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MCMP Philosophy of ScienceAuthor: MCMP Team
Mathematical Philosophy - the application of logical and mathematical methods in philosophy - is about to experience a tremendous boom in various areas of philosophy. At the new Munich Center for Mathematical Philosophy, which is funded mostly by the German Alexander von Humboldt Foundation, philosophical research will be carried out mathematically, that is, by means of methods that are very close to those used by the scientists. The purpose of doing philosophy in this way is not to reduce philosophy to mathematics or to natural science in any sense; rather mathematics is applied in order to derive philosophical conclusions from philosophical assumptions, just as in physics mathematical methods are used to derive physical predictions from physical laws. Nor is the idea of mathematical philosophy to dismiss any of the ancient questions of philosophy as irrelevant or senseless: although modern mathematical philosophy owes a lot to the heritage of the Vienna and Berlin Circles of Logical Empiricism, unlike the Logical Empiricists most mathematical philosophers today are driven by the same traditional questions about truth, knowledge, rationality, the nature of objects, morality, and the like, which were driving the classical philosophers, and no area of traditional philosophy is taken to be intrinsically misguided or confused anymore. It is just that some of the traditional questions of philosophy can be made much clearer and much more precise in logical-mathematical terms, for some of these questions answers can be given by means of mathematical proofs or models, and on this basis new and more concrete philosophical questions emerge. This may then lead to philosophical progress, and ultimately that is the goal of the Center. Language: en Genres: Philosophy, Society & Culture Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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Propensities, Chance Distributions, and Experimental Statistics
Episode 20
Thursday, 18 April, 2019
Mauricio Suarez (London, Madrid) gives a talk at the MCMP Colloquium (12 November, 2014) titled "Propensities, Chance Distributions, and Experimental Statistics". Abstract: Probabilistic or statistical modelling may be described as the attempt to characterise (finite) experimental data in terms of models formally involving probabilities. I argue that a coherent understanding of much of the practice of probabilistic modelling calls for a distinction between three notions that are often conflated in the philosophy of probability literature. A probability model is often implicitly or explicitly embedded in a theoretical framework that provides explanatory – not merely descriptive – strategies and heuristics. Such frameworks often appeal to genuine properties of objects, systems or configurations, with putatively some explanatory function. The literature provides examples of formally precise rules for introducing such properties at the individual or token level in the description of statistically relevant populations (Dawid 2007, and forthcoming). Thus, I claim, it becomes useful to distinguish probabilistic dispositions (or single-case propensities), chance distributions (or probabilities), and experimental statistics (or frequencies). I illustrate the distinction with some elementary examples of games of chance, and go on to claim that it is readily applicable to more complex probabilistic phenomena, notably quantum phenomena.
