Radically new or unexpected findings in a science demand an openness to new concepts and styles of explanation. The time is more than ripe for asking ourselves what we have learned from the research program of comparative genomics. Where not long ago the human genome was expected to reveal a close association of complexity with the quantitative expansion of the roster of unique genes, more recent findings, especially in relation to comparisons between human and chimp, have raised the bracing possibility that when it comes to complexity, it may be that `less is more'. But `less is more' is not the only observation or inference that follows from the data. The idea of `progressive detachment' will be introduced and set forth as the most perspicuous conceptual resource for unifying and interpreting the overall findings from comparative genomics to date.

In this article, I compare the epistemic standing of the function-to-structure inferences found in cognitive neuroscience and of the inferences based on dissociations in neuropsychology. I argue that the former have a poorer epistemic standing than the latter.

Evidence for a hypothesis typically confirms the hypothesis more if the evidence was predicted than if it was accommodated. Or so I argued in previous papers, where I also developed an analysis of why this should be so. But this was all a mistake if Howson and Franklin (1991) are to be believed. In this paper, I show why they are not to be believed. I also identify a grain of truth that may have been dimly grasped by those Bayesians who deny the confirmatory value of prediction.

A tempting argument for human rationality goes like this: it is more conducive to survival to have true beliefs than false beliefs, so it is more conducive to survival to use reliable belief-forming strategies than unreliable ones. But reliable strategies are rational strategies, so there is a selective advantage to using rational strategies. Since we have evolved, we must use rational strategies. In this paper I argue that some criticisms of this argument offered by Stephen Stich fail because they rely on unsubstantiated interpretations of some results from experimental psychology. I raise two objections to the argument: (i) even if it is advantageous to use rational strategies, it does not follow that we actually use them; and (ii) natural selection need not favor only or even primarily reliable belief-forming strategies.

Some features of physical science relevant for a discussion of physical explanation are mentioned. The D-N account of physical explanation is discussed, and it is seen to restrict the scope of explanation in physical science because it imposes the requirement that the explanandum must be deducible from the explanans. Analysis shows that an alternative view of scientific explanation, called the instance view, allows a wider range of physical explanations. The view is seen to be free from a certain class of counter examples to the D-N theory.

The use of a concept called “explicativity”, for (provisionally) accepting a theory or Hypothesis H, has previously been discussed. That previous discussion took into account the prior probability of H, and hence implicitly its theoretical simplicity. We here suggest that a modification of explicativity is required to allow for what may be called the pragmatic simplicity of H, that is, the simplicity of using H in applications as distinct from the simplicity of the description of H.

Intentionality is characteristic of many psychological phenomena. It is commonly held by philosophers that intentionality cannot be ascribed to purely physical systems. This view does not merely deny that psychological language can be reduced to physiological language. It also claims that the appropriateness of some psychological explanation excludes the possibility of any underlying physiological or causal account adequate to explain intentional behavior. This is a thesis which I do not accept. I shall argue that physical systems of a specific sort will show the characteristic features of intentionality. Psychological subjects are, under an alternative description, purely physical systems of a certain sort. The intentional description and the physical description are logically distinct, and are not intertranslatable. Nevertheless, the features of intentionality may be explained by a purely causal account, in the sense that they may be shown to be totally dependent upon physical processes.

This article explores three ways in which physics may involve counterpossible reasoning. The first way arises when evaluating false theories: to say what the world would be like if the theory were true, we need to evaluate counterfactuals with physically impossible antecedents. The second way relates to the role of counterfactuals in characterizing causal structure: to say what causes what in physics, we need to make reference to physically impossible scenarios. The third way is novel: to model metaphysical dependence in physics, we need to consider counterfactual consequences of metaphysical impossibilities. Physics accordingly bears substantial and surprising counterpossible commitments.

Configuration space representations have utility in physics but are not generally taken to have ontological significance. We examine one salient reason to think configuration space representations fail to be relevant in determining the fundamental ontology of a physical theory. This is based on a claim due to several authors (Allori, Dürr, Goldstein, Tumulka, and Zanghì) that fundamental theories must have primitive ontologies. This claim would,if correct, have broad ramifications for how to read metaphysics from physical theory. We survey ways of understanding the argument for a primitive ontology in order to assess the case against configuration space realism.

If p(x1, …, xn) and q(x1 …, xn) are two logically equivalent propositions then p(π(x1), …, π(xn)) and q(π(x1), …,π(xn)) are also logically equivalent where π is an arbitrary permutation of the elementary constituents x1, …, xn. In Quantum Logic the invariance of logical equivalences breaks down. It is proved that the distribution rules of classical logic are in fact equivalent to the meta-linguistic rule of universal substitution and that the more restrictive structure of the substitution group of Quantum Logic prevents us from defining truth in a classical fashion. These observations lead to a more profound understanding of the Logic of Quantum Mechanics and of the role that symmetry principles play in that theory.

… Its decisive difference in comparison to the Classical-Model is the fact that gratings in vector-space defy superposition.

—Hermann Weyl (1949)

In this paper, I discuss the prospects of nominalistic scientific realism (NSR) and show that it fails on many counts. In section 2, I discuss what is required for NSR to get off the ground. In section 3, I question the idea that theories have well-defined nominalistic content and the idea that causal activity is a necessary condition for commitment to the reality of an entity. In section 4, I challenge the notion of nominalistic adequacy of theories.

Many social scientists report that they find it difficult to frame adequate definitions of the important terms of the field. At present they are definition-shy. It is the purpose of this paper to show that this avoidance reaction is neither necessary nor desirable as a means for advancing social science. In order that this be made clear, an analysis of an important term will be presented. First, however, a few preparatory questions about the technique of defining must be asked and answered.

The paper considers recent proposals by Armstrong, Dretske, and Tooley that revive the view that statements of laws of nature are grounded by the existence of higher order facts relating universale. Several objections to such a view are raised and an alternative analysis, recognizing general facts, is considered. Such an alternative is shown to meet a number of the objections raised against the appeal to higher order facts and it is also related to views of Hume and Wittgenstein. Further objections are then raised to all the non-Humean “realist” attempts to provide special facts to ground the laws of nature.

Programs in quantum gravity often claim that time emerges from fundamentally timeless physics. In the semiclassical time program, time arises only after approximations are taken. Here we ask what justifies taking these approximations and show that time seems to sneak in when answering this question. This raises the worry that the approach is either unjustified or circular in deriving time from no-time.

Although Caenorhabditis elegans was chosen and modified to be an organism that would facilitate a reductionist program for neurogenetics, recent research has provided evidence for properties that are emergent from the neurons. While neurogenetic advances have been made using C. elegans which may be useful in explaining human neurobiology, there are severe limitations on C. elegans to explain any significant human behavior.

This essay defends two theses that jointly establish a link between causal and explanatory asymmetry. The first thesis is that statements specifying facts about effects, unlike statements specifying facts about causes, are not “independently variable”. The second thesis is that independent variability among purportedly explanatory factors is a necessary condition on scientific explanations.

Davidson's principle of the anomalousness of the mental was instrumental in discrediting once-popular versions of mind-brain reductionism. In this essay I argue that a novel account of intertheoretic reduction, which does not require the sort of cross-theoretic bridge laws that Davidson's principle rules out, allows a version of mind-brain reductionism which is immune from Davidson's challenge. In the final section, I address a second worry about reductionism, also based on Davidson's principle, that survives this response. I argue that new reductionists should revise some significant details of the program, particularly the conception of theories, to circumvent this more potent Davidson-inspired worry.

The Principle of Indifference, which dictates that we ought to assign two outcomes equal probability in the absence of known reasons to do otherwise, is vulnerable to well-known objections. Nevertheless, the appeal of the principle, and of symmetry-based assignments of equal probability, persists. We show that, relative to a given class of symmetries satisfying certain properties, we are justified in calling certain outcomes equally probable, and more generally, in defining what we call relative probabilities. Relative probabilities are useful in providing a generalized approach to conditionalization. The technique is illustrated by application to simple examples.

Brown and Pooley’s ‘dynamical approach’ to physical theories asserts, in opposition to the orthodox position on physical geometry, that facts about physical geometry are grounded in, or explained by, facts about dynamical fields, not the other way round. John Norton has claimed that the proponent of the dynamical approach is illicitly committed to spatiotemporal presumptions in ‘constructing’ space-time from facts about dynamical symmetries. In this article, I present an abstract, algebraic formulation of field theories and demonstrate that the proponent of the dynamical approach is not committed, in special relativity, to the illicit presumptions to which Norton refers.

Scientists and decision makers rely on climate models for predictions concerning future climate change. Traditionally, physical processes that are key to predicting extreme events are either directly represented (resolved) or indirectly represented (parameterized). Scientists are now replacing physically based parameterizations with neural networks that do not represent physical processes directly or indirectly. I analyze the epistemic implications of this method and argue that it undermines the reliability of model predictions. I attribute the widespread failure in neural network generalizability to the lack of process representation. The representation of climate processes adds significant and irreducible value to the reliability of climate model predictions.