Abstract
Probability logic is generally understood today as a logic which assigns to propositions not just two truth-values but a whole series of such values, variously called probabilities of truth, degrees of confirmation, degrees of likelihood, etc. [1]. As distinguished from classical mathematical logic which operates with two truth-values, probability logic has to do with a range of such values, which is, in principle, unlimited. It is for this reason a branch of many-valued logic. However, while the other systems of many-valued logic have to do with a set of discrete truth-values, probability logic deals with a continuous scale of values.
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References
Such an approach to probability was foreshadowed in Leibniz’ dissertation (printed in his 23rd year), ‘On Means of Choosing a King in Poland’. Cf. [5; V].
Dice, the center of gravity of which does not coincide with the center of gravity of
a cube (e.g., loaded dice).
This final specification is obvious since from a false proposition follows any proposition, true or false.
Keynes, Nagel, and others have doubts about the possibility of an exact quantitative expression of probability 1 (degree of confirmation of a hypothesis). This is why their works deal with numerical evaluation only in a few cases. They concentrate on the comparative concept of confirmation.
The reader can learn more about L-concepts from Carnap’s Meaning and Necessity [15; 36–48].
The range of a proposition is the class of all those state-descriptions in which the given proposition holds.
The subject of the application of probability logic to inferences by analogy is taken up in Uemov’s article in the present book.
Cf. ibid.
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© 1970 D. Reidel Publishing Company, Dordrecht, Holland
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Ruzavin, G.I. (1970). Probability Logic and its Role in Scientific Research. In: Tavanec, P.V. (eds) Problems of the Logic of Scientific Knowledge. Synthese Library, vol 25. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-3393-0_6
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