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.

The disagreement with regard to the validity of the principle of causality, existing to-day among scientists, has its roots in the diversity of definitions of the principle itself rather than in a problematic scientific situation. As far as the formulation of quantum theory is complete its bearing upon philosophical questions can be fixed with precision provided the questions are phrased intelligibly. But a question is intelligible from a scientific point of view only if it satisfies two conditions: (1) the meaning of its terms must be fixed; (2) it must be in accord with the conventions of the science to which the question is put.

Glymour has developed an account of the confirmation of scientific hypotheses which he advocates as an alternative to the hypothetico-deductive and Bayesian accounts. This account is subject to a counter-example which may be accomodated by a slight modification. So modified it describes an important dimension of confirmation. If the modification of Glymour's account is slightly extended, both the resulting account and the hypothetico-deductive account may be seen as special cases of a Bayesian theory which is immune to Glymour's criticisms.

Some have suggested that certain classical physical systems have undecidable long-term behavior, without specifying an appropriate notion of decidability over the reals. We introduce such a notion, decidability in μ (or d-μ) for any measure μ, which is particularly appropriate for physics and in some ways more intuitive than Ko's (1991) recursive approximability (r.a.). For Lebesgue measure λ, d-λ implies r.a. Sets with positive λ-measure that are sufficiently “riddled” with holes are never d-λ but are often r.a. This explicates Sommerer and Ott's (1996) claim of uncomputable behavior in a system with riddled basins of attraction. Furthermore, it clarifies speculations that the stability of the solar system (and similar systems) may be undecidable, for the invariant tori established by KAM theory form sets that are not d-λ.

The Doomsday argument and anthropic reasoning are two puzzling examples of probabilistic confirmation. In both cases, a lack of knowledge apparently yields surprising conclusions. Since they are formulated within a Bayesian framework, they constitute a challenge to Bayesianism. Several attempts, some successful, have been made in a Bayesian framework that represents credal states by single credence functions to avoid these conclusions, but none of them can do so for all versions of the Doomsday argument. I show that adopting an imprecise framework of probabilistic reasoning allows for a more adequate representation of ignorance and explains away these puzzles.

I defend the theory that the reasons why some event occurred are its causes. Many “counterexamples” to this theory turn on confusing two levels of reasons. We should distinguish the reasons why an event occurred (“first-level reasons”) from the reasons why those reasons are reasons (“second-level reasons”). An example that treats a second-level reason as a first-level reason will look like a counterexample if that second-level reason is not a cause. But second-level reasons need not be first-level reasons.

Reflection and martingale principles are central to models of rational learning. They can be justified in a variety of ways. In what follows we study martingale and reflection principles in the context of measure theory. We give special attention to two approaches for justifying these principles that have not been studied in that context before: diachronic coherence and the value of information. Together with an extant argument based on expected accuracy, these arguments lend support to the thesis that reflection and martingale principles govern rational learning.

Theoretical problems concerning concepts of systems and measurement of behavior were encountered during experimental studies of the effects of electrical stimulation of the brain on the social behavior of a monkey colony. General problems involved in the description and measurement of behavior of natural systems, and especially of organisms are discussed.

In animals with differentiated brain the general process of stimulation may be divided into four subprocesses: input, throughput, transput and output.

Categories of behavior, temporal and spatial units, and logical rules of homogeneity for sorting data and comparing measurements and categories, are discussed. Simple behavioral units are divided into: 1. Individual a. static or postural b. dynamic or gestural 2. Social a. static b. dynamic. Complex behavior is divided into: 1. Simultaneous 2. Sequential 3. Syntactic 4. Roles.

The process of interpretation of data by the observer is assumed to be dependent upon actual behavior of the system, techniques of observation and experimentation, theoretical and technical processes of analysis, and synthesis and integration of data. Expression of behavior in mathematical terms insures greater accuracy than descriptive methods and enables data to be processed with computers.

A common criticism of punctuated equilibria as an evolutionary theory is that it erects a straw man by characterizing the modern synthesis as being devoid of mechanisms that bring about rapid speciation and abrupt changes in morphology. Thompson supports this view and argues that the modern synthesis does not entail gradualism, all-pervasive adaptationism, or extrapolationism and that punctuationists have mischaracterized the theory on all these points; properly understood the synthetic theory is hierarchical and able to explain phenomena at all levels of the hierarchy, thus rendering macroevolutionary theories, such as punctuated equilibria, unnecessary. I argue in this paper that Thompson's approach is overly dependent upon rational reconstruction in the style of the logical empiricists, and as such ignores important sociological and historical factors that when taken into account justify punctuational criticism of the synthetic theory.

Quine and Putnam argued for mathematical realism on the basis of the indispensability of mathematics to science. They claimed that the mathematics that is used in physical theories is confirmed along with those theories and that scientific realism entails mathematical realism. I argue here that current theories of confirmation suggest that mathematics does not receive empirical support simply in virtue of being a part of well confirmed scientific theories and that the reasons for adopting a realist view of scientific theories do not support realism about mathematical entities, despite the use of mathematics in formulating scientific theory.

Case studies of diverse scientific fields show how scientists use a range of resources to generate new interpretative models and to establish their plausibility as explanations of a domain. They accomplish this by manipulating imagistic representations in particular ways. I show that scientists in different domains use the same basic transformations. Common features of these transformations indicate that general cognitive strategies of interpretation, simplification, elaboration, and argumentation are at work. Social and historical studies of science emphasize the diversity of local contexts of practice. However, the existence of common strategies shows that this diversity masks an important repertoire of cognitive strategies. Scientists use this repertoire to adapt their representations to meet the cognitive demands of different contexts of practice. This paper considers the implications of this finding for the notion of scientists as cognitive agents in distributed knowledge-producing systems.

The Aristotelian distinction between actual and potential presence of a substance in a mixture forms part of a conception of mixture which stands in contrast to atomist and Stoic theories as propounded by the ancients. But the central ideas on which these theories are built need not be combined and opposed to one another in precisely the ways envisaged by these ancient theories. This is well illustrated by Duhem, who maintained the Aristotelian idea that the original ingredients are only potentially, and not actually, present in a mixture, but who sided with the Stoics and against Aristotle on the possibility of co-occupancy. I have argued that the Stoic theory cannot dispense with some such notion as the Aristotelian conception of potentiality in allowing the elements to be actually present in a mixture. Here I suggest that some such Aristotelian notion must be at work in a more modern atomic conception of matter if this is to allow elemental substances to be actually present in compounds, which I think is how compounds are usually understood. Analogous issues arise regarding the status of solutions and their components.

The literary and poetic turn in philosophy exudes contempt for science and hostility against technology, both allegedly justified on grounds established by Nietzsche and Heidegger. I try to show that these grounds instead call for an extremely positive assessment of science and technology. It turns out that what Nietzsche and Heidegger describe as our highest achievement, namely, human autonomy, is really made possible by modern experimental physics. And I show how this assessment is borne out by what Heisenberg describes as the lesson of quantum physics.

A physical theory is a construction of thought which is founded on experience so as to constitute knowledge of the natural world. Propositions in physics are constituted of concepts which express the properties and processes of the physical world. For purposes of record and communication concepts are designated by the terms of a language, such as mathematical symbols, and philosophical discussion may be based on linguistic forms. In this essay, however, the element of discussion will be the concept as a mode of thought through which are grasped the attributes of physical things and the relations exemplified in physical processes. The present philosophical analysis of a physical theory, quantum mechanics, is to be carried out by critical analysis of its basic concepts. In particular, the program is to show that in quantum mechanics the concept of physical object exhibits the characters of discontinuity, complementarity, and relativity.

The paper, analyses the role of measurable concepts in deductive explanation. It is shown that such concepts are, although imprecise in a defined sense, exact in that neutral candidates to them do not arise. An analysis is given of the way in which imprecision is related to generalisation, and it is shown how imprecise concepts are incorporated in testable deductive explanations.

Combining quantum mechanics with special relativity requires (i) that a spacetime representation of quantum states be found; (ii) that such states, represented as extended along equal-time hyperplanes, be invariant when transformed from one frame to another; and (iii) that collapses of states be instantaneous in every frame. These requirements are met using branching spacetime, in which probabilities of outcomes are represented by the numerical proportions of branches on which the outcomes occur. Quantum states of systems are then identified with the probability values, built into spacetime along spacelike hypersurfaces, of all possible outcomes of all possible tests to which the systems can be subjected.

This article provides a new context for an established metaphysical debate regarding the problem of persistence. I contend that perdurance, a popular view about persistence which maintains that objects persist by having temporal parts, can be formulated in quantum mechanics due to the existence of a formal analogy between temporal and spatial location. However, this analogy fails due to a ‘no-go’ result which demonstrates that quantum systems cannot be said to have temporal parts in the same way that they have spatial parts. Therefore, if quantum mechanics describes persisting physical objects then those objects cannot be said to perdure.