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.
Since Ramsey, much discussion of the relation between probability and belief has taken for granted that there are degrees of belief, i.e., that there is a real-valued function, B, that characterizes the degree of belief that an agent has in each statement of his language. It is then supposed that B is a probability. It is then often supposed that as the agent accumulates evidence, this function should be updated by conditioning: BE(·) should be B(·E)/B(E). Probability is also important in (...) classical statistics, where it is generally supposed that probabilities are frequencies, and that inference proceeds by controlling error and not by conditioning. I will focus on the tension between these two approaches to probability, and in the main part of the paper show where and when Bayesian conditioning conflicts with error based statistics and how to resolve these conflicts. (shrink)
The dominant argument for the introduction ofpropensities or chances as an interpretation of probabilitydepends on the difficulty of accounting for single caseprobabilities. We argue that in almost all cases, the``single case'' application of probability can be accountedfor otherwise. ``Propensities'' are needed only intheoretical contexts, and even there applications ofprobability need only depend on propensities indirectly.
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.
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)
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 rapprochement between methodology and statistics suggested by Chow's book is a much needed one. His examples suggest that the situation is even worse in psychology than in some other disciplines. It is suggested that both historical accuracy and attention to recent work on the foundations of statistics would be beneficial in achieving the goals that Chow seeks.
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)
The evidence of your own eyes has often been regarded as unproblematic. But we know that people make mistaken observations. This can be looked on as unimportant if there issome class of statements that can serve as evidence for others, or if every statement in our corpus of knowledge is allowed to be no more than probable. Neither of these alternatives is plausible when it comes to machine or robotic observation. Then we must take the possibility of error seriously, and (...) we must be prepared to deal with error quantitatively. The problem of using internal evidence to arrive at error distributions is the main focus of the paper. (shrink)
The Bayesian view of inference has become popular in philosophy in recent years. Scientific Reasoning: a Bayesian Approach, by Colin Howson and Peter Urbach, represents an articulate and persuasive defense of the Bayesian view. We focus on the theme of that book, and argue that there are difficulties with Bayesianism, and alternatives worth considering. One of the most serious drawbacks to Bayesianism is the subjectivity that pervades most versions of it. We argue that this is an instance of a more (...) general contemporary tendency to move away from claims of objectivity, and toward frankly subjective views. This results from a desire to find a deductive, incorrigible, basis for scientific inference. We claim that such a desire is doomed to frustration, but that does not spell the end of efforts to formalize inductive reasoning. (shrink)
There are a number of reasons for being interested in uncertainty, and there are also a number of uncertainty formalisms. These formalisms are not unrelated. It is argued that they can all be reflected as special cases of the approach of taking probabilities to be determined by sets of probability functions defined on an algebra of statements. Thus, interval probabilities should be construed as maximum and minimum probabilities within a set of distributions, Glenn Shafer's belief functions should be construed as (...) lower probabilities, etc. Updating probabilities introduces new considerations, and it is shown that the representation of belief as a set of probabilities conflicts in this regard with the updating procedures advocated by Shafer. The attempt to make subjectivistic probability plausible as a doctrine of rational belief by making it more flowery — i.e., by adding new dimensions — does not succeed. But, if one is going to represent beliefs by sets of distributions, those sets of distributions might as well be based in statistical knowledge, as they are in epistemological or evidential probability. (shrink)
One of the serious motivations for the development of non-monotonic logics is the fact that, however sure we may be of some set of facts, there can come a time at which at least some of them must be given up. A number of philosophical approaches have stemmed from the study of scientific inference, in which a law or theory, accepted on good evidence at one time, comes to be rejected on the basis of more evidence. These approaches are reviewed, (...) and an alternative approach, whose key idea is the control of observational error for the purpose of predictive adequacy is developed. (shrink)
In this work Henry Kyburg presents his views on a wide range of philosophical problems associated with the study and practice of science and mathematics. The main structure of the book consists of a presentation of Kyburg's notions of epistemic probability and its use in the scientific enterprise i.e., the effort to modify previously adopted beliefs in the light of experience. Intended for cognitive scientists and people in artificial intelligence as well as for technically oriented philosophers, the book also provides (...) a general overview of the philosophy of science for the non-philosopher by one of the leading authorities in the field. (shrink)
Quantitative laws are more typical of science than are generalizations involving observational predicates, yet much discussion of scientific inference takes the confirmation of a universal generalization by its instances to be typical and paradigmatic. The important difference is that measurement necessarily involves error. It is argued that because of error laws can no more be refuted by observation than they can be verified by observation. Without much background knowledge, tests of a law mainly provide evidence for the distribution of errors (...) of measurement of the quantities involved. With more background knowledge, the data may contribute either to our knowledge of the error distributions, or to the grounds we have for accepting or rejecting the law. With enough background knowledge, data may verify as well as refute laws. (shrink)
Measurement is fundamental to all the sciences, the behavioural and social as well as the physical and in the latter its results provide our paradigms of 'objective fact'. But the basis and justification of measurement is not well understood and is often simply taken for granted. Henry Kyburg Jr proposes here an original, carefully worked out theory of the foundations of measurement, to show how quantities can be defined, why certain mathematical structures are appropriate to them and what meaning attaches (...) to the results generated. Crucial to his approach is the notion of error - it can not be eliminated entirely from its introduction and control, her argues, arises the very possibility of measurement. Professor Kyburg's approach emphasises the empirical process of making measurements. In developing it he discusses vital questions concerning the general connection between a scientific theory and the results which support it (or fail to). (shrink)
The system presented by the author in The Logical Foundations of Statistical Inference (Kyburg 1974) 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 (R. Bogdan (ed.) 1982). (shrink)
