well-known criticisms of the trace theory of memory is discussed. Part II consists of an account of the so-called “logical” notion of a memory trace.

Trace theories are sometimes thought to be empirical hypotheses about the functioning of memory. That this is not the case, that trace theories are in fact philosophical theories, is shown, I believe, in the arguments which follow. If this is so, one may well wonder about psychologists' insistence that any empirical theory of memory must involve the postulation of traces (or trace-like entities: engrams, schemata, etc.).

Past criticisms to the contrary, methodological individualism in the social sciences is neither trivial nor obviously false. In the style of Weber's sociology, it restricts the ultimate explanatory repertoire of social science to agents' reasons for action. Although this restriction is not obviously false, it ought not to be accepted, at present, as a regulative principle. It excludes, as too far-fetched to merit investigation, certain hypotheses concerning the influence of objective interests on large-scale social phenomena. And these hypotheses, in fact, merit empirical consideration. The attractiveness of methodological individualism as a regulative principle depends on two independent confusions, the conflation of an agent's reasons for action with the beliefs, needs, desires, or goals which are the reasons why he acted as he did, and the identification of explaining a phenomenon and describing its causes.

This paper presents a theory of conclusions based upon the suggestions of Tukey [21]. The logic offered here is based upon two rules of detachment that occur naturally in probabilistic inference, a traditional rule of acceptance, and a rule of rejection. The rules of detachment provide flexibility: the theory of conclusions can account for both statistical and deductive arguments. The rule of acceptance governs the acceptance of new conclusions, is a variant of the rule of high probability, and is a limiting case of a decision-theoretic rule of acceptance. The rule of rejection governs the removal of previously accepted conclusions on the basis of new evidence. The resulting theory of conclusions is not a decision-theoretic logic but does, through the aforementioned limiting property, provide a line of demarcation between decision and conclusion (i.e., nondecision) logics of acceptance. The theory of conclusions therefore complements decision-theoretic inference.

The theory of conclusions presented here satisfies Tukey's desiderata, specifically:

1. (1) conclusions are statements which are accepted on the basis of unusually strong evidence;

2. (2) conclusions are to remain accepted unless and until unusually strong evidence to the contrary arises;

3. (3) conclusions are subject to future rejection, when and if the evidence against them becomes strong enough.

Finally, the preferred theory of conclusions has a strong conservative bias, reflecting Tukey's aims.

Taking reduction in the traditional deductive sense, the programmatic claim that most of genetics can be reduced by molecular genetics is defended as feasible and significant. Arguments by Ruse and Hull that either the relationship is replacement or at best a weaker form of reduction are shown to rest on a mixture of historical and logical confusions about the nature of the theories involved.

1. Confirmation Theory One, which we shall construct, when applied to the Raven Hypothesis yields the following results:

1. (1.1) Any black raven confirms the Raven Hypothesis.

2. (1.2) Any black non-raven confirms the Raven Hypothesis.

3. (2) Any non-black raven disconfirms the Raven Hypothesis.

4. (3) Any non-black non-raven is neutral to the Raven Hypothesis.

Theory One consists of two parts: (A) six basic concepts from confirmation theory proper, and (B) the underlying logic.

In this paper a model is presented for the growth of knowledge in a dynamic scientific system. A system which is in some respects an idealization of a Lakatosian research program. The kinematics of the system is described in terms of two probabilistic variables, one of which is related to the evolution of its theoretical component and the other—to the growth of the empirical component. It is shown that when the empirical growth is faster than the theoretical growth the posterior probability of the theoretical component increases. Thus, empirical progressiveness of a research program, as explicated in this model, is accompanied by an increase in the degree of confirmation. In such a case the system grows in a Popperian-like spirit, while learning from experience in a Bayesian manner.