Induction

Abstract

This article presents an inductive framework to increase the reliability of our judgments for situations characterized by extreme uncertainty. The proposed framework hinges on probabilistic reasoning, Occam’s razor, and Bayesian statistics. Starting from raw data – past events drawn from the actual world or a conceptual model thereof, the framework lets us: compare and evaluate competing hypotheses, justify our inductive conclusions, overcome language-dependent barriers that may limit our evaluations, and use those principles to make predictions about future uncertain events as well as justify our judgments about unprecedented but potentially catastrophic events. – AI-generated abstract.