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Computational epistemology : ウィキペディア英語版
Computational epistemology
Computational epistemology is a subdiscipline of formal epistemology that studies the intrinsic complexity of inductive problems for ideal and computationally bounded agents. In short, computational epistemology is to induction what recursion theory is to deduction.
==Themes==
Some of the themes of computational epistemology include:
*the essential likeness of induction and deduction (as illustrated by systematic analogies between their respective complexity classes)
*the treatment of discovery, prediction and assessment methods as effective procedures (algorithms) as originates in algorithmic learning theory.
*the characterization of inductive inference problems as consisting of:
#a set of relevant possibilities (possible worlds), each of which specifies some potentially infinite sequence of inputs to the scientist’s method,
#a question whose potential answers partition the relevant possibilities (in the set theoretic sense),
#a convergent success criterion and
#a set of admissible methods
*the notion of logical reliability for inductive problems

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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