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 inducewhat recursion theory is to deduction .
Some of the themes of computational epistemology include:
- the essential likeness of induction and deduction (as illustrated by systematic analogies entre Their respective complexity classes )
- the treatment of discovery, prediction and assessment methods have effective procedures ( algorithms ) as originates in algorithmic learning theory .
- the characterization of inductive inference
- a set of possible possibilities , each of which specifies some possible infinite sequence of inputs to the scientist’s method,
- Whose issue has potential answers partition the relevant possibilities (in the set theoretic sense)
- a convergent success criterion and
- a set of acceptable methods
- the notion of logical reliability for inductive problems
Computational epistemology definition:
- “Computational epistemology is an interdisciplinary field that concerns itself with the relationships and constraints between reality, measure, data, information, knowledge, and wisdom” (Rugai, 2013)
Inductive problems easier to solve:
- “Eliminating relevant possibilities, weakening the convergence criterion, coarsening the question, or increasing the collection of potential strategies” (Kelly, 2000a)
On the divergence of computational epistemology from Bayesian confirmation theory and the like:
- “Whenever you are inclined to explain a feature of science in terms of probability and confirmation, take a moment to see how the issue would look in terms of complexity and success” (Kelly, 2000a)
Computational epistemology in a nutshell:
- Formal learning theory is very simple in outline. An inductive problem specifies a range of epistemically possible worlds where it is possible to correct the situation, where both content and truth (or some analogous virtue like empirical adequacy) may be correct. Each possible world produces an input stream that inductively processes sequentially, generating its own output stream, which may terminate or go on forever. A notion of success specifies how the method should converge to a correct output in each possible world. A method solves the problem (in a given sense) just in casethe method succeeds in each of the possible worlds specified by the problem. We say that such a method is reliable since it succeeds all the epistemically possible worlds. Of two non-solutions, one is as reliable as the other just in case it succeeds in all the worlds the other one succeeds in. That’s all there is to it! (Kelly et al 1997)
On the proper role of methodology
- “It is for empirical science to investigate the details of the mechanisms, and for methodologies to motto and refine even better (inferential) mechanisms and methods” (Nozick, 1981)
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- Kelly, Kevin (2002). “Efficient Convergence Implies Ockham’s Razor”, Proceedings of the 2002 International Workshop on Computational Models of Scientific Reasoning and Applications, Las Vegas, USA, June 24-27, 2002.
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