In his recent book, Time and Chance, David Albert claims that by positing that there is a uniform probability distribution defined, on the standard measure, over the space of microscopic states that are compatible with both the current macrocondition of the world, and with what he calls the “past hypothesis”, we can explain the time asymmetry of all of the thermodynamic behavior in the world. The principal purpose of this paper is to dispute this claim. I argue that Albert's proposal fails in his stated goal—to show how to use the time-reversible dynamics of Newtonian physics to “underwrite the actual content of our thermodynamic experience” (Albert 2000, 159). Albert's proposal can satisfactorily explain why the overall entropy of the universe as a whole is increasing, but it does not and cannot explain the increasing entropy of relatively small, relatively short-lived systems in energetic isolation without making use of a principle that leads to reversibility objections.

Explaining the connection, if any, between simplicity and truth is among the deepest problems facing the philosophy of science, statistics, and machine learning. Say that an efficient truth finding method minimizes worst case costs en route to converging to the true answer to a theory choice problem. Let the costs considered include the number of times a false answer is selected, the number of times opinion is reversed, and the times at which the reversals occur. It is demonstrated that (1) always choosing the simplest theory compatible with experience, and (2) hanging onto it while it remains simplest, is both necessary and sufficient for efficiency.

Critiques of case studies as an epistemic genre usually focus on the domain of justification and hinge on comparisons with statistics and laboratory experiments. In this domain, case studies can be defended by the notion of “infirming”: they use many different bits of evidence, each of which may independently “infirm” the account. Yet their efficacy may be more powerful in the domain of discovery, in which these same different bits of evidence must be fully integrated to create an explanatory account with internal validity.

In this paper I examine a dispositional property that has been receiving increased attention in biology, evolvability. First, I identify three compatible but distinct investigative approaches, distinguish two interpretations of evolvability, and treat the difference between dispositions of individuals versus populations. Second, I explore the relevance of philosophical distinctions about dispositions for evolvability, isolating the assumption that dispositions are intrinsically located. I conclude that some instances of evolvability cannot be understood as purely intrinsic to populations and suggest alternative strategies for resolving this difficulty.

Characterizing the genetic structure of populations is of importance to evolutionary biology, to human disease gene mapping, and to forensic science. Sewall Wright introduced a set of F-statistics to describe population structure in 1951, and he emphasized that these quantities were ratios of variances. Responding to uncertainty over the best way to estimate F-statistics, Weir and Cockerham published a method-of-moments set of estimators in 1984. This paper continues to be widely cited, with over 7,000 citations to date. Some background to the publishing history of the Weir and Cockerham paper is given here, along with subsequent developments and a discussion of current uses of Wright's F-statistics.

It is argued that it is not sufficient to consider only the sentences included in the explanans and explanandum when determining whether they constitute an explanation, but these sentences must always be evaluated relative to a knowledge situation. The central criterion on an explanation is that the explanans in a non-trivial way increases the belief value of the explanandum, where the belief value of a sentence is determined from the given knowledge situation. The outlined theory of explanations is applied to some well-known examples and is also compared to other theories of explanation.

The concept of genetic information is controversial because it attributes semantic properties to what seem to be ordinary biochemical entities. I argue that nucleic acids contain information in a semantic sense, but only about a limited range of effects. In contrast to other recent proposals, however, I analyze genetic information not in terms of a naturalized account of biological functions, but instead in terms of the way in which molecules determine their products during processes known as template-directed syntheses. I argue that determining an outcome in a certain way is constitutive for being an instruction. On this account, the content of genetic information is identified with the template's properties, which determine the product in the way constitutive for instructions.

Traditionally, empiricism has relied on the specialness of human observation, yet science is rife with sophisticated instrumentation and techniques. The present article advances a conception of empirical evidence applicable to actual scientific practice. I argue that this conception elucidates how the results of scientific research can be repurposed across diverse epistemic contexts: it helps to make sense of how evidence accumulates across theory change, how different evidence can be amalgamated and used jointly, and how the same evidence can be used to constrain competing theories in the service of breaking local underdetermination.

An examination of difficulties in three standard accounts of functions leads to the suggestion that sentences of the form “the function of x is to do y” are used to make a variety of different claims, all of which involve a means-end relationship and the idea of design, or use, or benefit. The analysis proposed enables us to see what is right and also wrong with accounts that analyze the meaning of function statements in terms of good consequences, goals, and etiological explanation. It also enables us to show that function sentences can be used in providing various types of explanations, including, in certain cases, noncausal explanations of the presence of the item with the function.

The historical development of a field of human knowledge progresses like the solution of a jig-saw puzzle, the full extent of which is completely unknown. What begins as an ocean may become only a lake; what starts as a grove of trees may develop into a forest. As study advances through the decades, the situation is repeatedly surveyed and the interpretation of the whole is modified to accord with the added information. For these reasons, conceptions and generalizations periodically undergo alteration, as effectively stated and discussed by Cooper (1926). Universal conformity in such conceptions is of course hardly to be expected in matters that deal largely with speculation rather than with simple experimental data.

Mark Colyvan uses applications of mathematics to argue that mathematical entities exist. I claim that his argument is invalid based on the assumption that a certain way of thinking about applications, called ‘the mapping account,’ is correct. My main contention is that successful applications depend only on there being appropriate structural relations between physical situations and the mathematical domain. As a variety of non-realist interpretations of mathematics deliver these structural relations, indispensability arguments are invalid.

Various claims have been made, recently, that Darwin's argumentation in the Origin instantiates and so supports some general philosophical proposal about scientific theorizing, for example, the “semantic view”. But these claims are grounded in various incorrect analyses of that argumentation. A summary is given here of an analysis defended at greater length in several papers by the present author. The historical and philosophical advantages of this analysis are explained briefly. Darwin's argument comprises three distinct evidential cases on behalf of natural selection, cases, that is, for its existence, its adequacy and its responsibility. Theorizing, today, about evolution by natural selection involves a similar structure of evidential and explanatory concerns.

James Joyce's ‘Nonpragmatic Vindication of Probabilism’ gives a new argument for the conclusion that a person's credences ought to satisfy the laws of probability. The premises of Joyce's argument include six axioms about what counts as an adequate measure of the distance of a credence function from the truth. This paper shows that (a) Joyce's argument for one of these axioms is invalid, (b) his argument for another axiom has a false premise, (c) neither axiom is plausible, and (d) without these implausible axioms Joyce's vindication of probabilism fails.

Phase transitions are well-understood phenomena in thermodynamics (TD), but it turns out that they are mathematically impossible in finite SM systems. Hence, phase transitions are truly emergent properties. They appear again at the thermodynamic limit (TL), i.e., in infinite systems. However, most, if not all, systems in which they occur are finite, so whence comes the justification for taking TL? The problem is then traced back to the TD characterization of phase transitions, and it turns out that the characterization is the result of serious idealizations which under suitable circumstances approximate actual conditions.

This paper considers definitions of classical dynamical chaos that focus primarily on notions of predictability and computability, sometimes called algorithmic complexity definitions of chaos. I argue that accounts of this type are seriously flawed. They focus on a likely consequence of chaos, namely, randomness in behavior which gets characterized in terms of the unpredictability or uncomputability of final given initial states. In doing so, however, they can overlook the definitive feature of dynamical chaos—the fact that the underlying motion generating the behavior exhibits extreme trajectory instability. I formulate a simple criterion of adequacy for any definition of chaos and show how such accounts fail to satisfy it.

In July, 1939, a celebration was held at Leland Stanford University to commemorate the hundredth anniversary of the discovery that the cell is the basic unit of all living tissue. Today we are beginning to realize and to appreciate the fact that the symbol is the basic unit of all human behavior and civilization.

The traditional role which correspondence rules, coordinating definitions, or semantical rules, have in a logical analysis of a scientific theory is questioned by providing an alternative analysis. The alternative account suggests that scientific theories are “meaningful” prior to the establishment of correspondence rules, and that correspondence rules are introduced to permit explanation and testing in the “observational” sector. The role of models is briefly assessed in connection with this prior or “antecedent theoretical meaning,” and a causal sequence analysis of a class of correspondence rules is presented which makes explicit the interdependence of scientific theories.

This is the second, mathematically more detailed part of a paper consisting of two articles, the first having appeared in the immediately preceding issue of this Journal∗. It shows that a measurement converts a pure case into a mixture with reducible probabilities. The measurement as such permits no inference whatever as to the state of the physical system subjected to measurement after the measurement has been performed. But because the probabilities after the act are classical and therefore reducible, it is often possible to adjust them so that Von Neumann's projection postulate is true.

Among the more specific features dealt with in Part II is the occurrence of negative joint probabilities for the measurement of non-commuting operators in certain (not all!) quantum states. The general conclusions reached are stated at the end of the article.

It is widely supposed that the scientists in any field use identical standards for evaluating theories. Without such unity of standards, consensus about scientific theories is supposedly unintelligible. However, the hypothesis of uniform standards can explain neither scientific disagreement nor scientific innovation. This paper seeks to show how the presumption of divergent standards (when linked to a hypothesis of dominance) can explain agreement, disagreement and innovation. By way of illustrating how a rational community with divergent standards can encourage innovation and eventually reach consensus, recent developments in geophysics are discussed at some length.

Robots are being extensively used for the purpose of discovering and testing empirical hypotheses about biological sensorimotor mechanisms. We examine here methodological problems that have to be addressed in order to design and perform “good” experiments with these machine models. These problems notably concern the mapping of biological mechanism descriptions into robotic mechanism descriptions; the distinction between theoretically unconstrained “implementation details” and robotic features that carry a modeling weight; the role of preliminary calibration experiments; the monitoring of experimental environments for disturbing factors that affect both modeling features and theoretically unconstrained implementation details of robots. Various assumptions that are gradually introduced in the process of setting up and performing these robotic experiments become integral parts of the background hypotheses that are needed to bring experimental observations to bear on biological mechanism descriptions.