Computational epistemology is a specialized subdiscipline of formal epistemology that investigates the inherent complexity of inductive reasoning, drawing an analogy to how recursion theory relates to deduction. It views scientific discovery, prediction, and assessment as effective procedures or algorithms, focusing on how both ideal and computationally limited agents can reliably solve inductive problems. Central to its approach is the characterization of inductive inference problems using "possible worlds," specific questions, and convergent success criteria, emphasizing the notion of logical reliability.

This field notably distinguishes itself from probability-centric methods like Bayesian confirmation theory, choosing instead to explain scientific features through "complexity and success," a perspective articulated by Kelly in 2000a. Rooted in algorithmic learning theory, computational epistemology assesses whether an inductive method can consistently succeed across all "epistemically possible worlds." As defined by Rugai (2013), it's an interdisciplinary field concerning the relationships and constraints among reality, data, information, knowledge, and wisdom.