Quantities are naturally viewed as functions, whose arguments may be construed as situations, events, objects, etc. We explore the question of the range of these functions: should it be construed as the real numbers (or some subset thereof)? This is Carnap's view. It has attractive features, specifically, what Carnap views as ontological economy. Or should the range of a quantity be a set of magnitudes? This may have been Helmholtz's view, and it, too, has attractive features. It reveals the close (...) connection between measurement and natural law, it makes dimensional analysis intelligible, and explains the concern of scientists and engineers with units in equations. It leaves the philosophical problem of the relation between the structure of magnitudes and the structure of the reals. What explains it? And is it always the same? We will argue that on the whole, construing the values of quantities as magnitudes has some advantages, and that (as Helmholtz seems to suggest in "Numbering and Measuring from an Epistemological Viewpoint") the relation between magnitudes and real numbers can be based on foundational similarities of structure. (shrink)

Charles Morgan has argued that nonmonotonic logic is ``impossible''. We show here that those arguments are mistaken, and that Morgan's preferred alternative, the representation of nonmonotonic reasoning by ``presuppositions'' fails to provide a framework in which nonmonotonic reasoning can be constructively criticised. We argue that an inductive logic, based on probabilistic acceptance, offers more than Morgan's approach through presuppositions.

The system presented by the author in The Logical Foundations of Statistical Inference suffered from certain technical difficulties, and from a major practical difficulty; it was hard to be sure, in discussing examples and applications, when you had got hold of the right reference class. The present paper, concerned mainly with the characterization of randomness, resolves the technical difficulties and provides a well structured framework for the choice of a reference class. The definition of randomness that leads to this framework (...) is simplified and clarified in a number of respects. It resolves certain puzzles raised by S. Spielman and W. Harper in their contributions to Profiles: Henry E. Kyburg, Jr. and Isaac Levi 1982). (shrink)

One hears increasingly from philosophers that statistical inference is a technical study that is well in control by statisticians and should be left to them; and one hears, increasingly, from mathematical statisticians that all this talk about interpretations of probability is so much philosophical frosting that is utterly irrelevant to the serious business of producing mathematical statistics. "The more interpretations of probability there are, the wider the scope of applications of our purely mathematical theories." The point of this paper is (...) to present, in detail, a situation in which an individual with given degrees of belief, given evidence, and given values, will have three different and contrary courses of action recommended to him, each according to one of the three most popular interpretations of probability. (shrink)

We examine the notion of conditionals and the role of conditionals in inductive logics and arguments. We identify three mistakes commonly made in the study of, or motivation for, non-classical logics. A nonmonotonic consequence relation based on evidential probability is formulated. With respect to this acceptance relation some rules of inference of System P are unsound, and we propose refinements that hold in our framework.

Ralph Waldo Emerson said, “a Foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines.” The alleged evidence has mounted that ordinary folk are prone to inconsistency, and particularly that they are prone to inconsistency when it comes to probabilistic judgments. I write “alleged,” because it is open to question whether the experiments that provide this evidence are well designed—in particular whether Quine’s principle of logistical charity has been followed. I also do so because (...) in some cases of probability judgments the untutored intuitions of ordinary people seem to be at least as good as, and perhaps better than, the intuitions of those who run the experiments. (shrink)

If someone comes to my house, saying, "Here is a bone; I hope Obrecht likes it," I might answer with a deductive argument: "You may rest assured on that score. Obrecht is a dog, and all dogs like bones; therefore Obrecht will like it." We may formalize this argument as follows: Let G be the bone, O be Obrecht, D be the class of dogs, B be the class of bones, and, finally, let L be the class of ordered pairs (...) such that x likes y. The premises are then: x ∈ D ⋅ y ∈ B ⋅ ⊃ ∈ L) G ∈ B O ∈ D. (shrink)

Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the book (...) will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science. (shrink)

First, a comment on a pessimistic note: Salmon says we can't be sure there is any such thing as inductive inference: in demanding that some explanations have the form of correct inductive inferences, “we may be laying down a requirement which cannot be fulfilled.” To doubt that we can fulfill that requirement is to doubt that we can formalize inductive logic. It may be true, but why begin the fight by throwing in the sponge? It is also true that there (...) are difficulties involved in formalizing acceptance rules for inductive conclusions, but these difficulties may be overcome. It is false to claim that we ‘cannot claim to know exactly’ what these acceptance rules are. Only two years ago I did just that. It was true that my claim was shown to be erroneous a year later, but that fate can befall almost any worthwhile claim. (shrink)

Bishop Butler, [Butler, 1736], said that probability was the very guide of life. But what interpretations of probability can serve this function? It isn’t hard to see that empirical (frequency) views won’t do, and many recent writers-for example John Earman, who has said that Bayesianism is “the only game in town”-have been persuaded by various dutch book arguments that only subjective probability will perform the function required. We will defend the thesis that probability construed in this way offers very little (...) guidance, dutch book arguments notwithstanding. We will sketch a way out of the impasse. (shrink)