Computational epistemology

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:

1. a set of relevant possibilities (possible worlds), each of which specifies some potentially infinite sequence of inputs to the scientist’s method,
2. a question whose potential answers partition the relevant possibilities (in the set theoretic sense),
3. a convergent success criterion and
4. a set of admissible methods

the notion of logical reliability for inductive problems

Quotations

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)

On making inductive problems easier to solve:

"Eliminating relevant possibilities, weakening the convergence criterion, coarsening the question, or augmenting the collection of potential strategies all tend to make a problem easier to solve" (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:

On the proper role of methodology:

"It is for empirical science to investigate the details of the mechanisms whereby we track, and for methodologists to devise and refine even better (inferential) mechanisms and methods" (Nozick, 1981)