  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.
