Daniel Dennett distinguishes real patterns from bogus patterns by appeal to compressibility. As information theorists have shown, data are compressible if and only if those data exhibit a pattern. Noting that high-level models are much simpler than their low-level counterparts, Dennett interprets high-level models as compressed representations of the fine-grained behavior of their target system. As such, he argues that high-level models depend on patterns in this behavior. Unfortunately, data scientific practice complicates Dennett’s interpretation, undermining the traditional justification for real patterns and suggesting a revised research program for its defenders.

Computationalism—the view that cognition is computation—has always been controversial. It faces two types of objection. According to insufficiency objections, computation is insufficient for some cognitive phenomenon X. According to objections from neural realization, cognitive processes are realized by neural processes, but neural processes have feature Y, and having Y is incompatible with being (or realizing) computations. In this article, I explain why computationalism has survived these objections. To adjudicate the dispute between computationalism and its foes, I will conclude that we need a better account of computation.

Recently Halpern and Pearl and Hitchcock have presented influential accounts of actual (token) causation using Bayesian networks. These accounts have been deterministic. Here we present a probabilistic extension to these active path analyses of actual causation. The extension uses “soft” interventions to set distributions rather than just single values. The resulting account can handle at least as wide a range of examples as the original accounts, without assuming determinism.

One thing which has struck me most as I have read the articles of the Encyclopedia of Unified Science is the complexity that can be discerned in many of the operations which for the purpose of the article are treated as elementary. It is apparent that Unity of Science, like every other discipline, has its own stock of “atoms of discourse”, suited to its own purposes. Experience in physics would prepare one to expect that for certain purposes it may be profitable to attempt to analyze these atoms further. The atom of discourse, or presupposition commonly made by most adherents of the Unity of Science movement, with which I shall be chiefly concerned, is with regard to the nature of “science“. My present concern with this matter has arisen from my extreme difficulty in communicating my meaning to other people. The difficulty has been a genuine puzzle to me, until quite recently a discussion with one of my colleagues disclosed a difference of attitude on fundamental matters so revelatory that I am encouraged to return again to the attack.

Rédei and Gyenis suggest that Lewis’s Principal Principle is meaningful only if it satisfies certain consistency conditions: starting from any assignment of credences to some algebra of events, we must always be able to extend our algebra with events as “the value of the objective chance of event E is p” and assign credences to such events in a consistent manner. I show that this extension is possible. However, I also argue that this requirement is unnecessary: the Principal Principle concerns subjective beliefs about objective chance; hence, events concerning those probabilities are meant to be in the algebra initially.

Neuroethology is a branch of biology that studies the neural basis of naturally occurring animal behavior. This science, particularly a recent program called computational neuroethology, has a similar structure to the interdisciplinary endeavor of cognitive science. I argue that it would be fruitful to conceive of cognitive science as the computational neuroethology of humans. However, there are important differences between the two sciences, including the fact that neuroethology is much more comparative in its perspective. Neuroethology is a biological science and as such, evolution is a central notion. Its target organisms are studied in the context of their evolutionary history. The central goal of this paper is to argue that cognitive science can and ought to be more comparative in its approach to cognitive phenomena in humans. I show how the domain of cognitive phenomena can be divided up into four different classes, individuated by the relative phylogenetic uniqueness of the behavior. I then describe how comparative evidence can enrich our understanding in each of these different arenas.

Professor Savage has been candid and generous in stating his interest in philosophy, and the philosophers who have heard him are surely grateful for this. His attitude is very far from that of some competent scientists and mathematicans who purport to clear up the questions which philosophers raise concerning their disciplines by means of a battery of technical results of varying relevance—a procedure which can often be appropriately described as “an abominable snow-job.” However, Professor Savage's generosity places a responsibility on philosophers, since the questions he raises are difficult.

In Patterns of Discovery, [1], and Concept of the Positron, [2], the late N. R. Hanson put forward an intersting and, I believe, essentially sound argument to the effect that, necessarily, micro-entities are “unpicturable.” Hanson's claim is centrally a claim about microreduction, but his use of the term ‘unpicturable’ may be misleading, generating critiques which overplay its implications and its importance. A. M. Paul, in a recent article, [4], has taken Hanson to task in this regard, claiming that the notion of picturability is so cloudy as to render Hanson's whole enterprise unconvincing. In what follows I shall critically discuss Hanson's argument and Paul's analysis and attempt to state a version of it capable of meeting Paul's objections. I will claim that Hanson's use of the term ‘unpicturable’ may be, at worst, infelicitous, and that his thesis concerning micro- or property-reduction remains unaffected by questions concerning what we can or cannot picture.

Though one believes that P is true, one can have reasons for thinking it false. Yet, it seems that one cannot know that P is true and (still) have reasons for thinking it false. Why is this so ? What feature of knowledge (or of reasons) precludes having reasons or evidence to believe (true) what you know to be false? If the connection between reasons (evidence) and what one believes is expressible as a probability relation, it would seem that the only satisfactory explanation of this fact is that when one knows that P is true, the reasons or evidence one has in support of P are such as to confer upon P the probability of 1. It is shown by an application of Bayes' Theorem that any value smaller than 1 would permit having reasons to believe what one knows to be false. Hence, it would seem that knowledge requires conclusive reasons to believe (if reasons or evidence is required at all).

Current theories of addiction try to explain what addiction is, who experiences it, why it occurs, and how it develops and persists. In this article, I explain why none of these theories can be accepted as a comprehensive model. I argue that current models fail to account for differences in embodiment, interaction processes, and the experience of addiction. To redress these limiting factors, I design a proposal for an enactive account of addiction that follows the enactive model of autism proposed by Hanne De Jaegher.

I present a response to Vollmer's review of the book Of Minds and Molecules, and especially her comments on my own article therein. This provides an opportunity to discuss two central ideas in the philosophy of chemistry. These are the distinction between elements as simple substances (element-1) and elements as basic substances (element-2) and Paneth's proposed intermediate position for philosophy of chemistry. The response also discusses the question of isotopes in relationship to the nature of the elements and their classification as well as the philosophical status of atomic orbitals.

The canonical Bayesian solution to the ravens paradox faces a problem: it entails that black nonravens disconfirm the hypothesis that all ravens are black. I provide a new solution that avoids this problem. On my solution, black ravens confirm that all ravens are black, while nonblack nonravens and black nonravens are neutral. My approach is grounded in certain relations of epistemic dependence, which, in turn, are grounded in the fact that the kind raven is more natural than the kind black. The solution applies to any generalization “All F’s are G” in which F is more natural than G.

Among philosophers of science nearly a century ago the dominant attitude was that (in Rudolph Carnap's words) philosophy of science was “like science itself, neutral with respect to practical aims, whether they are moral aims for the individual, or political aims for a society.” The dominant attitude today is not much different: our aim is still to articulate scientific rationality, and our understanding of that rationality still excludes the moral and political. I contrast this with the growing entanglements within the sciences of the ethical and the epistemic, and I suggest ways in which philosophers of science can usefully respond.

Professor Leonard proposes that imperative and interrogative sentences be classified, together with declarative ones, as true and false. The interesting analysis he gives in connection with this proposal points out that these three types of utterance have something in common and has the merit of evincing the identity of this common element. Also it may seem to offer attractive possibilities of integrating various types of discourse in its promise of partial assimilation of interrogatives and imperatives to the model of truth-valuable language. Even so, Leonard's account is not altogether satisfactory. I shall not discuss his treatment of imperatives but shall argue that his conception of interrogatives has unacceptable consequences.

The value-ladenness of computer algorithms is typically framed around issues of epistemic risk. In this article, I examine a deeper sense of value-ladenness: algorithmic methods are not only themselves value-laden but also introduce value into how we reason about their domain of application. I call this domain distortion. In particular, using insights from jurisprudence, I show that the use of recidivism risk assessment algorithms (1) presupposes legal formalism and (2) blurs the distinction between liability assessment and sentencing, which distorts how the domain of criminal punishment is conceived and provides a distinctive avenue for values to enter the legal process.