Husserl argues in the Crisis that the prevalent tradition of positive science in his time had a philosophical core, called by him “Galilean science”, that mistook the quest for objective theory with the quest for truth. Husserl is here referring to Göttingen science of the Golden Years. For Husserl, theory “grows“ out of the “soil” of the prescientific, that is, pretheoretical, life-world. Scientific truth finally is to be sought not in theory but rather in the pragmatic-perceptual praxes of measurement. Husserl is faulted for taking measuring processes to be “infinitely perfectible“. The dependence of new scientific phenomena on the existence of prior “prescientific” inductive praxis is analyzed, also Husserl's residual objectivism and failure to appreciate the hermeneutic character of measurement. Though not a scientific (theory-)realist, neither was he an instrumentalist, but he was a scientific (phenomena-)realist.

According to a prominent view of evolutionary theory, natural selection and the processes of development compete for explanatory relevance. Natural selection theory explains the evolution of biological form insofar as it is adaptive. Development is relevant to the explanation of form only insofar as it constrains the adaptation-promoting effects of selection. I argue that this view of evolutionary theory is erroneous. I outline an alternative, according to which natural selection explains adaptive evolution by appeal to the statistical structure of populations, and development explains the causes of adaptive evolution at the level of individuals. Only together can a statistical theory of selection and a mechanical theory of development explain why populations of organisms comprise individuals that are adapted to their conditions of existence.

In this paper we argue that the emergence of the classical world from the underlying quantum reality involves two elements: self-induced decoherence and macroscopicity. Self-induced decoherence does not require the openness of the system and its interaction with the environment: a single closed system can decohere when its Hamiltonian has continuous spectrum. We show that, if the system is macroscopic enough, after self-induced decoherence it can be described as an ensemble of classical distributions weighted by their corresponding probabilities. We also argue that classicality is an emergent property that arises when the behavior of the system is described from an observational perspective.

David Malament's (1977) well-known result, which is often taken to show the uniqueness of the Poincaré-Einstein convention for defining simultaneity, involves an unwarranted physical assumption: that any simultaneity relation must remain invariant under temporal reflections. Once that assumption is removed, his other criteria for defining simultaneity are also satisfied by membership in the same backward (forward) null cone of the family of such cones with vertices on an inertial path. What is then unique about the Poincaré-Einstein convention is that it is independent of the choice of inertial path in a given inertial frame, confirming a remark in Einstein 1905. Similarly, what is unique about the backward (forward) null cone definition is that it is independent of the state of motion of an observer at a point on the inertial path.

This paper examines the nature of model-based reasoning in the interplay between theory and experiment in the context of biomedical engineering research laboratories, where problem solving involves using physical models. These “model systems” are sites of experimentation where in vitro models are used to screen, control, and simulate specific aspects of in vivo phenomena. As with all models, simulation devices are idealized representations, but they are also systems themselves, possessing engineering constraints. Drawing on research in contemporary cognitive science that construes cognition as occurring in a complex distributed system comprising people and artifacts, I argue that reasoning with model systems is a constraint satisfaction process involving co-construction, manipulation, and revision of mental and physical models.

We discuss the intertwined topics of Fulling non-uniqueness and the Unruh effect. The Fulling quantization, which is in some sense the natural one for an observer uniformly accelerated through Minkowski spacetime to adopt, is often heralded as a quantization of the Klein-Gordon field which is both physically relevant and unitarily inequivalent to the standard Minkowski quantization. We argue that the Fulling and Minkowski quantizations do not constitute a satisfactory example of physically relevant, unitarily inequivalent quantizations, and indicate what it would take to settle the open question of whether a satisfactory example exists. A popular gloss on the Unruh effect has it that an observer uniformly accelerated through the Minkowski vacuum experiences a thermal flux of Rindler quanta. Taking the Unruh effect, so glossed, to establish that the notion of particle must be relativized to a reference frame, some would use it to demote the particle concept from fundamental status. We explain why technical results do not support the popular gloss and why the attempted demotion of the particle concept is both unsuccessful and unnecessary. Fulling non-uniqueness and the Unruh effect merit attention despite these negative verdicts because they provide excellent vehicles for illustrating key concepts of quantum field theory and for probing foundational issues of considerable philosophical interest.

Modularity is one of the most important concepts used to articulate a theory of cognitive architecture. Over the last 30 years, the debate in many areas of the cognitive sciences and in philosophy of psychology about what modules are, and to what extent our cognitive architecture is modular, has made little progress. After providing a diagnosis of this lack of progress, this article suggests a remedy. It argues that the theoretical framework of network science can be brought to bear on the traditional modularity debate, facilitating our progress in articulating a good theory of the human cognitive architecture.

The explanatory significance of equilibrium depends on the underlying dynamics. A number of questions of stability and robustness are relevant. Here I investigate these questions with respect to some simple evolutionary models from my book, Evolution of the Social Contract. These models use the replicator dynamics. In each of these models I identify the equilibria and characterize their local dynamic stability properties. In two of the models, I show that one equilibrium is globally asymptotically stable. I then show that the foregoing results are robust over a large class of adaptive dynamics that might be considered as alternatives to the replicator dynamics. I investigate the structural stability properties of the three models. The question of the structural stability of a model of bargaining with correlated encounters raised by D'Arms, Batterman, and Górny (1998) is answered in the affirmative. The other two models are not structurally stable. Modification of a structurally unstable signaling system to allow for correlated encounters results in a structurally stable model.

This paper examines the relationship between modern theories of microeconomics and macroeconomics and, more generally, it evaluates the prospects of theoretically reducing macroeconomics to microeconomics. Many economists have shown strong interest in providing “microfoundations” for macroeconomics and much of their work is germane to the issue of theoretical reduction. Especially relevant is the work that has been done on what is called The Problem of Aggregation. On some accounts, The Problem of Aggregation just is the problem of reducing macroeconomics to microeconomics. I show how to separate these problems and then try to determine to what extent particular kinds of solutions to The Problem of Aggregation succeed in reducing macroeconomics to microeconomics as well. I argue that reduction is not possible by this means given the current state of microeconomics. I also describe how reduction may be possible by means of (dis)aggregation if microeconomics is supplemented in a certain way with the results of experimental research on individual economic agents.

In this paper I attempt to show that adaptational sentences (i.e. sentences containing the terms “adaptive”, “adapted”, etc.) in evolutionary biology are best interpreted as equivalent to sentences about Darwinian or genetical selection. Thus, the use of adaptational languages does not introduce final purposes or other nonempirical notions into biology. I also try to demonstrate that adaptational sentences and functional sentences are not equivalent in an evolutionary context so that an analysis of function does not dispense with the need for an analysis of adaptation. Finally, it is argued that, although some adaptational sentences might be construed as teleological explanations, given an empirical content, they do not serve as explanations. Rather, they express the outcome of selection, regarded in one way, and regarded in another, they express data for which a theory of evolution must account.

In considering the possibility that the fundamental particles of matter might violate Leibniz's Principle, one is confronted with logical proofs that the Principle is a Theorem of Logic. This paper shows that the proof of that theorem is not universal enough to encompass entities that might not be unique, and also strongly suggests that photons, for example, do violate Leibniz's Principle. It also shows that the existence of non-individuals would imply the breakdown of Quine's criterion of ontological commitment.

This paper places formal learning theory in a broader philosophical context and provides a glimpse of what the philosophy of induction looks like from a learning-theoretic point of view. Formal learning theory is compared with other standard approaches to the philosophy of induction. Thereafter, we present some results and examples indicating its unique character and philosophical interest, with special attention to its unified perspective on inductive uncertainty and uncomputability.

In response to recent recognition of the complexities of scientific change, discussion of the objectivity and the rationality of science has focused on criteria of theory choice. This paper addresses instead the rationality of scientific decisions at the level of ongoing research. It argues that whether or not a realist view of theories is compatible with the historical discontinuities of scientific change, certain realist assumptions are crucial to the rationality of research. The researcher must presume that questions about the existence and the properties of at least some of the “unobservable“ entities he theorizes about or experiments on are answerable on the basis of his work. The rationale of research cannot be understood solely in terms of the desiderata of instrumental utility or the empirical adequacy of theories.

How, if at all, does the internal structure of human phenomenological color space map onto the internal structure of objective reflectance-profile space, in such a fashion as to provide a useful and accurate representation of that objective feature space? A prominent argument (due to Hardin, among others) proposes to eliminate colors as real, objective properties of objects, on grounds that nothing in the external world (and especially not surface-reflectance-profiles) answers to the well-known and quite determinate internal structure of human phenomenological color space. The present paper proposes a novel way to construe the objective space of possible reflectance profiles so that (1) its internal structure becomes evident, and (2) that structure's homomorphism with the internal structure of human phenomenological color space becomes obvious. The path is thus reopened to salvage the objective reality of colors, in the same way that we preserved the objective reality of such features as temperature, pitch, and sourness—by identifying them with some objective feature recognized in modern physical theory.

Biological systems exhibit complexity at all levels of organization. It has recently been argued by Michael Behe that at the biochemical level a type of complexity exists—irreducible complexity—that cannot possibly have arisen as the result of natural, evolutionary processes and must instead be the product of (supernatural) intelligent design. Recent work on self-organizing chemical reactions calls into question Behe's analysis of the origins of biochemical complexity. His central interpretative metaphor for biochemical complexity, that of the well-designed mousetrap that ceases to function if critical parts are absent, is undermined by the observation that typical biochemical systems exhibit considerable redundancy and overlap of function. Real biochemical systems, we argue, manifest redundant complexity—a characteristic result of evolutionary processes.

One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth “all at once”. The second, which we call AE convergence, demands only that for every sentence in the theorist's language, there is a time at which the theorist settles the status of the sentence. The relative difficulties of these criteria are compared for effective and ineffective agents. We then examine in detail how the enrichment of an agent's hypothesis language makes the task of converging to the truth more difficult. In particular, we parametrize first-order languages by predicate and function symbol arity, presence or absence of identity, and quantifier prefix complexity. For nearly each choice of values of these parameters, we determine the senses in which effective and ineffective agents can converge to the complete truth on an arbitrary structure for the language. Finally, we sketch directions in which our learning theoretic setting can be generalized or made more realistic.

Hempel's paradox of the ravens arises from the inconsistency of three prima facie plausible principles of confirmation. This paper uses Camapian inductive logic to (a) identify which of the principles is false, (b) give insight into why this principle is false, and (c) identify a true principle that is sufficiently similar to the false one that failure to distinguish the two might explain why the false principle is prima facie plausible. This solution to the paradox is compared with a variety of other responses and is shown to differ from all of them.