Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 27, that better purposive interpretation could lower barriers to complex transactions and scale relational contracting. Parties now spend heavily on deliberation, careful language, and lawyers to produce a good contract, even though nominal formation is cheap. This is significant because reliable reconstruction may expand who can coordinate through nuanced agreements. It connects to access to contracting, relational contracts, transaction costs, legal drafting, contract innovation, scale.

better purposive interpretation could lower barriers to complex transactions and scale relational contracting. This matters because reliable reconstruction may expand who can coordinate through nuanced agreements.

access to contracting
relational contracts
transaction costs
legal drafting
contract innovation
scale

Cheaper interpretation cannot replace advice, bargaining fairness, or substantive regulation.

Page 27 connects improved simulation to transaction cost and social organization.


Yonathan A. Arbel, Time and Contract Interpretation: Lessons from Machine Learning, in Research Handbook on Law and Time 109, 109–130 (Frank Fagan & Saul Levmore eds., 2025).

working-paper PDF of published book chapter

Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 28, that contract interpretation is prediction across time and should be evaluated like other predictive enterprises. The comparison invites systematic attention to inputs, targets, model complexity, and error. This is significant because legal interpretation need not remain insulated from general knowledge about prediction. It connects to temporal prediction, model evaluation, contract interpretation, machine learning, legal methodology, interdisciplinary research.

contract interpretation is prediction across time and should be evaluated like other predictive enterprises. This matters because legal interpretation need not remain insulated from general knowledge about prediction.

temporal prediction
model evaluation
contract interpretation
machine learning
legal methodology
interdisciplinary research

Legal prediction remains constrained by distinct normative and sociological considerations.

Page 28 restates the chapter's core thesis.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 28, that legal interpretation has been strikingly unempirical about the performance of its techniques. Courts and scholars debate methods without routinely measuring how accurately they recover intent or how consistently they operate. This is significant because doctrinal confidence should be tested against evidence rather than sustained only by theory and intuition. It connects to empirical legal studies, interpretive validation, textualism, contextualism, legal methodology, measurement.

legal interpretation has been strikingly unempirical about the performance of its techniques. This matters because doctrinal confidence should be tested against evidence rather than sustained only by theory and intuition.

empirical legal studies
interpretive validation
textualism
contextualism
legal methodology
measurement

Ground truth is difficult, and empirical designs must avoid substituting convenient proxies for intent.

Page 28 identifies the evaluation gap in the field.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 28, that precision, accuracy, bias, and variance give legal scholars a vocabulary for asking answerable questions about interpretation. The chapter closes by asking how accurate textualism is, how precise contextualism is, and what bias each creates. This is significant because conceptual clarity is a precondition for credible empirical comparison and institutional reform. It connects to research agenda, precision, accuracy, bias, variance, contract interpretation.

precision, accuracy, bias, and variance give legal scholars a vocabulary for asking answerable questions about interpretation. This matters because conceptual clarity is a precondition for credible empirical comparison and institutional reform.

research agenda
precision
accuracy
bias
variance
contract interpretation

The vocabulary narrows questions but does not itself provide the empirical answers.

Page 28 ends with a research agenda for validating interpretive techniques.