Nomic Probability and the Foundations of Induction by John L. Pollock
By John L. Pollock
During this booklet Pollock bargains with the topic of probabilistic reasoning, making normal philosophical experience of target percentages and exploring their courting to the matter of induction. He argues that likelihood is prime not just to actual technological know-how, yet to induction, epistemology, the philosophy of technology and masses of the reasoning correct to man made intelligence. Pollock's major declare is that the basic concept of likelihood is nomic--that is, it comprises the idea of traditional legislations, legitimate throughout attainable worlds. many of the epistemic and statistical conceptions of chance, he demonstrates, are derived from this nomic inspiration. He is going directly to offer a thought of statistical induction, an account of computational rules permitting a few percentages to be derived from others, an account of recognition ideas, and a concept of direct inference.
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Extra resources for Nomic Probability and the Foundations of Induction
19 However, this whole approach seems misguided. Invariance over changes in place selections does not seem to have anything to do with the relationship between prob(A/B) and the behavior of sequences of B's. Suppose, for example, that it is a physical Jaw that B's occur sequentially, and every other B is an A. An example might be the temporal sequence of days and nights. 20 Reichenbach  tried to avoid this diffICulty by attaching probabilities directly to ordered sets rather than unordered sets, but such a ploy seems desperate.
Or more simply. 46 I have defended this view of conceptual analysis at 1enb>th over the last twenty years. and I now propose to ap~Jy it to nomic generalizations and to the analysis of probability. 1 To illustrate my general point. consider the problem of perception. That is the problem of explaining how it is possible to acquire knowledge of the physical world on the basis of perception. twentieth-century philosophers tried to solve this problem by defining physical object concepts like red in terms of concepts describing our perceptual states (like the concept of looking red).
Practical decisions like this are often based upon rough estimates of probability. The important thing to nOle is that the probabilities required for decision theory are definite probabilities. For example, it does little good to know that the probability of its raining under general circumstances of type C is 28 The distinction between definite and indefinite probabilities is a fundamental one and, 1 should think, an obvious one. It is amazing then how often philOSOphers have ovcrlooked it and IJcoome thoroughly muddled as a resulL file Cltamp1e.