Imre Lakatos’ idea that history of science without philosophy of science is blind may still be given a plausible interpretation today, even though his theory of the methodology of scientific research programmes has been rejected. The latter theory captures neither rationality in science nor the sense in which history must be told in a rational fashion. Nonetheless, Lakatos was right in insisting that the discipline of history consists of written rational reconstructions. In this paper, we will examine possible ways to cash out different, philosophically interesting, relationships: between rationality and science, between rationality and philosophy of science and/or epistemology, and, of course, between history and philosophy of science. Our conclusion is that the historian of science may be a philosopher of science as weIl, but if that philosophy of science is essentially a historical and dogmatic, it either cannot be used for history or it will deprive history of some of its most interesting and useful categories.

The more unknowns there are and the newer a field of research is, the less well defined are the experiments. Once a field has been sufficiently worked over so that the possible conclusions are more or less limited to existence or nonexistence, and perhaps to quantitative determination, the experiments will become increasingly better defined. But they will no longer be independent, because they are carried along by a system of earlier experiments and decisions, which is generally the situation in physics and chemistry today. Such a system will then become self-evident know-how itself. We will no longer be aware of its application and effect (Fleck 1935 (1980), p. 114, translation slightly altered from Fleck 1979, p. 86, original emphasis).

According to van Fraassen, constructive empiricism (CE) makes better sense of scientific activity than scientific realism (SR). I discuss a recent episode in biomedical research - investigations about Helicobacter Pylori and its relation to peptic ulcer. CE's expedient to cope with it is a sort of belief substitution. I argue that replacing realist beliefs by empiricist surrogates (as-if beliefs) could accommodate scientists' expectations and behavior. Nonetheless, theoretical agnosticism could hardly motivate scientists to focus just on the observational consequences derived from the theory at issue. Contrary to van Fraassen, I conclude then that, concerning scientific practice, realist beliefs cannot be considered as a gratuitious surplus which should be rejected.

In this paper I shall carefully examine some recent arguments for dualism. These arguments presuppose a strong version of physicalism that I consider inappropriate. I shall try to show that, if we reformulate the thesis of physicalism according to Kim's view of physicalism (in terms of the supervenience relation), there is a third option, a version of type physicalism, where physicalism and quaiia could be conciliated. In order to sketch this option, I shall consider the main argument against type physicalism: the explanatory gap argument, and two arguments that Kim mentions against physicalism: the inverted spectrum / zombies argument and the intrinsicality argument. I shall try to show that these three arguments depend upon a misconception of the nature of our ordinary mental concepts.

Phil Dowe has argued persuasively for a reductivist theory of causality. Drawing on Wesley Salmon's mark transmission theory and David Fair's transferencetheory, Dowe proposes to reduce causality to the exchange of conserved quantities. Dowe's account has the virtue of being simple and offering a definite "visible" idea of causation. According to Dowe and Salmon, it is also virtuous in being localist. That a theory of causation is localist means that it does not need the aid of counterfactuals and/or laws to work. Moreover, it can become the means by which we explain counterfactuals and laws. In this paper, I will argue that the theory is not localist (and hence, that it is less simple than it seems). As far as I can see, the theory needs the aid of laws.

We briefly present two areas of natural computing, vividly investigated in the recent years: DNA computing and membrane computing. Both of them have the roots in cellular biology and are rather developedat the theoretical level (new concepts, models, paradigms of computer science, with mathematical and epistemological significance have been considered in this framework), but both areas are still looking for implementations of a practical interest.

In this article we discuss what constitutes a good choice of semantic representation, compare different approaches of constructing semantic representations for fragments of natural language, and give an overview of recent methods for employing inference engines for natural language understanding tasks.

In this article we review some of the main results of descriptive complexity theory in order to make the reader familiar with the nature of the investigations in this area. We start by presenting the characterization of automata recognizable languages by monadic second-order logic. Afterwards we explain the characterization of various logics by fIxed-point logics. We assume familiarity with logic but try to keep knowledge of complexity theory to aminimum.

Which is the relation between logic and philosophy of mind? This work tries to answer that question by shortly examining, first, the place that is assigned to logic in three current views of the mind: Computationalism, Interpretativism and Naive Naturalism. Secondly, the classical debate between psychologism and antipsychologism is reviewed -the question about whether logic is or not a part of psychology- and it is indicated in which place of such debate the three mentioned conceptions of mind are located.

The goal of this paper is to survey, in a uniform and systematic way, the main results regarding networks of evolutionary processors reported so far. First, we recall the results concerning the computational power of these networks viewed as language generating devices. Then, we briefly present a few NP-complete problems and recall how they were solved in linear time by networks of evolutionary processors with linearly bounded resources (nodes, ruIes, symbols).