In Kvart (1991a), I discussed the analysis of causal relevance presented in A Theory of Counterfactuals (1986) (and first in 1975). I explained there in what respect the notion captured by the analysis of Kvart (1986) is a mere approximation to the requisite notion of causal relevance. In this paper I present another analysis of causal relevance, devoid of the shortcoming of its predecessor.

The present analysis of causal relevance is, again, grounded in a chancelike notion of objective probability. The correlative notion of causal independence is analyzed as holding in case there is a so-called causal impact series, which is defined here. The basic notions used for this analysis are the notion of a differentiator, which upsets an equiprobability case, and that of a blocker, which restores it.

In addition to its obvious successes within the kinetic theory the ideal gas law and the modeling assumptions associated with it have been used to treat phenomena in domains as diverse as economics and biology. One reason for this is that it is useful to model these systems using aggregates and statistical relationships. The issue I deal with here is the way R. A. Fisher used the model of an ideal gas as a methodological device for examining the causal role of selection in producing variation in Mendelian populations. The model enabled him to create the kind of population where one could measure the effects of selection in a way that could not be done empirically. Consequently we are able to see how the model of an ideal gas was transformed into a biological model that functioned as an instrument for both investigating nature and developing a new theory of genetics.

Probability theory is important not least because of its relevance for decision making, which also means: its relevance for the single case. The frequency theory of probability on its own is irrelevant in the single case. However, Howson and Urbach argue that Bayesianism can solve the frequentist's problem: frequentist-probability information is relevant to Bayesians (although to nobody else). The present paper shows that Howson and Urbach's solution cannot work, and indeed that no Bayesian solution can work. There is no way to make frequentist probability relevant.

I am grateful for the opportunity to share with you some of my convictions, anxieties, and hopes. My central conviction is the immense value of a mental outlook which can be called by the suggestive and historically recognized name Enlightenment, whose exact characterization, however, requires careful discussion. It is an outlook widely shared, though with innumerable personal variations, by the members of the Philosophy of Science Association, by the broader community of scientists, science teachers, and philosophers, and by an ill-defined part of the general public. My anxieties are that the cultivation and the influence of this mental outlook in our nation and in the world are threatened in various ways, among them ideological attacks, slackening of educational standards, and demoralization of its adherents. My central hope is that Enlightenment will prevail despite these threats.

In this paper, we show that a restricted form of time travel both accords with special relativity kinematics and avoids several prima facie objections. We argue that such time travel provides a reasonable way to interpret certain phenomena which can readily be described, and the analogues of which have already been observed at the level of elementary particle reactions. We then describe how a time-traveling object could measure itself, and demonstrate how, in the appropriate circumstances, such an experiment could convince a theorist who insisted on a single criterion for length measurements that his standard had itself changed in length. The same experiment can shed light on the possibility of detecting a universal expansion; and we show that, given certain experimental results, one must conclude that every member of a class of time travelers has changed in length simultaneously, though perhaps only while going backwards in time.

Hacking argues against van Fraassen's constructive empiricism by appeal to features of microscopic imaging. Hacking relies on both our practices involving imaging instruments and the structure of the images produced by these micropractices. Van Fraassen's reply is formally correct yet fundamentally unsatisfying. I aim to strengthen van Fraassen's reply, but must then extend constructive empiricism, specifically the central notion of “theoretical immersion.” I argue that immersion is more analogous to entering a virtual reality than to learning a language. This metaphor assimilates instrument-based practice as well as theoretical debate and explanation, and can provide an anti-realist view of our micro-practices consonant with constructive empiricism.

The likelihood principle of Bayesian statistics implies that information about the stopping rule used to collect evidence does not enter into the statistical analysis. This consequence confers an apparent advantage on Bayesian statistics over frequentist statistics. In the present paper, I argue that information about the stopping rule is nevertheless of value for an assessment of the reliability of the experiment, which is a pre-experimental measure of how well a contemplated procedure is expected to discriminate between hypotheses. I show that, when reliability assessments enter into inquiries, some stopping rules prescribing optional stopping are unacceptable to both Bayesians and frequentists.

A philosophically important but largely overlooked cognitive theory is examined, one that provides information on which inferences an agent will make from his beliefs. Such a theory of feasible inferences is indispensable in a complete cognitive psychology, in particular, for predicting the agent's actions on the basis of rationality conditions and attributed beliefs and desires. However, very little of the feasibility theory which applies to a typical human being can be shown a priori to apply to all agents. The logical competence required of a rational agent seems to have a cluster structure: it cannot be the case that an agent is able to make no inferences, but an agent can be unable to make any particular one.

In a recent paper, Michael Levin argues that both statements reporting causal relations and causal explanatory statements are extensional. We show that his argument for the extensionality of causal explanatory statements fails to establish that conclusion. His claim that certain ‘because’ statements are elliptical for statements of what he terms the ‘causal explanatory’ form is unsubstantiated. The argument for the referential transparency of the allegedly explanatory form, regardless of whether it is a distinct explanatory form, fails because of scope problems. Finally, we argue that the apparent referential opacity of explanations, the attribution of certain kinds of properties to explanations, and our satisfaction with explanations in what appear to be disparate forms are best accounted for, not by the assumption of ellipticality of statements in various forms for statements in some inherently explanatory form, but rather by an account of explanation as a speech act, the success or failure of which may be affected by which of several co-referring expressions is employed.

In the quest to discover the neural bases of cognition, rigorous behavioral tools are equally as important as sophisticated tools for neural intervention. This paper evaluates several episodes in the development of a novel behavioral tool for rodent cognitive testing, the rodent touchscreen operant chamber. Using conceptual tools on offer in the philosophical literature on exploratory experimentation and control, I illuminate how optimization of this behavioral tool and an understanding of the causal knowledge it may be used to generate historically has involved dynamic interplay between community-driven exploratory practices operating in parallel with causal hypothesis-driven research.

This discussion adjudicates a dispute between Larry Laudan and Gerald Doppelt over the nature of methodological rules. Laudan holds that all methodological rules are hypothetical imperatives, while Doppelt argues that a subset of those rules, basic methodological standards, are not hypothetical imperatives. I argue that neither writer offers a satisfactory account of methodological rules and that their reliance on the hypothetical/nonhypothetical distinction does not advance our understanding of methodological rules. I propose that we dispense with this dubious distinction and develop an alternative account of scientific norms.

Gott (1993) has used the ‘Copernican principle’ to derive a probability distribution for the total longevity of any phenomenon, based solely on the phenomenon's past longevity. Leslie (1996) and others have used an apparently similar probabilistic argument, the ‘Doomsday Argument’, to claim that conventional predictions of longevity must be adjusted, based on Bayes's Theorem, in favor of shorter longevities. Here I show that Gott's arguments are flawed and contradictory, but that one of his conclusions is plausible and mathematically equivalent to Laplace's famous—and notorious—‘rule of succession’. On the other hand, the Doomsday Argument, though it appears consistent with some common-sense grains of truth, is fallacious; the argument's key error is to conflate future longevity and total longevity. Applying the work of Hill (1968) and Coolen (1998, 2006) in the field of nonparametric predictive inference, I propose an alternative argument for quantifying how past longevity of a phenomenon does provide evidence for future longevity. In so doing, I identify an objective standard by which to choose among counting time intervals, counting population, or counting any other measure of past longevity in predicting future longevity.

There is a famous character in one of Oscar Wilde's plays who knew the price of everything and the value of nothing. An economist wonders uneasily if the reference is not to him. The word “value” occurs in economic writings with high frequency, the frequency of meanings being almost as great as the frequency of occurrence. It has been the occasions of long and bitter disputes, some on the semantic level, some more substantive. What I want to accomplish in this paper is a relatively humble task—to examine some of the concepts and related systems in economics which seem to be relevant to the general problem of value, and to see how far and in what directions they might be safely generalized. I do not claim that economics, even when generalized to the full, gives us a complete theory of value. Many of the most important and most difficult questions in value theory cannot be answered within the framework which I shall set up. But economics can, I hope, provide something of a foundation on which to build, or at least a launching platform from which to send off our philosophical rockets.

In this article, I explain how a variant of David Miller's argument concerning the language dependence of the accuracy of predictions can be applied to Joyce's notion of the accuracy of “estimates of numerical truth-values” (i.e., Joycean credences). This leads to a potential problem for Joyce's accuracy-dominance-based argument for the conclusion that credences (understood as “estimates of numerical truth-values” in Joyce's sense) should obey the probability calculus.

It is proposed in this paper to develop a method by which the most general problem of the algebra of propositions is solved. This problem is to construct all propositions whose truth is independent of the form of the variables. As might be expected this method will enable us to determine without the use of matrices the consistency and independence of propositions, except in the case of those fundamental properties, which taken together define consistency itself. In the discussion which follows no limitations are placed on the meaning of the terms and the word proposition is used simply in the sense of a variable. If we were to say, for example, that our variables must be restricted to zero or one values, we would be introducing a limitation which would reduce the generality of our science. A logic may be false in the sense that its postulates lead to contradictory theorems, or it may be false in the sense that it lacks generality, and the situation is not saved by calling it a two-valued, or a three-valued logic, or a logic of “propositions,” or by designating it by some other euphuism.

Explanations in physics commonly appeal to data drawn from different length or time scales, as when a “top-down” macroscopic constraint such as rigidity is used to evade the complexities one would confront in attempting to model the situation in a purely “bottom-up” fashion. Such techniques commonly embody rather complex shifts in explanatory strategy.

The suggestion that cognition is holistic has become a prominent criticism of optimism about the prospects for cognitive science. This paper argues that the standard motivation for this holism, that of epistemological holism, does not justify this pessimism. An illustration is given of how the effects of epistemological holism on perception are compatible with the view that perceptual processes are highly modular. A suggestion for generalizing this idea to conceptual cognitive processing is made, and an account of the holists' failure is offered.

What does it mean to say that a physical system computes or, specifically, to say that the nervous system computes? One answer, endorsed here, is that computing is a sort of modeling. I trace this line of answer in the conceptual and philosophical work conducted over the last 3 decades by researchers associated with the University of California, San Diego (UCSD). The linkage between their work and the modeling notion is no coincidence: the modeling notion aims to account for the computational approach in neuroscience, and UCSD has been home to central studies in neurophilosophy, connectionism, and computational neuroscience.