ABSTRACT
Contract interpretation is the task of estimating what distant in time parties meant to say or would have said about a specific contingency. For at least a century, scholars and courts have been debating how to best carry out this task.
Conceiving of the interpretative task as one of prediction, I suggest that there are some valuable lessons to be drawn from a field devoted to building prediction models: machine learning. From this viewpoint, this chapter makes four contributions to the study of contract interpretation. It first defends the view of interpretation-as-prediction against the common linguistic view. The linguistic view perceives interpretation as establishing meaning in the philosophy of language sense. But as applied to contract interpretation, such arguments often employ motte-and-bailey argumentation. The second is in explaining a puzzling aspect of the debate about interpretative methods. Both textualists and contextualists insist that their method is more accurate. They can do so because they conflate two senses of the term, precision and accuracy. Third, it brings the hard problem of bias-variance tradeoff to the choice of interpretative methods. Finally, and most speculatively, the chapter distinguishes between interpretation and simulation, and argues that the latter is far more important but far less understood in legal theory. With advances in modeling techniques, the idea of simulation demands serious reconsideration.
Arbel, Yonathan A, Time and Contract Interpretation: Lessons from Machine Learning (April 26, 2024), University of Alabama Legal Studies Research Paper No 100; Research Handbook on Law and Time (forthcoming Cambridge University Press 2024, Frank Fagan and Saul Levmore eds).
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