Clark Glymour

We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with them. Preliminary experimental results suggest that 2- to 4-year-old children spontaneously construct new causal maps and that their learning is consistent with the Bayes-Net formalism.

Magnani, Lorenzo (2001), Abduction, Reason, and Science: Processes of Discovery and Explanation. New York: Kluwer Academic/ Plenum Publishers. Magnani. Lorenzo, and Nancy Nersessian (eds.) (2002), Model-Based Reasoning: Technology, Science, Values. New York: Kluwer Academic/ Plenum Publishers. Joseph E. Earley, Sr. (ed.), Chemical Explanation: Characteristics, Development, Autonomy, Annals of the New York Academy of Sciences, vol. 988. New York Academy of Sciences (2003), 370 pp., $130.00 (cloth).

A general principle for good pedagogic strategy is this: other things equal, make the essential principles of the subject explicit rather than tacit. We think that this principle is routinely violated in conventional instruction in statistics. Even though most of the early history of probability theory has been driven by causal considerations, the terms “cause” and “causation” have practically disappeared from statistics textbooks. Statistics curricula guide students away from the concept of causality, into remembering perhaps the cliche disclaimer “correlation does not mean causation,” but rarely thinking about what correlation does mean. The treatment of causality is a serious handicap to later studies of such topics as experimental design, where often the main goal is to establish (or disprove) causation. Much of the technical vocabulary of the language used in research design textbooks consists in euphemisms for specific causal relations, e.g, “latent variable,” “intervening variable,” “confounding factor,” etc. The multiplicity of terms used to refer to causation results in confusion and, in effect, may hinder understanding of the basic principles of research design.

“It ain't nuthin' until I call it.”Bill Guthrie, UmpireNumerous criticisms of Adolf Grünbaum's account of conventions in physics have been published, and he has replied to most of them. Nonetheless, there seem to me to be good reasons for offering further criticism. In the first place Grünbaum's philosophy seems to me at least partly an extrapolation of one aspect of the views on conventions developed by Reichenbach and others. Since I think many of the issues which Reichenbach attempted to settle in his various discussions of conventions in physics are genuine and important, and I also think that those aspects of his views on which Grünbaum has focused are among the least satisfactory, it seems important to suggest questions and answers about conventions in physics which may develop more satisfactory Reichenbachian themes. Secondly, Grünbaum's philosophy centers on a distinction, that between extrinsic and intrinsic properties and relations, which has never been made satisfactorily clear either by Grünbaum or by his critics. Until it is clarified we will remain unsure of just what he is claiming and why we should think it true or think it false. Finally, Grünbaum's replies to his critics, especially his most recent reply, [9], involve unusually important claims which fail to be buttressed by the arguments he gives. I have in mind such claims as that we can learn something important about the ontological status of properties and relations by examining descriptions of them, that on the most straightforward and literal interpretation, the general theory of relativity is inconsistent, and that the foremost advocates of geometrodynamics, Clifford and Wheeler, were and are enmeshed in contradiction. My own view is that all of these claims are dubious or false, but I shall be less concerned with establishing their falsity than with discrediting the arguments offered for them.

Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the reliability of several other automated methods adapted to the same purpose. Evidence is given that the procedure can be applied to some other mineral classes as well. Since the procedure is fast with low memory requirements, it is suitable for on-board scientific analysis by orbiters or surface rovers.

Simon Kochen and Ernst Specker's well-known argument against hidden variable theories for quantum mechanics is also an argument against the possibility of quantum systems having, simultaneously, precise values for all of the dynamical quantities associated with such systems. Devices for defeating the argument were in the literature even before its publication, but recently Arthur Fine has raised a new difficulty. Fine points out that Kochen and Specker's argument requires the following principles:Sum Rule: At all times, in all states, the value of the quantity with operator A + B is the sum of the values of the quantity with operator A and the quantity with operator B, provided A, B commute.Product Rule: At all times, in all states, the value of the quantity with operator AB is the product of the values of the quantities with operators A and B, provided A, B commute.