Clark Glymour

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.

sities. TETRAD II discovers a class of possible causal structures of a system from a data set containing measurements of the system variables. The signi cance of learning the causal structure of a system is that it allows for predicting the e ect of interventions into the system, crucial in policy making. Our data sets contained information on 204 U.S. national universities, collected by the US News and World Report magazine for the purpose of college ranking in 1992 and 1993. One apparently robust nding of our study is that student retention is directly related to the average standardized test scores of the incoming freshmen. When test scores of incoming students are controlled for, factors such as student faculty ratio, faculty salary, and university's educational expenses per student are all independent of graduation rates, and, therefore, do not seem to directly in uence student retention. As the test scores are indicators of the overall quality of the incoming students, we predict that one of the most e ective ways of improving student retention in an individual university is increasing the university's selectivity.

Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned

One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call AE convergence, demands only that for every sentence in the theorist's language, there is a time at which the theorist settles the status of the sentence. The relative difficulties of these criteria are compared for effective and ineffective agents. We then examine in detail how the enrichment of an agent's hypothesis language makes the task of converging to the truth more difficult. In particular, we parametrize first-order languages by predicate and function symbol arity, presence or absence of identity, and quantifier prefix complexity. For nearly each choice of values of these parameters, we determine the senses in which effective and ineffective agents can converge to the complete truth on an arbitrary structure for the language. Finally, we sketch directions in which our learning theoretic setting can be generalized or made more realistic