![]() The basic axioms or formal conditions of decision theory, especially the ordering condition put on preferences and the axioms underlying the expected utility (EU) formula, are subject to a number of counterexamples, some of which can be endowed with normative value and thus fall within the ambit of a philosophical reflection on practical rationality. So as Jaynes suggests “a good deal of leading by the hand is necessary here, in order to develop a mastery of method.” Its normative outlook contributes a great deal to practical reasoning. ![]() I see this as an epistemological recidivism, - a travesty of an ideal on the bases of inadequacies. However, in spite of the formal simplicity of these rules, the major critique leveled against it is that it lacks practical relevance (for a non-ideal agent in a non-ideal situation), and so worthless, if people actually fail to attain its ideals. Finally, the essence of any decision rule is that the decision must be made on the basis of observed data, (and all relevant prior knowledge) which contains all the information sufficient to arriving at the decision. The decision rule L(D, I) = −log curtails information loss, conditions our choice as it is suggestive of decision Di when the specific information I is present. This resultant apparatus leads to the idea of assigning distributions to represent complete ignorance, and to accommodate new information showing the most honest description of what we actually know without assuming any information. Thus achieving a logical unity, simplicity and flexibility in application of Ma圎nt decision model. This is somewhat instrumental in resolving the imaginary distinction between Probability Theory and Statistical Theory. How then can one deal with this epistemological inadequacy with its nuances? Jaynes’ Objective-Bayesianism expressed in the Ma圎nt principle serves as the foundation of Probability theory for modern science. Since most Decision problems hinges on uncertainty crisis in enumerating the range of possibilities. Our mathematical probability model however different, is a powerful analytical tool that guarantees detection of inconsistencies or violations of our other desiderata, derivable from its axioms. But all mathematical formulation attempts to put forward a consistent and complete representation of the real world, often fails to satisfy Kurt Gödel’s incompleteness theorem. Within this framework the use of Logic and Set Theories, was key to understanding the foundations of mathematics. At Jaynes’ time of writing, the speculative sciences were beginning to assume a mathematical bend vis-a-vis the representation of the process of reasoning quantitatively. This work pries into the analytic and systematic approach to the study of decision making in the light of Edwin Jaynes’ Probability Logic.
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