Resolving Goodman’s Paradox How to Defuse Inductive Skepticism

Subjective Bayesian inference is unsuitable as an ideal for learning strategies to approximate, as the arbitrariness in prior probabilities makes claims to Bayesian learning too easily vulnerable to inductive skepticism. An objective Bayesian approach, which determines priors by maximizing information entropy, runs into insurmountable difficulties in conditions where no definite background theory is available. However, this lack of background knowledge makes the maximum entropy argument directly applicable to the process of drawing samples from a population. As a result, evidence can be seen not just as eliminating a number of incompatible hypotheses out of an infinity of possibilities, but as being representative of the true state of affairs. Hence inductive skepticism can be avoided, as demonstrated by a resolution of Goodman’s ‘grue’ paradox. This leads to a clearer understanding of the vital role abductive processes and tools like simple generalization play in learning.