We explicate recent results that shed light on the obscure and troubling problem of renormalization in Quantum Field Theory (QFT). We review how divergent predictions arise in perturbative QFT, and how they are renormalized into finite quantities. Commentators have worried that there is no foundation for renormalization, and hence that QFTs are not logically coherent. We dispute this by describing the physics behind liquid diffusion, in which exactly analogous divergences are found and renormalized. But now we are looking at a problem that is physically and formally well-defined, proving that the problems of renormalization, by themselves, cannot refute QFT.

Attempts at quantification turn up in many areas within the modern courtroom, but nowhere more than in the realm of toxic tort law. Evidence, in these cases, is routinely presented in statistical form. The vagueness inherent in phrases such as ‘balance of probabilities’ and ‘more likely than not’ is reinterpreted to correspond to precise mathematical values. Standing alone these developments would not be a cause for great concern. But in practice courts and commentators have routinely mixed up incompatible quantities, leading to grave injustice. I argue that these confusions result from an unjustified assumption of universal causal determinism.

In The Chances of Explanation, Paul Humphreys presents a metaphysical analysis of causation. In this paper, I argue that this analysis is flawed. Humphreys' model of Causality incorporates three completeness requirements. I show that these completeness requirements, when applied in the world, force us to take causally irrelevant factors to be causally relevant. On this basis, I argue that Humphreys' analysis should be rejected.

If physicalism is true (e.g., if every event is a fundamental-physical event), then it looks as if there is a fundamental-physical explanation of everything. If so, then what is to become of special scientific explanations? They seem to be excluded by the fundamental-physical ones, and indeed to be excellent candidates for elimination. I argue that, if physicalism is true, there probably is a fundamental-physical explanation of everything, but that nevertheless there can perfectly well be special scientific explanations as well, notwithstanding eliminativist scruples concerning overdetermination and Ockham's Razor.

Lewis' argument against the Limit Assumption and Pollock's Generalized Consequence Principle together suggest that “minimal-change” theories of counterfactuals are wrong. The “small-change” theories presented by Nute do not say enough. While these theories rely on closeness between possible worlds, I base an alternative on the ceteris paribus concept. My theory solves a problem that the above cannot, and is more relevant to the philosophy of science. Ceteris paribus conditions should normally include the causes, but exclude the effects, of the negated antecedent. An example from community ecology, the debate over null models in island-biogeographical studies of competition, supports these arguments.

I offer an account of theories useful in addressing the question of whether children are young theoreticians whose development can be regarded as the product of theory change. I argue that to regard a set of propositions as a theory is to be committed to evaluating that set in terms of its explanatory power. If theory change is the substance of cognitive development, we should see patterns of affect and arousal consonant with the emergence and resolution of explanation-seeking curiosity. Affect has largely been ignored as a potential source of support or disconfirmation for the “theory theory” of development.

This paper presents a cognitive account of the process of evaluating scientific data. Our account assumes that when individuals evaluate data, they construct a mental model of a data-interpretation package, in which the data and theoretical interpretations of the data are integrated. We propose that individuals attempt to discount data by seeking alternative explanations for events within the mental model; data-interpretation packages are accepted when the individual cannot find alternative accounts for these events. Our analysis indicates that there are many levels at which data-interpretation packages can be accepted or denied.

In line with the semantic conception of scientific theories, I develop an account of the intertheory relation of comparative structural similarity. I argue that this relation is useful in explaining the concept of verisimilitude and I support this contention with a concrete historical example. Finally, I defend this relation against the familiar charge that the concept of similarity is insufficiently objective.

Visualizations used in the practice of neuroscience, as one example of a scientific practice, can be sorted according to whether they represent (A) actual things, (B) theoretical models, or (C) some integration of these two. In this paper I hypothesize that an assessment of a chain of visual representations from (A) through (C) to (B) (and back again) is used, as part of the practice of scientific judgment, to assess the adequacy of the “working fit” between the theoretical model and the actual thing or process that the model is intended to explain.

This paper responds to criticisms levelled by Fodor, Pylyshyn, and McLaughlin against connectionism. Specifically, I will rebut the charge that connectionists cannot account for representational systematicity without implementing a classical architecture. This will be accomplished by drawing on Paul Smolensky's Tensor Product model of representation and on his insights about split-level architectures.

It is sometimes held that facts confirm a hypothesis only if they were not used in the construction of that hypothesis. This requirement of “use novelty” introduces a historical aspect into the assessment of evidence claims. I examine a methodological principle invoked by physicists in the experimental search for the top quark that bears a striking resemblance to this view. However, this principle is better understood, both historically and philosophically, in terms of the need to conduct a severe test than in terms of use novelty. Nevertheless, a historical factor remains in the assessment of some evidence claims.

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.

In the curve fitting problem two conflicting desiderata, simplicity and goodness-of-fit, pull in opposite directions. To this problem, we propose a solution that strikes a balance between simplicity and goodness-of-fit. Using Bayes’ theorem we argue that the notion of prior probability represents a measurement of simplicity of a theory, whereas the notion of likelihood represents the theory’s goodness-of-fit. We justify the use of prior probability and show how to calculate the likelihood of a family of curves. We diagnose the relationship between simplicity of a theory and its predictive accuracy.

In order for Bayesian inquiry to count as objective, one might argue that it must lead to a consensus among those who use it and share evidence, but presumably this is not enough. It has been proposed that one should also require that the consensus be reached from very different initial opinions by conditioning only on basic experimental evidence, evidence free from subjective, social, or psychological influence. I will argue here, however, that this notion of objectivity in Bayesian inquiry is too narrow.

When can a Bayesian investigator select an hypothesis H and design an experiment (or a sequence of experiments) to make certain that, given the experimental outcome(s), the posterior probability of H will be lower than its prior probability? We report an elementary result which establishes sufficient conditions under which this reasoning to a foregone conclusion cannot occur. Through an example, we discuss how this result extends to the perspective of an onlooker who agrees with the investigator about the statistical model for the data but who holds a different prior probability for the statistical parameters of that model. We consider, specifically, one-sided and two-sided statistical hypotheses involving i.i.d. Normal data with conjugate priors. In a concluding section, using an “improper” prior, we illustrate how the preceding results depend upon the assumption that probability is countably additive.

According to van Fraassen, constructive empiricism yields a better account of science than does scientific realism. One particularly important advantage van Fraassen claims his position to have over scientific realism is that the former can make sense of science without invoking (what he calls) pre-Kantian metaphysics. In the present paper the consistency of van Fraassen’s position is put in doubt. Specifically, it will be argued that van Fraassen faces the paradox that he cannot do with nor without the pre-Kantian metaphysics he abhors.

Scientific realists have argued that the truth(likeness) of our theories provides the only explanation for the success of science. I consider alternative explanations proposed by antirealists. I endorse Leplin’s contention that neither van Fraassen’s Darwinist explanation nor Laudan’s methodological explanation provides the sort of explanatory alternative which is called for in this debate. Fine’s suggestion— that the empirical adequacy of our theories already explains their success—is more promising for antirealists. Leplin claims that this putative explanation collapses into realism on one reading and into vacuity on another reading. But his analysis conflates three doctrines into two, and one of the three avoids both realism and vacuity.

Over the last two decades, the debate over scientific realism has been dominated by two arguments that pull in contrary directions: the ‘no miracle’ argument and the ‘pessimistic induction’. The latter suggests that the historical record destroys the realist’s belief in an explanatory connection between truthlikeness and genuine empirical success. This paper analyzes the structure of the ‘pessimistic induction’, presents a move—the divide et impera move—that neutralizes it, and motivates a substantive yet realistic version of scientific realism. This move is also compared with Worrall’s and Kitcher’s recent reactions to the ‘pessimistic induction’.

Wesley Salmon defends an ontic realism that distinguishes explanatory from descriptive knowledge. Explanatory knowledge makes appeals to (unobservable) theoretical acausal mechanisms. Salmon presents an argument designed both to legitimize attributing truth values to theoretical claims and to justify treating theoretical claims as descriptions. The argument succeeds but only at the price of calling the distinction between explanation and description into question. Even if Salmon’s attempts to distinguish causal mechanisms from other mechanisms are successful, the assumed centrality of the appeal to such mechanisms in providing scientific explanations is left open by Salmon’s account.

This paper proposes a logic, motivated by modal interpretations, in which every quantum mechanics propositions has a truth-value. This logic is completely classical, hence violates the conditions of the Kochen-Specker theorem. It is shown how the violation occurs, and it is argued that this violation is a natural and acceptable consequence of modal interpretations. It is shown that despite its classicality, the proposed logic is empirically indistinguishable from quantum logic.