Measuring the Completeness of Theories
37 Pages Posted: 16 Aug 2017 Last revised: 28 Sep 2019
Date Written: April 23, 2019
We use machine learning to provide a tractable measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness." We apply this measure to three problems: assigning certain equivalents to lotteries, initial play in games, and human generation of random sequences. We discover considerable variation in the completeness of existing models, which sheds light on whether to focus on developing better models with the same features or instead to look for new features that will improve predictions. We also illustrate how and why completeness varies with the experiments considered, which highlights the role played in choosing which experiments to run.
Keywords: Prediction, Randomness Perception, Theory Completeness
JEL Classification: C10, C52, C92, D9
Suggested Citation: Suggested Citation