According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Attention is drawn to two closely related functions served by scientific theory which are of fundamental importance in physical science but as yet little discussed in philosophy. As indicated by their names, they constitute the theoretical basis of physical measurements. After analysing some historically important examples and sketching the historical development of these ideas, this paper examines the similarities and differences between the estimate functions of theory and such well-known functions as prediction and explanation. The pervasiveness of the estimative functions (...) even when theory is but poorly developed is noted; and some of the problems raised by the physical equivalence of the measuring instrument to the object measured are discussed. The relations of estimation to ‘reductive logic’ are also considered. We then apply this understanding of estimative functioning to distinguishing experimental errors from those genuine anomalies which result in discovery. It is also shown that there can be no facts established nor any verification of predictions except on the basis of valid estimates derived, in turn, from antecedently accepted theories. (shrink)

Framing systematics as a field consistent with scientific inquiry entails that inferences of phylogenetic hypotheses have the goal of producing accounts of past causal events that explain differentially shared characters among organisms. Linking observations of characters to inferences occurs by way of why-questions implied by data matrices. Because of their form, why-questions require the use of common-cause theories. Such theories in phylogenetic inferences include natural selection and genetic drift. Selection or drift can explain ‘morphological’ characters but selection cannot be causally (...) applied to sequences since fitness differences cannot be directly associated with individual nucleotides or amino acids. The relation of selection to sequence data is by way of downward or top-down causation from those phenotypes upon which selection occurs. The application of phylogenetic inference to explain sequence data is thus restricted to instances where drift is the relevant theory; those nucleotides or amino acids that can be explained via downward causation are precluded from inclusion in the data matrix. The restrictions on the inclusion of sequence data in phylogenetic inferences equally apply to species hypotheses, precluding the more restrictive approach known as DNA barcoding. Not being able to discern drift and selection as relevant causal mechanisms can severely constrain the inclusion and explanations of sequence data. Implications of such exclusion are discussed in relation to the requirement of total evidence. (shrink)

Unlike explanation in science, explanation in mathematics has received relatively scant attention from philosophers. Whereas there are canonical examples of scientific explanations, there are few examples that have become widely accepted as exhibiting the distinction between mathematical proofs that explain why some mathematical theorem holds and proofs that merely prove that the theorem holds without revealing the reason why it holds. This essay offers some examples of proofs that mathematicians have considered explanatory, and it argues that these examples suggest a (...) particular account of explanation in mathematics. The essay compares its account to Steiner's and Kitcher's. Among the topics that arise are proofs that exploit symmetries, mathematical coincidences, brute-force proofs, simplicity in mathematics, merely clever proofs, and proofs that unify what other proofs treat as separate cases. (shrink)

Logic and philosophy of science share a long history, though contacts have gone through ups and downs. This paper is a brief survey of some major themes in logical studies of empirical theories, including links to computer science and current studies of rational agency. The survey has no new results: we just try to make some things into common knowledge.

Three competing ‘methods’ have been endorsed for inferring phylogenetic hypotheses: parsimony, likelihood, and Bayesianism. The latter two have been claimed superior because they take into account rates of sequence substitution. Can rates of substitution be justified on its own accord in inferences of explanatory hypotheses? Answering this question requires addressing four issues: (1) the aim of scientific inquiry, (2) the nature of why-questions, (3) explanatory hypotheses as answers to why-questions, and (4) acknowledging that neither parsimony, likelihood, nor Bayesianism are inferential (...) actions leading to explanatory hypotheses. The aim of scientific inquiry is to acquire causal understanding of effects. Observation statements of organismal characters lead to implicit or explicit why-questions. Those questions, conveyed in data matrices, assume the truth of observation statements, which is contrary to subsequently invoking substitution rates within inferences to phylogenetic hypotheses. Inferences of explanatory hypotheses are abductive in form, such that some version of an evolutionary theory(ies) is/are included or implied. If rates of sequence evolution are to be considered, it must be done prior to, rather than within abduction, which requires renaming those putatively-shared nucleotides subject to substitution rates. There are, however, no epistemic grounds for renaming characters to accommodate rates, calling into question the legitimacy of causally accounting for sequence data. (shrink)

Here we enrich Paul Weirich’s thesis holding that a simulation model can create knowledge in the form of causal explanations. We sustain the validity of exporting results from the model to the modelized world in virtue of the similarity between model and world, which is analyzable in terms of partial identity of structure, eliminating the superficial similarity that repeats empirical results by adjusting data via calibration. The structure of relations rescues from the world critical results to analyze such similarity, as (...) so certain properties which condition the kind of relations held between the elements of the model representing entities of the world. (shrink)