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 1, that contract interpretation estimates what parties separated from adjudicators by time meant to say or would have said about a contingency. The interpretive dispute is therefore a problem of inference about past intentions under later conditions. This is significant because the temporal distance between formation and dispute makes prediction central to interpretation. It connects to contract interpretation, party intent, time, prediction, contingencies, judicial inference.

contract interpretation estimates what parties separated from adjudicators by time meant to say or would have said about a contingency. This matters because the temporal distance between formation and dispute makes prediction central to interpretation.

contract interpretation
party intent
time
prediction
contingencies
judicial inference

The formulation centers reconstruction and does not deny that judges sometimes engage in construction or public-policy review.

The abstract defines the interpretive task in predictive and temporal terms.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 1, that machine learning offers useful conceptual tools because it is a field organized around building and evaluating prediction models. The chapter borrows modeling ideas without claiming that legal judgment and statistical learning are identical. This is significant because legal theory can gain clarity from disciplines that explicitly measure predictive performance. It connects to machine learning, predictive modeling, interdisciplinary method, legal theory, model evaluation, contract law.

machine learning offers useful conceptual tools because it is a field organized around building and evaluating prediction models. This matters because legal theory can gain clarity from disciplines that explicitly measure predictive performance.

machine learning
predictive modeling
interdisciplinary method
legal theory
model evaluation
contract law

The analogy is methodological and remains subject to legal, normative, and institutional constraints.

The abstract states the chapter's predictive frame and proposed disciplinary borrowing.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 1, that interpretation-as-prediction is a better organizing frame than treating contract interpretation solely as a philosophical search for linguistic meaning. The predictive frame asks which methods best recover party intent rather than which theory describes meaning in the abstract. This is significant because methodological choice should turn on efficacy in adjudication rather than linguistic metaphysics alone. It connects to interpretation as prediction, linguistic meaning, party intent, methodology, efficacy, philosophy of language.

interpretation-as-prediction is a better organizing frame than treating contract interpretation solely as a philosophical search for linguistic meaning. This matters because methodological choice should turn on efficacy in adjudication rather than linguistic metaphysics alone.

interpretation as prediction
linguistic meaning
party intent
methodology
efficacy
philosophy of language

Language remains important evidence within the predictive frame.

The abstract identifies defense of interpretation-as-prediction as the first contribution.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 1, that textualists and contextualists can both claim superior accuracy because they conflate accuracy with precision. One concept concerns closeness to the correct answer; the other concerns consistency and predictability across decisions. This is significant because separating the two reveals genuine tradeoffs obscured by a shared label. It connects to accuracy, precision, textualism, contextualism, predictability, conceptual clarity.

textualists and contextualists can both claim superior accuracy because they conflate accuracy with precision. This matters because separating the two reveals genuine tradeoffs obscured by a shared label.

accuracy
precision
textualism
contextualism
predictability
conceptual clarity

The distinction does not by itself determine the normatively optimal balance.

The abstract previews the precision-accuracy distinction as the second contribution.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 1, that the bias-variance tradeoff helps explain the costs of interpretive simplicity and complexity. Rules using few inputs may make systematic errors, while highly contextual rules may overfit particular disputes and generalize poorly. This is significant because neither maximal textual restriction nor maximal evidentiary openness necessarily produces the best predictive model. It connects to bias, variance, model complexity, textualism, contextualism, generalization.

the bias-variance tradeoff helps explain the costs of interpretive simplicity and complexity. This matters because neither maximal textual restriction nor maximal evidentiary openness necessarily produces the best predictive model.

bias
variance
model complexity
textualism
contextualism
generalization

The statistical framework is analogical and the chapter later notes that legal decision-making is not fully captured by a linear equation.

The abstract identifies bias-variance as the chapter's third contribution.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 1, that purpose simulation is more important and less understood than conventional meaning-focused interpretation. The chapter distinguishes predicting what words mean from modeling what parties were trying to accomplish. This is significant because purpose and meaning can diverge, requiring different objectives, evidence, and normative justification. It connects to simulation, purposive interpretation, linguistic meaning, party goals, prediction, contract theory.

purpose simulation is more important and less understood than conventional meaning-focused interpretation. This matters because purpose and meaning can diverge, requiring different objectives, evidence, and normative justification.

simulation
purposive interpretation
linguistic meaning
party goals
prediction
contract theory

The author labels this final claim the chapter's most speculative contribution.

The abstract previews the distinction between interpretation and simulation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 2, that time is the source of interpretive conflict because contracts regulate an unpredictable future. Courts arrive only after a dispute and reconstruct the parties' treatment of a present contingency from evidence left in the past. This is significant because contract doctrine must bridge formation-time plans and later states of the world. It connects to contract time, future contingencies, litigation, reconstruction, evidence, judicial role.

time is the source of interpretive conflict because contracts regulate an unpredictable future. This matters because contract doctrine must bridge formation-time plans and later states of the world.

contract time
future contingencies
litigation
reconstruction
evidence
judicial role

Some theories assign judges constructive or normative roles beyond reconstruction.

Page 2 opens with the temporal structure of contract performance and adjudication.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 2, that contracting parties plan against the erosion caused by new information, shifting preferences, and changed environments. They allocate risks such as price changes or equipment failure in advance and ordinarily expect deferential enforcement. This is significant because contract drafting is an attempt to govern future uncertainty from a moment of incomplete knowledge. It connects to risk allocation, changed circumstances, contract drafting, future uncertainty, party autonomy, literal enforcement.

contracting parties plan against the erosion caused by new information, shifting preferences, and changed environments. This matters because contract drafting is an attempt to govern future uncertainty from a moment of incomplete knowledge.

risk allocation
changed circumstances
contract drafting
future uncertainty
party autonomy
literal enforcement

The simple planning narrative assumes the parties successfully addressed the later contingency.

Page 2 describes how parties anticipate change and design contractual responses.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 2–3, that the parties' agreement is often distributed across documents, practices, norms, advice, and culture rather than contained in one canonical text. Courts must select among dispersed sources under constraints of process and judicial economy. This is significant because the boundaries of the evidentiary record are themselves a central interpretive choice. It connects to diffuse agreement, course of dealing, industry norms, extrinsic evidence, judicial economy, contract formation.

the parties' agreement is often distributed across documents, practices, norms, advice, and culture rather than contained in one canonical text. This matters because the boundaries of the evidentiary record are themselves a central interpretive choice.

diffuse agreement
course of dealing
industry norms
extrinsic evidence
judicial economy
contract formation

Not every social fact is probative or practically admissible.

Pages 2-3 identify diffusion of contractual plans as the first hard problem.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 3, that parties cannot plan for every contingency, and black-swan events can separate what they wrote from what they would have wanted. Facially absolute language may not resolve a situation that neither side contemplated. This is significant because interpretation sometimes requires counterfactual reconstruction rather than literal application of an ex ante sentence. It connects to incomplete contracts, black swans, contractual silence, counterfactual intent, gap filling, future events.

parties cannot plan for every contingency, and black-swan events can separate what they wrote from what they would have wanted. This matters because interpretation sometimes requires counterfactual reconstruction rather than literal application of an ex ante sentence.

incomplete contracts
black swans
contractual silence
counterfactual intent
gap filling
future events

Counterfactual intent may be uncertain and vulnerable to hindsight bias.

Page 3 identifies unplanned contingencies as the second hard problem.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 3, that interpretation as prediction asks how the parties would have allocated rights and liabilities had they discussed the present contingency at formation. The task is predictive or retrodictive: reconstruct the answer past parties would give in light of a later state of the world. This is significant because the frame states a testable objective for comparing interpretive methods. It connects to hypothetical bargain, retrodiction, party intent, rights allocation, interpretive method, prediction.

interpretation as prediction asks how the parties would have allocated rights and liabilities had they discussed the present contingency at formation. This matters because the frame states a testable objective for comparing interpretive methods.

hypothetical bargain
retrodiction
party intent
rights allocation
interpretive method
prediction

The paper does not claim that every omitted contingency has a unique recoverable answer.

Page 3 states the core counterfactual question of the prediction framework.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 3–4, that legal interpretation shares enough structure with prediction disciplines to permit productive conceptual borrowing. Although one field concerns language, doctrine, and humans and the other models and machines, both choose inputs and methods to estimate an unknown target. This is significant because surface differences should not obscure common problems of error, complexity, and evaluation. It connects to disciplinary arbitrage, legal interpretation, statistical learning, model inputs, prediction error, method comparison.

legal interpretation shares enough structure with prediction disciplines to permit productive conceptual borrowing. This matters because surface differences should not obscure common problems of error, complexity, and evaluation.

disciplinary arbitrage
legal interpretation
statistical learning
model inputs
prediction error
method comparison

Borrowed concepts require adaptation to legal values and institutions.

Pages 3-4 explain the basis and limits of the machine-learning analogy.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 3–4, that the linguistic theory seeks to establish the meaning of expressions, whereas the predictive theory seeks to estimate party intent. The former invokes philosophy of language; the latter treats words as one kind of evidence in an empirical decision problem. This is significant because competing frames can recommend different evidence and different criteria for success. It connects to linguistic theory, predictive theory, semantics, party intent, evidence, interpretive goals.

the linguistic theory seeks to establish the meaning of expressions, whereas the predictive theory seeks to estimate party intent. This matters because competing frames can recommend different evidence and different criteria for success.

linguistic theory
predictive theory
semantics
party intent
evidence
interpretive goals

Meaning and intent frequently overlap, so the distinction is analytical rather than absolute.

Pages 3-4 introduce the chapter's principal theoretical contrast.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 4, that contextualist reliance on linguistic indeterminacy often uses a motte-and-bailey move. The uncontroversial proposition that context affects meaning is used to support the stronger institutional claim that courts should admit broad extrinsic evidence. This is significant because a true linguistic premise does not automatically decide a legal rule about evidence. It connects to motte and bailey, contextualism, linguistic pragmatics, extrinsic evidence, legal reasoning, institutional design.

contextualist reliance on linguistic indeterminacy often uses a motte-and-bailey move. This matters because a true linguistic premise does not automatically decide a legal rule about evidence.

motte and bailey
contextualism
linguistic pragmatics
extrinsic evidence
legal reasoning
institutional design

The critique targets a common argumentative pattern, not every defense of contextualism.

Page 4 previews the first substantive contribution.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 4, that interpretive methods should be compared through partial and measurable performance rather than claims to perfect correctness. The predictive frame accepts degrees of success and distinguishes consistency from closeness to intent. This is significant because a probabilistic task calls for evaluation metrics rather than all-or-nothing declarations of meaning. It connects to partial accuracy, measurement, probabilistic judgment, interpretive performance, empirical legal studies, model evaluation.

interpretive methods should be compared through partial and measurable performance rather than claims to perfect correctness. This matters because a probabilistic task calls for evaluation metrics rather than all-or-nothing declarations of meaning.

partial accuracy
measurement
probabilistic judgment
interpretive performance
empirical legal studies
model evaluation

Reliable ground truth about party intent may itself be difficult to observe.

Page 4 explains the prediction frame's openness to partial accuracy and measurement.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 4, that bias and variance provide a second way to evaluate interpretive models beyond the precision-accuracy distinction. Model complexity affects systematic error, instability, explainability, and predictability. This is significant because legal theories must account for how rules perform on new disputes, not only how well they fit one case. It connects to bias-variance tradeoff, model complexity, generalization, interpretive theory, explainability, predictability.

bias and variance provide a second way to evaluate interpretive models beyond the precision-accuracy distinction. This matters because legal theories must account for how rules perform on new disputes, not only how well they fit one case.

bias-variance tradeoff
model complexity
generalization
interpretive theory
explainability
predictability

The chapter uses the framework to clarify rather than mechanically optimize doctrine.

Page 4 identifies bias-variance as a distinct contribution.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 4, that meaning prediction and purpose simulation are different interpretive modalities with different data and normative implications. Courts commonly blend them even though one models linguistic expression and the other models the parties themselves. This is significant because clarifying the target of prediction is necessary before choosing evidence or measuring accuracy. It connects to meaning, purpose, simulation, objective function, evidentiary data, normative theory.

meaning prediction and purpose simulation are different interpretive modalities with different data and normative implications. This matters because clarifying the target of prediction is necessary before choosing evidence or measuring accuracy.

meaning
purpose
simulation
objective function
evidentiary data
normative theory

The simulation proposal is expressly exploratory.

Page 4 states the final contribution and its modeling implications.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 4–5, that modern doctrine generally states that interpretation seeks the parties' intention at the time of contract formation. Written language is then treated as the best evidence of that historical intention. This is significant because the consensus objective leaves unresolved how courts should infer intent from available sources. It connects to formation intent, objective interpretation, written agreement, historical inquiry, contract doctrine, evidence.

modern doctrine generally states that interpretation seeks the parties' intention at the time of contract formation. This matters because the consensus objective leaves unresolved how courts should infer intent from available sources.

formation intent
objective interpretation
written agreement
historical inquiry
contract doctrine
evidence

The chapter distinguishes interpretation from judicial construction that overrides clear terms on external grounds.

Pages 4-5 state the conventional goal and the writing-centered evidentiary premise.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 5, that contract theory may posit a correct interpretive answer even though courts disagree about how to operationalize the search. The controversy concerns the model used to estimate the answer rather than the abstract aspiration to recover intent. This is significant because agreement on ends can conceal deep disagreement about inputs, costs, and error. It connects to correct answer, operationalization, interpretive methods, party intent, adjudication, contract theory.

contract theory may posit a correct interpretive answer even though courts disagree about how to operationalize the search. This matters because agreement on ends can conceal deep disagreement about inputs, costs, and error.

correct answer
operationalization
interpretive methods
party intent
adjudication
contract theory

The existence of a theoretically correct answer does not make it observable.

Page 5 separates the accepted objective from contested implementation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 5, that textualism uses a relatively restricted evidentiary set centered on the final document. It relies on text, judicial semantic knowledge, dictionaries, and interpretive canons within the contract's four corners. This is significant because textualism is best understood as constrained contextual inquiry rather than context-free word worship. It connects to textualism, four corners, dictionaries, interpretive canons, semantic knowledge, evidence limits.

textualism uses a relatively restricted evidentiary set centered on the final document. This matters because textualism is best understood as constrained contextual inquiry rather than context-free word worship.

textualism
four corners
dictionaries
interpretive canons
semantic knowledge
evidence limits

Actual textualist practices vary across jurisdictions and cases.

Page 5 defines the principal sources used by textualist interpretation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 5, that textualists consider internal context but ordinarily reach outside the document only for unavoidable ambiguity or voidability. Their disagreement with contextualists concerns the permissible range and timing of contextual evidence. This is significant because the debate is about institutional boundaries around context, not whether words are ever situated. It connects to internal context, ambiguity, parol evidence, textualism, voidability, evidentiary sequence.

textualists consider internal context but ordinarily reach outside the document only for unavoidable ambiguity or voidability. This matters because the debate is about institutional boundaries around context, not whether words are ever situated.

internal context
ambiguity
parol evidence
textualism
voidability
evidentiary sequence

Some textualist regimes recognize additional exceptions not catalogued here.

Page 5 qualifies the definition of textualism and explains its thresholds for outside evidence.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 5, that contextualism treats the contractual text as important but admits a much broader body of potentially probative evidence. Special-community usage, trade usage, course of dealing, course of performance, and formation context can all inform the inference. This is significant because contextualism changes both the information available to courts and the complexity of adjudication. It connects to contextualism, trade usage, course of performance, course of dealing, formation context, parol evidence.

contextualism treats the contractual text as important but admits a much broader body of potentially probative evidence. This matters because contextualism changes both the information available to courts and the complexity of adjudication.

contextualism
trade usage
course of performance
course of dealing
formation context
parol evidence

Contextualist approaches differ in admissibility rules and evidentiary weight.

Page 5 defines the broader contextualist evidence base.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 6, that purposive interpretation asks why the parties adopted a term, while literal interpretation asks what the language means. For a fourteen-day delivery clause, a literalist parses when the clock starts, whereas a purposivist investigates the function of the deadline. This is significant because identical text can be analyzed through different target variables. It connects to purposivism, literalism, contract purpose, word meaning, delivery terms, interpretive target.

purposive interpretation asks why the parties adopted a term, while literal interpretation asks what the language means. This matters because identical text can be analyzed through different target variables.

purposivism
literalism
contract purpose
word meaning
delivery terms
interpretive target

Purpose and literal meaning can point to the same result in many cases.

Page 6 defines the chapter's half-theories through a delivery example.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 6, that purposivism and literalism can combine with either textualism or contextualism. A contextualist may use outside evidence to identify meaning or purpose, and a text can itself state its purpose. This is significant because evidentiary breadth and interpretive objective are separate dimensions of theory. It connects to purposivism, literalism, textualism, contextualism, two-dimensional taxonomy, contract evidence.

purposivism and literalism can combine with either textualism or contextualism. This matters because evidentiary breadth and interpretive objective are separate dimensions of theory.

purposivism
literalism
textualism
contextualism
two-dimensional taxonomy
contract evidence

The author observes correlations between the pairings in practice rather than logical necessity.

Page 6 explains compatibility among the chapter's two-and-a-half schools.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 6–7, that American scholars tend toward contextualism while courts tend toward textualism. The longstanding dispute also carries pragmatic, metaphysical, and sometimes political associations. This is significant because institutional practice may diverge from academic preference even when both use the language of party intent. It connects to courts, legal scholarship, formalism, contextualism, legal politics, institutional practice.

American scholars tend toward contextualism while courts tend toward textualism. This matters because institutional practice may diverge from academic preference even when both use the language of party intent.

courts
legal scholarship
formalism
contextualism
legal politics
institutional practice

The prevalence statement relies on cited commentary and does not report a new jurisdictional census.

Pages 6-7 situate the interpretive schools in modern debate.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 7, that the linguistic model seeks word meaning, whereas the predictive model uses any reliable source to estimate what the parties wanted to mean. Text, industry norms, and judicial knowledge all matter to the extent that they improve the estimate. This is significant because evidence should be selected by predictive contribution rather than category alone. It connects to linguistic meaning, predictive evidence, industry norms, judicial priors, party intent, model selection.

the linguistic model seeks word meaning, whereas the predictive model uses any reliable source to estimate what the parties wanted to mean. This matters because evidence should be selected by predictive contribution rather than category alone.

linguistic meaning
predictive evidence
industry norms
judicial priors
party intent
model selection

Legal admissibility, fairness, and cost constrain the otherwise broad empirical criterion.

Page 7 directly contrasts the two theories' targets and tools.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 7, that the predictive approach is bottom-up, empirical, and committed to fidelity rather than metaphysical purity. It asks which process maximizes predictive accuracy after adjudication and error costs are included. This is significant because interpretive doctrine can be revised when evidence shows that another process performs better. It connects to bottom-up method, empirical jurisprudence, predictive fidelity, error costs, adjudication costs, adaptive doctrine.

the predictive approach is bottom-up, empirical, and committed to fidelity rather than metaphysical purity. This matters because interpretive doctrine can be revised when evidence shows that another process performs better.

bottom-up method
empirical jurisprudence
predictive fidelity
error costs
adjudication costs
adaptive doctrine

Measuring fidelity requires a defensible proxy for parties' unobservable intentions.

Page 7 states the efficacy criterion of interpretation-as-prediction.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 7, that the best interpretive method can depend on institutional strength. Weak institutions may need text-centered rules, while capable and trustworthy institutions can use broader evidence productively. This is significant because interpretive theory should be institution-sensitive rather than universal across courts. It connects to institutional competence, textualism, contextualism, judicial capacity, comparative institutions, legal design.

the best interpretive method can depend on institutional strength. This matters because interpretive theory should be institution-sensitive rather than universal across courts.

institutional competence
textualism
contextualism
judicial capacity
comparative institutions
legal design

The chapter does not offer an operational measure of institutional strength.

Page 7 connects evidentiary breadth to the capabilities of adjudicating institutions.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 7, that contract interpretation is probabilistic and uncertain even when doctrine speaks in categorical terms. The predictive frame openly treats judicial conclusions as estimates rather than metaphysical discoveries. This is significant because acknowledging uncertainty makes error analysis and model comparison possible. It connects to probability, uncertainty, judicial confidence, model comparison, party intent, legal epistemology.

contract interpretation is probabilistic and uncertain even when doctrine speaks in categorical terms. This matters because acknowledging uncertainty makes error analysis and model comparison possible.

probability
uncertainty
judicial confidence
model comparison
party intent
legal epistemology

Probabilistic framing does not eliminate the need for binary judgments in litigation.

Page 7 describes the predictive frame's acceptance of uncertainty.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 7–8, that contextualists often move from the true claim that context shapes meaning to the unsupported claim that broad extrinsic evidence must be admitted. The motte-and-bailey diagnosis separates linguistic pragmatics from the design of an evidentiary regime. This is significant because doctrinal conclusions require comparative institutional evidence, not only observations about language. It connects to context dependence, extrinsic evidence, motte and bailey, contextualism, evidence law, institutional analysis.

contextualists often move from the true claim that context shapes meaning to the unsupported claim that broad extrinsic evidence must be admitted. This matters because doctrinal conclusions require comparative institutional evidence, not only observations about language.

context dependence
extrinsic evidence
motte and bailey
contextualism
evidence law
institutional analysis

A contextualist can defend broad evidence on predictive or fairness grounds without committing this fallacy.

Pages 7-8 introduce and define the alleged argumentative move.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 8, that every sentence operates within interpretive assumptions and a situation. Stanley Fish's point captures the uncontroversial linguistic insight that reference and meaning depend on context. This is significant because textualism should not be caricatured as belief in wholly context-free words. It connects to Stanley Fish, linguistic pragmatics, context, reference, textualism, interpretive assumptions.

every sentence operates within interpretive assumptions and a situation. This matters because textualism should not be caricatured as belief in wholly context-free words.

Stanley Fish
linguistic pragmatics
context
reference
textualism
interpretive assumptions

The linguistic insight alone does not specify which contextual facts a court should admit.

Page 8 presents the strong, defensible motte of the contextualist argument.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 8–9, that language's indeterminacy does not logically entail admitting testimony, trade usage, or every circumstance surrounding formation. Corbin's 'therefore' moves from a semantic proposition to a much stronger rule about judicial evidence. This is significant because a theory of meaning cannot substitute for a theory of adjudicative cost and reliability. It connects to Corbin, semantic indeterminacy, parol evidence, trade usage, legal entailment, evidentiary policy.

language's indeterminacy does not logically entail admitting testimony, trade usage, or every circumstance surrounding formation. This matters because a theory of meaning cannot substitute for a theory of adjudicative cost and reliability.

Corbin
semantic indeterminacy
parol evidence
trade usage
legal entailment
evidentiary policy

The critique does not deny that particular kinds of extrinsic evidence can be highly probative.

Pages 8-9 identify the bailey and the inferential gap supporting it.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 9, that a jurisprudence requiring all context produces an infinite regress with no principled stopping point. If every circumstance requires its own surrounding circumstances, complete context is impossible. This is significant because real courts must use bounded evidence and a satisfactory rather than exhaustive account of meaning. It connects to infinite regress, bounded rationality, contextual evidence, judicial economy, linguistic theory, stopping rules.

a jurisprudence requiring all context produces an infinite regress with no principled stopping point. This matters because real courts must use bounded evidence and a satisfactory rather than exhaustive account of meaning.

infinite regress
bounded rationality
contextual evidence
judicial economy
linguistic theory
stopping rules

Practical contextualism already uses relevance and sufficiency limits.

Page 9 applies the self-referential view of language to the impossibility of total context.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 9–10, that even committed contextualists ultimately accept a good-enough probabilistic approximation. Remote or implausible interpretations can be ignored despite theoretical relevance. This is significant because actual interpretive practice is empirical and pragmatic rather than an unlimited search for veritable meaning. It connects to good-enough standard, probabilistic approximation, pragmatism, contextualism, legal relevance, bounded inquiry.

even committed contextualists ultimately accept a good-enough probabilistic approximation. This matters because actual interpretive practice is empirical and pragmatic rather than an unlimited search for veritable meaning.

good-enough standard
probabilistic approximation
pragmatism
contextualism
legal relevance
bounded inquiry

The threshold for good enough remains a normative and institutional question.

Pages 9-10 explain the inevitable stopping rule in contextualist practice.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 10, that the strongest case for contextualism is that context improves predictions of actual party intent. References to a computer or payment may incorporate the object or arrangement the parties discussed, not an abstract prototype. This is significant because contextualism needs no metaphysical premise when it can be defended through improved fidelity. It connects to contextualism, predictive accuracy, implicit context, UCC, party psychology, commercial meaning.

the strongest case for contextualism is that context improves predictions of actual party intent. This matters because contextualism needs no metaphysical premise when it can be defended through improved fidelity.

contextualism
predictive accuracy
implicit context
UCC
party psychology
commercial meaning

The value of context depends on reliability and cost in the particular institution.

Page 10 restates contextualism in predictive rather than semiotic terms.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 10, that the strongest case for textualism is that a cheap, constrained method can be sufficiently accurate. Plain meaning may perform well enough under practical constraints to make broader inquiry not worth its cost. This is significant because textualism is an empirical claim about cost-adjusted performance, not a claim that words possess context-free essence. It connects to textualism, plain meaning, cost-effectiveness, satisficing, predictive accuracy, judicial economy.

the strongest case for textualism is that a cheap, constrained method can be sufficiently accurate. This matters because textualism is an empirical claim about cost-adjusted performance, not a claim that words possess context-free essence.

textualism
plain meaning
cost-effectiveness
satisficing
predictive accuracy
judicial economy

Sufficient accuracy cannot be assumed and may vary by transaction or institution.

Page 10 gives the predictive reconstruction of textualist argument.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 10, that cost- and fairness-weighted predictive accuracy supplies a common benchmark for interpretive methods. The framework clears conceptual confusion and permits comparison across models rather than allegiance to labels. This is significant because a shared metric can expose which disagreements are empirical and which are normative. It connects to common benchmark, fairness, error costs, method comparison, empirical testing, contract interpretation.

cost- and fairness-weighted predictive accuracy supplies a common benchmark for interpretive methods. This matters because a shared metric can expose which disagreements are empirical and which are normative.

common benchmark
fairness
error costs
method comparison
empirical testing
contract interpretation

Weights assigned to cost, fairness, and error direction remain contestable.

Page 10 proposes the evaluation benchmark used in the rest of the chapter.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 11, that interpretive debates use accuracy ambiguously, allowing both sides to claim victory. Machine-learning practice distinguishes repeatability from closeness to the target. This is significant because terminological precision is needed before courts and scholars can compare performance. It connects to accuracy, precision, terminology, machine learning, interpretive debate, performance metrics.

interpretive debates use accuracy ambiguously, allowing both sides to claim victory. This matters because terminological precision is needed before courts and scholars can compare performance.

accuracy
precision
terminology
machine learning
interpretive debate
performance metrics

The chapter adopts specialized terms whose usage varies across technical fields.

Page 11 introduces the ambiguity and the proposed distinction.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 11, that Varney v. Ditmars turns the phrase fair share of profits into a test of interpretive performance. An architect stayed after a bonus promise, was fired, and received no profit share because the majority found the phrase too indefinite. This is significant because the case exposes how a doctrine seeking certainty can select an outcome plainly inconsistent with the bargain's direction. It connects to Varney v. Ditmars, indefiniteness, profit sharing, employment contract, fair share, contract remedies.

Varney v. Ditmars turns the phrase fair share of profits into a test of interpretive performance. This matters because the case exposes how a doctrine seeking certainty can select an outcome plainly inconsistent with the bargain's direction.

Varney v. Ditmars
indefiniteness
profit sharing
employment contract
fair share
contract remedies

The case record did not supply the industry-custom evidence Cardozo wanted.

Page 11 states the facts, majority holding, and Cardozo's contextual response.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 11–12, that the Varney majority chose the only allocation certainly inconsistent with an agreement to share some profits. Denying the claim gave the employer one hundred percent even though the parties plainly contemplated a positive employee share. This is significant because formal certainty can maximize error rather than minimize it. It connects to formalism, indefinite terms, zero recovery, party intent, accuracy, judicial error.

the Varney majority chose the only allocation certainly inconsistent with an agreement to share some profits. This matters because formal certainty can maximize error rather than minimize it.

formalism
indefinite terms
zero recovery
party intent
accuracy
judicial error

The parties may not have shared a unique percentage, so any precise positive award would also be uncertain.

Pages 11-12 characterize full employer retention as the least accurate allocation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 12, that Varney can be reconstructed as a penalty default sacrificing current accuracy to induce clearer future drafting. On this account, nonenforcement warns later parties that vague bonus terms will fail. This is significant because a seemingly mistaken interpretation may pursue dynamic incentives rather than case-specific fidelity. It connects to penalty defaults, drafting incentives, dynamic effects, indefiniteness, future contracts, interpretive policy.

Varney can be reconstructed as a penalty default sacrificing current accuracy to induce clearer future drafting. This matters because a seemingly mistaken interpretation may pursue dynamic incentives rather than case-specific fidelity.

penalty defaults
drafting incentives
dynamic effects
indefiniteness
future contracts
interpretive policy

The opinion did not clearly articulate this incentive rationale.

Page 12 offers the penalty-default interpretation as a possible salvage.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 12, that the proposed penalty default in Varney targets the wrong drafter when employers control employment agreements. An employer benefits from vagueness if indefiniteness eliminates its duty to share profits. This is significant because information-forcing rules must be assigned against the party able and motivated to clarify. It connects to information forcing, employer drafting, penalty default, strategic ambiguity, employment law, incentives.

the proposed penalty default in Varney targets the wrong drafter when employers control employment agreements. This matters because information-forcing rules must be assigned against the party able and motivated to clarify.

information forcing
employer drafting
penalty default
strategic ambiguity
employment law
incentives

Bargaining power and drafting responsibility may vary in other relationships.

Page 12 explains why the dynamic defense of Varney is unpersuasive.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 12, that a textualist court could enforce a rough general understanding of fair share even if it misses the industry's exact custom. A semi-arbitrary twenty-eighty division would be inaccurate relative to a stipulated ten-ninety norm but directionally faithful to a sharing promise. This is significant because partial accuracy can dominate the perfectly certain zero-share result. It connects to textual priors, rough justice, partial accuracy, profit allocation, industry custom, contract enforcement.

a textualist court could enforce a rough general understanding of fair share even if it misses the industry's exact custom. This matters because partial accuracy can dominate the perfectly certain zero-share result.

textual priors
rough justice
partial accuracy
profit allocation
industry custom
contract enforcement

The numerical allocations are stipulated illustrations rather than empirical findings about architects.

Page 12 constructs the hypothetical textualist estimate.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 12–13, that contextual evidence may bring a court closer to industry custom while also widening the range of possible outcomes. Testimony and practice can reveal the true split but can also mislead through motivated or unreliable proof. This is significant because more information may improve expected closeness while reducing predictability. It connects to contextual evidence, industry custom, motivated testimony, outcome distribution, accuracy, precision.

contextual evidence may bring a court closer to industry custom while also widening the range of possible outcomes. This matters because more information may improve expected closeness while reducing predictability.

contextual evidence
industry custom
motivated testimony
outcome distribution
accuracy
precision

The distribution is hypothetical and designed to isolate concepts.

Pages 12-13 construct the contextualist outcome distribution.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 13, that textualism can be precise even when biased away from the parties' likely intent. A narrow method clusters decisions around a stable twenty-eighty allocation that parties can anticipate. This is significant because predictability is a distinct institutional benefit rather than proof of substantive correctness. It connects to textualism, precision, predictability, bias, precedent, planning.

textualism can be precise even when biased away from the parties' likely intent. This matters because predictability is a distinct institutional benefit rather than proof of substantive correctness.

textualism precision predictability bias precedent planning

The figure is illustrative and does not estimate actual judicial distributions.

Page 13 explains the left panel of the hypothetical figure.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 13, that contextualism can be more accurate on average but less precise across cases. Some decisions reach the stipulated true custom while others scatter far from both truth and one another. This is significant because fidelity and consistency may move in opposite directions. It connects to contextualism, accuracy, variance, judicial discretion, outcome scatter, predictability.

contextualism can be more accurate on average but less precise across cases. This matters because fidelity and consistency may move in opposite directions.

contextualism
accuracy
variance
judicial discretion
outcome scatter
predictability

Greater variance is a modeled possibility, not proof that every contextualist system is erratic.

Page 13 explains the right panel of the hypothetical figure.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 13–14, that precision alone can be maximized by an arbitrary rule. Always ruling for one side is perfectly repeatable but has no necessary connection to party intent. This is significant because certainty is valuable only when paired with at least reasonable accuracy. It connects to mechanical rules, certainty, precision, arbitrariness, rule of law, party intent.

precision alone can be maximized by an arbitrary rule. This matters because certainty is valuable only when paired with at least reasonable accuracy.

mechanical rules
certainty
precision
arbitrariness
rule of law
party intent

Some bright-line rules may have independent fairness or administrability justifications.

Pages 13-14 show why precision is a quantity rather than an unqualified virtue.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 14, that interpretive methods should be compared along a frontier where both precision and accuracy can improve. The choice is broader than fantastically certain textualism versus messy but accurate contextualism. This is significant because hybrid methods may dominate the corner solutions without requiring an all-or-nothing school. It connects to Pareto frontier, hybrid interpretation, textualism, contextualism, method design, performance improvement.

interpretive methods should be compared along a frontier where both precision and accuracy can improve. This matters because hybrid methods may dominate the corner solutions without requiring an all-or-nothing school.

Pareto frontier
hybrid interpretation
textualism
contextualism
method design
performance improvement

Whether a proposed hybrid dominates must be established empirically.

Page 14 reframes the supposed binary through a performance frontier.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 14, that the desired balance between precision and accuracy depends on normative commitments. Equal-treatment theories value clustered outcomes, while individualized-merit theories value closeness to each pair's actual intent. This is significant because interpretive design embeds a theory of justice as well as a prediction model. It connects to equal treatment, individual merit, legal justice, precision, accuracy, normative choice.

the desired balance between precision and accuracy depends on normative commitments. This matters because interpretive design embeds a theory of justice as well as a prediction model.

equal treatment
individual merit
legal justice
precision
accuracy
normative choice

The chapter does not select one complete theory of justice.

Page 14 connects the statistical distinction to legal values.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 14, that commercial parties may value precision because it supports planning and settlement. Predictable interpretations reduce dispute probability and permit pricing, but only while outcomes remain reasonably faithful. This is significant because textualism's administrability benefit has a limit when systematic error blocks desired bargains. It connects to commercial planning, settlement, litigation costs, textualism, predictability, party autonomy.

commercial parties may value precision because it supports planning and settlement. This matters because textualism's administrability benefit has a limit when systematic error blocks desired bargains.

commercial planning
settlement
litigation costs
textualism
predictability
party autonomy

Risk preferences and bargaining conditions differ among parties.

Page 14 states the efficiency case and its accuracy constraint.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 14–15, that risk neutrality does not make interpretive bias irrelevant. Schwartz and Scott emphasize being correct on average, but choosing textualism can move the mean outcome as well as alter its variance. This is significant because a stable decision rule can systematically misprice or misenforce contracts even for sophisticated actors. It connects to risk neutrality, Schwartz and Scott, mean error, textualism, sophisticated parties, contract design.

risk neutrality does not make interpretive bias irrelevant. This matters because a stable decision rule can systematically misprice or misenforce contracts even for sophisticated actors.

risk neutrality
Schwartz and Scott
mean error
textualism
sophisticated parties
contract design

The critique accepts that sophisticated parties may draft around some predictable biases.

Pages 14-15 challenge the claim that variance alone distinguishes the methods.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 15, that contextualism's greater evidentiary breadth may improve the mean accuracy of interpretation. If additional evidence reveals industry-specific intent, its advantage is not merely occasional correction within the same average. This is significant because conceding contextual accuracy weakens a defense of textualism based solely on unbiased averages. It connects to contextual accuracy, mean outcome, industry evidence, method comparison, interpretive bias, contract scholarship.

contextualism's greater evidentiary breadth may improve the mean accuracy of interpretation. This matters because conceding contextual accuracy weakens a defense of textualism based solely on unbiased averages.

contextual accuracy
mean outcome
industry evidence
method comparison
interpretive bias
contract scholarship

The size of the improvement remains an empirical question.

Page 15 explains how contextualism can change the locus of the mean.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 15, that textualism retains a distinct advantage in predictability even if contextualism is more accurate. More clustered decisions facilitate settlement and reduce disputes. This is significant because method choice involves real institutional tradeoffs rather than a declaration that one school owns accuracy. It connects to predictability, settlement, dispute reduction, textualism, contextualism, institutional tradeoff.

textualism retains a distinct advantage in predictability even if contextualism is more accurate. This matters because method choice involves real institutional tradeoffs rather than a declaration that one school owns accuracy.

predictability
settlement
dispute reduction
textualism
contextualism
institutional tradeoff

The benefit depends on textual decisions remaining within an acceptable accuracy range.

Page 15 summarizes the precision advantage of textualism.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 15–16, that additional extrinsic evidence can have negative marginal informational value. Parol evidence may be forged, inaccurate, self-serving, confusing, or unduly costly. This is significant because more data does not automatically improve a legal prediction when input quality is poor. It connects to parol evidence, information quality, evidence law, contextualism, marginal value, judicial error.

additional extrinsic evidence can have negative marginal informational value. This matters because more data does not automatically improve a legal prediction when input quality is poor.

parol evidence
information quality
evidence law
contextualism
marginal value
judicial error

Some extrinsic evidence is reliable and highly informative; the claim concerns expected value at the margin.

Pages 15-16 question the contextualist assumption that every added input improves accuracy.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 16, that evidence law already recognizes that relevant information can reduce decision quality. Rule 403 permits exclusion when probative value is outweighed by prejudice, confusion, delay, or cumulation. This is significant because legal doctrine itself embodies a model-selection principle that filters harmful inputs. It connects to Rule 403, probative value, prejudice, evidence filtering, model selection, judicial administration.

evidence law already recognizes that relevant information can reduce decision quality. This matters because legal doctrine itself embodies a model-selection principle that filters harmful inputs.

Rule 403
probative value
prejudice
evidence filtering
model selection
judicial administration

Contract interpretation may operate under different evidentiary rules and burdens.

Page 16 connects negative informational value to familiar evidence doctrine.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 16, that interpretive processes are models that transform evidentiary inputs into predicted outcomes. Their complexity affects accuracy, explainability, and the ability of parties to anticipate decisions. This is significant because the number and weighting of features are institutional design choices. It connects to interpretive models, features, verdict prediction, model complexity, explainability, legal institutions.

interpretive processes are models that transform evidentiary inputs into predicted outcomes. This matters because the number and weighting of features are institutional design choices.

interpretive models
features
verdict prediction
model complexity
explainability
legal institutions

Judicial reasoning includes normative judgment not fully reducible to model mechanics.

Page 16 establishes the modeling analogy underlying bias-variance analysis.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 16, that textualism is relatively low complexity because it limits features to text and a small set of linguistic tools. A narrower input space generally makes the method easier to understand and predict. This is significant because simplicity can be a source of institutional transparency as well as error. It connects to low-complexity models, textualism, feature selection, interpretive canons, explainability, predictability.

textualism is relatively low complexity because it limits features to text and a small set of linguistic tools. This matters because simplicity can be a source of institutional transparency as well as error.

low-complexity models
textualism
feature selection
interpretive canons
explainability
predictability

Parties can write contextual sources into the agreement, making textualism more complex.

Page 16 maps textualist sources onto a low-complexity model.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 16, that contextualism is high complexity because the parties can introduce an open-ended set of circumstances. Emails, conversations, photographs, trade practice, and other facts lack a fixed weighting procedure. This is significant because open feature sets can make outcomes responsive but difficult to reproduce or forecast. It connects to high-complexity models, contextualism, open-ended evidence, feature weighting, judicial discretion, predictability.

contextualism is high complexity because the parties can introduce an open-ended set of circumstances. This matters because open feature sets can make outcomes responsive but difficult to reproduce or forecast.

high-complexity models
contextualism
open-ended evidence
feature weighting
judicial discretion
predictability

Contextualist systems can adopt evidentiary structure and need not consider every offered fact.

Page 16 describes the sources of complexity in contextualist adjudication.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 16–17, that prediction error can be decomposed conceptually into bias, variance, and irreducible error. The equation clarifies that reducing one component may increase another. This is significant because an interpretation method cannot be assessed from a single success or failure. It connects to mean squared error, bias, variance, irreducible error, model evaluation, contract doctrine.

prediction error can be decomposed conceptually into bias, variance, and irreducible error. This matters because an interpretation method cannot be assessed from a single success or failure.

mean squared error
bias
variance
irreducible error
model evaluation
contract doctrine

The paper expressly notes that a linear additive equation may not capture the full complexity of legal decisions.

Pages 16-17 introduce the technical bias-variance framework and its limitation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 17, that a simple winter-umbrella rule illustrates underfitting and predictable bias. It is often wrong on sunny days but remains rational, transparent, and better than never carrying an umbrella. This is significant because a rule can be knowingly imperfect yet useful because its errors are stable and comprehensible. It connects to underfitting, simple rules, predictable error, bias, decision heuristics, explainability.

a simple winter-umbrella rule illustrates underfitting and predictable bias. This matters because a rule can be knowingly imperfect yet useful because its errors are stable and comprehensible.

underfitting
simple rules
predictable error
bias
decision heuristics
explainability

The weather analogy simplifies the stakes and structure of adjudication.

Page 17 uses the simple umbrella rule to make statistical bias intuitive.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 17, that a highly detailed umbrella rule can fit historical weather perfectly and fail on new conditions. Spurious features such as day of week or a weatherperson's shirt color create apparent accuracy without generalization. This is significant because case-specific detail can mistake noise for signal. It connects to overfitting, spurious correlation, variance, generalization, complex rules, historical data.

a highly detailed umbrella rule can fit historical weather perfectly and fail on new conditions. This matters because case-specific detail can mistake noise for signal.

overfitting
spurious correlation
variance
generalization
complex rules
historical data

Careful validation and regularization can mitigate overfitting in technical models.

Page 17 illustrates variance through an overfit weather rule.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 18, that optimal model complexity lies between underfitting and overfitting. As complexity rises, bias usually falls while variance rises, requiring a balance between predictable error and brittle specificity. This is significant because interpretive theory should search for a sweet spot rather than a pure school. It connects to model complexity, underfitting, overfitting, sweet spot, bias-variance tradeoff, hybrid rules.

optimal model complexity lies between underfitting and overfitting. This matters because interpretive theory should search for a sweet spot rather than a pure school.

model complexity
underfitting
overfitting
sweet spot
bias-variance tradeoff
hybrid rules

The relationship between added features and variance is not necessarily linear.

Page 18 states the central bias-variance dilemma.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 18, that interpretive decisions participate in a feedback loop among courts, precedents, and contract drafters. Courts consult prior meanings, and parties choose words in anticipation of future judicial interpretation. This is significant because a method's value includes how well its precedents guide future conduct, not only its resolution of one dispute. It connects to precedent, contract drafting, feedback loops, legal prediction, dynamic effects, interpretive rules.

interpretive decisions participate in a feedback loop among courts, precedents, and contract drafters. This matters because a method's value includes how well its precedents guide future conduct, not only its resolution of one dispute.

precedent
contract drafting
feedback loops
legal prediction
dynamic effects
interpretive rules

Not every interpretation has equal precedential force or drafting salience.

Page 18 describes the dynamic system of judicial and private anticipation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 18–19, that radical textualism produces systematic bias by excluding information necessary to understand contractual language. Every repeated misprediction under the narrow procedure adds to model bias. This is significant because simplification becomes counterproductive when it removes signal rather than noise. It connects to radical textualism, systematic bias, information exclusion, party meaning, model error, contract precedent.

radical textualism produces systematic bias by excluding information necessary to understand contractual language. This matters because simplification becomes counterproductive when it removes signal rather than noise.

radical textualism
systematic bias
information exclusion
party meaning
model error
contract precedent

The criticism targets an extreme method that even many textualists would reject.

Pages 18-19 apply the statistical definition of bias to text-only interpretation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 18–19, that contextual evidence can reduce textualist bias while introducing variance. A more complex model captures relevant circumstances but makes outcomes more sensitive to facts and proof specific to prior cases. This is significant because reducing systematic error does not guarantee better generalization. It connects to contextualism, bias reduction, variance, extrinsic evidence, model complexity, generalization.

contextual evidence can reduce textualist bias while introducing variance. This matters because reducing systematic error does not guarantee better generalization.

contextualism
bias reduction
variance
extrinsic evidence
model complexity
generalization

Not every additional variable increases variance, as the paper expressly notes.

Pages 18-19 state the central tradeoff between the schools.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 19, that context-heavy precedents may overfit earlier disputes and mislead later courts. A past definition of flood can be too bound to the circumstances in which it was produced. This is significant because precedent transfers models across cases, making generalization error a legal concern. It connects to overfit precedent, flood exclusions, insurance contracts, contextualism, generalization error, stare decisis.

context-heavy precedents may overfit earlier disputes and mislead later courts. This matters because precedent transfers models across cases, making generalization error a legal concern.

overfit precedent
flood exclusions
insurance contracts
contextualism
generalization error
stare decisis

Courts can distinguish facts and reduce, but not eliminate, transfer error.

Page 19 explains how variance operates through reliance on prior contextual decisions.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 19, that requiring each contextualist court to start from a blank slate is unrealistic and duplicative. Humans use prior decisions, and parties need stable predictions when choosing words today. This is significant because a method that is accurate only for a Solomonic judge with unlimited evidence is not institutionally viable. It connects to tabula rasa, judicial capacity, precedent, duplication costs, contract planning, institutional realism.

requiring each contextualist court to start from a blank slate is unrealistic and duplicative. This matters because a method that is accurate only for a Solomonic judge with unlimited evidence is not institutionally viable.

tabula rasa
judicial capacity
precedent
duplication costs
contract planning
institutional realism

Fresh analysis may be appropriate in unusually distinctive cases.

Page 19 rejects the blank-slate response to contextual overfitting.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 19–20, that intermediate interpretive approaches are preferable to radical textualism or extreme contextualism. The goal is a method as simple as possible but not simpler, balancing bias and variance. This is significant because corner solutions sacrifice one kind of error to minimize another. It connects to intermediate rules, integrative interpretation, bias-variance balance, textualism, contextualism, method design.

intermediate interpretive approaches are preferable to radical textualism or extreme contextualism. This matters because corner solutions sacrifice one kind of error to minimize another.

intermediate rules
integrative interpretation
bias-variance balance
textualism
contextualism
method design

The paper does not specify one universal middle rule or optimal feature set.

Pages 19-20 state the doctrinal lesson of the bias-variance analysis.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 19–20, that bias and variance do not exhaust the values relevant to interpretation. Certainty, litigation cost, drafting cost, fairness, and the direction of error also matter. This is significant because technical metrics must be nested within legal and normative evaluation. It connects to litigation costs, drafting costs, fairness, error direction, bias, variance.

bias and variance do not exhaust the values relevant to interpretation. This matters because technical metrics must be nested within legal and normative evaluation.

litigation costs
drafting costs
fairness
error direction
bias
variance

The chapter introduces rather than fully integrates these additional objectives.

Pages 19-20 qualify the modeling framework with legal considerations.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 20, that meaning-focused textualism and contextualism share an interpretive paradigm even though they use different amounts of evidence. Both treat the evidentiary breadcrumbs as inputs for understanding the agreement. This is significant because a deeper alternative must change the target, not merely broaden the record. It connects to interpretive paradigm, textualism, contextualism, evidence, meaning, simulation.

meaning-focused textualism and contextualism share an interpretive paradigm even though they use different amounts of evidence. This matters because a deeper alternative must change the target, not merely broaden the record.

interpretive paradigm
textualism
contextualism
evidence
meaning
simulation

The two schools may already incorporate purpose in practice.

Page 20 marks the shift from competing interpretive inputs to a new paradigm.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 20, that Krell v. Henry asks whether a room-hire agreement was implicitly conditional on a coronation procession. The renter paid a deposit for a balcony view, refused the balance after cancellation, and the court treated the procession as foundational. This is significant because the case exposes tension between literal obligation, implied condition, and allocation of unexpected loss. It connects to Krell v. Henry, frustration, implied condition, coronation cases, risk allocation, contract purpose.

Krell v. Henry asks whether a room-hire agreement was implicitly conditional on a coronation procession. This matters because the case exposes tension between literal obligation, implied condition, and allocation of unexpected loss.

Krell v. Henry
frustration
implied condition
coronation cases
risk allocation
contract purpose

The historical record leaves uncertainty about the parties' other possible uses and expectations.

Page 20 introduces the transaction and the court's implied-condition holding.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 20–21, that the implied-condition reading of Krell is not compelled because the parties might have chosen a nonrefundable allocation. Krell incurred opportunity and preparation costs, while Henry might still have used the apartment for a gathering. This is significant because shared awareness of purpose does not uniquely determine who bears cancellation risk. It connects to nonrefundable reservation, opportunity costs, frustration, risk allocation, party expectations, contract remedies.

the implied-condition reading of Krell is not compelled because the parties might have chosen a nonrefundable allocation. This matters because shared awareness of purpose does not uniquely determine who bears cancellation risk.

nonrefundable reservation
opportunity costs
frustration
risk allocation
party expectations
contract remedies

The alternative facts are possibilities used to test the inference, not established findings.

Pages 20-21 explain why neither text nor contextual purpose yields an obvious complete answer.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 21, that the coronation court's cab-fare distinction illustrates the instability of implied-purpose analysis. It said a cancelled sporting event would not excuse a known-purpose cab ride because the purpose was not the contract's foundation. This is significant because calling a purpose foundational can restate rather than solve the allocation problem. It connects to foundation of contract, cab fare, implied condition, purpose, doctrinal line drawing, frustration.

the coronation court's cab-fare distinction illustrates the instability of implied-purpose analysis. This matters because calling a purpose foundational can restate rather than solve the allocation problem.

foundation of contract
cab fare
implied condition
purpose
doctrinal line drawing
frustration

The author notes possible confusion between a completed ride and an advance booking.

Page 21 critiques the court's comparison to carriage contracts.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 21, that the embedded-option view treats termination rules as negotiated allocations of loss rather than all-or-nothing meaning. Deposits, reservation fees, and subcompensatory liquidated damages reflect opportunity cost, risk aversion, information, and third-party commitments. This is significant because remedies can encode a nuanced fallback bargain that neither literalism nor implied-condition analysis captures. It connects to embedded options, liquidated damages, deposits, termination rights, risk aversion, contract remedies.

the embedded-option view treats termination rules as negotiated allocations of loss rather than all-or-nothing meaning. This matters because remedies can encode a nuanced fallback bargain that neither literalism nor implied-condition analysis captures.

embedded options
liquidated damages
deposits
termination rights
risk aversion
contract remedies

The parties' actual loss allocation may remain uncertain without additional evidence.

Page 21 invokes Goldberg's embedded-option account.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 21, that Krell may have covertly allocated the cancellation loss rather than simply declaring one side right. By dismissing the deposit claim while excusing the unpaid balance, the court effectively divided the seventy-five-pound price two-thirds to one-third. This is significant because the remedial outcome can reveal a simulated compromise obscured by the court's interpretive language. It connects to loss sharing, remedial allocation, Krell v. Henry, deposit, judicial construction, embedded option.

Krell may have covertly allocated the cancellation loss rather than simply declaring one side right. This matters because the remedial outcome can reveal a simulated compromise obscured by the court's interpretive language.

loss sharing
remedial allocation
Krell v. Henry
deposit
judicial construction
embedded option

The attribution to the court is an interpretive reconstruction rather than an express rationale.

Page 21 explains Goldberg's reading of the actual monetary result.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 21–22, that what parties meant to say can differ from what they meant to do. A textualist may see an absolute obligation and a contextualist an implied condition, while a purpose model asks which allocation best fits the parties' objectives and constraints. This is significant because linguistic meaning is an imperfect proxy for transactional purpose. It connects to meaning-purpose gap, textualism, contextualism, risk allocation, party objectives, simulation.

what parties meant to say can differ from what they meant to do. This matters because linguistic meaning is an imperfect proxy for transactional purpose.

meaning-purpose gap
textualism
contextualism
risk allocation
party objectives
simulation

Purpose can be harder to prove and may conflict with third-party reliance on text.

Pages 21-22 draw the chapter's central distinction from Krell.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 22, that meaning interpretation and purpose interpretation are critically distinct even though commentators often blend them. Williston defines interpretation through word sense, Corbin through ideas induced by language, and purposive practice can still be described in semantic terms. This is significant because shared vocabulary can conceal different predictive targets. It connects to Williston, Corbin, word meaning, purposivism, interpretive definitions, conceptual conflation.

meaning interpretation and purpose interpretation are critically distinct even though commentators often blend them. This matters because shared vocabulary can conceal different predictive targets.

Williston
Corbin
word meaning
purposivism
interpretive definitions
conceptual conflation

Words ordinarily express purposes, so overlap is common rather than anomalous.

Page 22 explains why the distinction is often unnoticed.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 22–23, that Bagchi's airport-ride hypothetical shows that relational context can change the authority to depart from literal promises. A close friend may cancel after confidently learning that Bob prefers a new ride, while a paid driver may not infer beyond the agreement. This is significant because the reliability of purpose prediction can shape contractual obligation. It connects to contract and promise, intimates, strangers, airport ride, relational context, purpose inference.

Bagchi's airport-ride hypothetical shows that relational context can change the authority to depart from literal promises. This matters because the reliability of purpose prediction can shape contractual obligation.

contract and promise
intimates
strangers
airport ride
relational context
purpose inference

The hypothetical assumes the friend's confidence and benevolent motivation.

Pages 22-23 present the Alice, Bob, and Charlie comparison.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 23, that intimates can act on evolving purposes because superior knowledge improves predictive accuracy. Alice can draw on Bob's history and preferences, while Charlie has only general experience and a brief interaction. This is significant because permission to override text may depend on the quality of the decisionmaker's model of the other person. It connects to predictive accuracy, relational knowledge, intimates, commercial strangers, contract obligations, information asymmetry.

intimates can act on evolving purposes because superior knowledge improves predictive accuracy. This matters because permission to override text may depend on the quality of the decisionmaker's model of the other person.

predictive accuracy
relational knowledge
intimates
commercial strangers
contract obligations
information asymmetry

Familiarity can also produce bias or misplaced confidence.

Page 23 restates Bagchi's relational distinction in predictive terms.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 23, that purpose interpretation works by simulating how a party would respond to a counterfactual scenario. Alice builds a model of Bob from past interactions and probes it against the changed travel plan. This is significant because simulation identifies a distinct cognitive process rather than a looser form of semantic interpretation. It connects to counterfactual simulation, mental models, party preferences, past interactions, predictive reasoning, purpose.

purpose interpretation works by simulating how a party would respond to a counterfactual scenario. This matters because simulation identifies a distinct cognitive process rather than a looser form of semantic interpretation.

counterfactual simulation
mental models
party preferences
past interactions
predictive reasoning
purpose

Human simulations remain fallible and can be clouded by self-interest.

Page 23 explains the mechanics of Alice's purpose inference.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 23–24, that the reliability of simulation can generate different normative duties. Alice's well-grounded model may justify unilateral adjustment, while Charlie remains bound to less person-dependent criteria. This is significant because epistemic competence and relational position can affect what conduct a promise permits. It connects to normative obligation, epistemic authority, relational contracts, textual fidelity, simulation, contract morality.

the reliability of simulation can generate different normative duties. This matters because epistemic competence and relational position can affect what conduct a promise permits.

normative obligation
epistemic authority
relational contracts
textual fidelity
simulation
contract morality

The chapter does not fully defend this normative implication.

Pages 23-24 connect predictive reliability to differentiated obligations.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 24, that simulation is a humanistic exercise continuous with empathy and ordinary social understanding. People routinely model how others will react to gestures, apologies, persuasion, gifts, and plans. This is significant because technological vocabulary should not obscure the familiar interpersonal capacity underlying purpose inference. It connects to empathy, social cognition, mental simulation, humanism, interpersonal prediction, theory of mind.

simulation is a humanistic exercise continuous with empathy and ordinary social understanding. This matters because technological vocabulary should not obscure the familiar interpersonal capacity underlying purpose inference.

empathy
social cognition
mental simulation
humanism
interpersonal prediction
theory of mind

Empathy does not guarantee accuracy or neutral judgment.

Page 24 connects simulation to everyday interpersonal reasoning.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 24, that theory of mind supplies an innate but unevenly distributed capacity for purpose simulation. The skill develops early, varies among people, and generally improves with knowledge of the person being modeled. This is significant because adjudicative reliability may depend on expertise and informational intimacy rather than doctrine alone. It connects to theory of mind, cognitive development, individual differences, predictive accuracy, adjudication, relational knowledge.

theory of mind supplies an innate but unevenly distributed capacity for purpose simulation. This matters because adjudicative reliability may depend on expertise and informational intimacy rather than doctrine alone.

theory of mind
cognitive development
individual differences
predictive accuracy
adjudication
relational knowledge

The developmental analogy does not establish how accurately judges can simulate litigants.

Page 24 grounds simulation in cognitive theory and individual variation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 24–25, that meaning search and purpose simulation are different models with different objective functions and datasets. Meaning search focuses on how people expressed goals, while simulation focuses on the goals they wanted to express. This is significant because choosing between them changes both the target and the relevant evidence. It connects to objective functions, datasets, meaning search, purpose simulation, evidence selection, model design.

meaning search and purpose simulation are different models with different objective functions and datasets. This matters because choosing between them changes both the target and the relevant evidence.

objective functions
datasets
meaning search
purpose simulation
evidence selection
model design

Text remains evidence of purpose and nonlinguistic facts can sometimes clarify meaning.

Pages 24-25 formalize the distinction in modeling terms.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 25, that purpose should generally receive priority over meaning when the two conflict in ordinary contract law. Words are downstream of transactional aims, and Krell illustrates a court willing to depart from surface meaning. This is significant because contract law is ordinarily concerned with enabling parties' projects rather than worshipping their formulations. It connects to purpose priority, party autonomy, contract goals, Krell v. Henry, purposive interpretation, legal meaning.

purpose should generally receive priority over meaning when the two conflict in ordinary contract law. This matters because contract law is ordinarily concerned with enabling parties' projects rather than worshipping their formulations.

purpose priority
party autonomy
contract goals
Krell v. Henry
purposive interpretation
legal meaning

The author states but does not fully defend the claim and notes special concerns such as insurance and third-party reliance.

Page 25 presents the chapter's central normative claim.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 25, that purpose simulation offers a more coherent way to identify and fill contractual gaps. Literal methods struggle to distinguish intentional silence from an unaddressed contingency and sometimes rely on metaphors such as a yawning void. This is significant because a model of the parties' projects can give concrete content to supplementation. It connects to gap filling, contractual silence, default rules, purpose, incomplete contracts, judicial supplementation.

purpose simulation offers a more coherent way to identify and fill contractual gaps. This matters because a model of the parties' projects can give concrete content to supplementation.

gap filling
contractual silence
default rules
purpose
incomplete contracts
judicial supplementation

Purpose-based gap filling remains vulnerable to inaccurate reconstruction.

Page 25 connects simulation to the problem of identifying contractual gaps.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 25–26, that purpose simulation faces serious objections concerning data, adjudicator burden, third-party reliance, precision, and accuracy. Judges receive conflicting motivated evidence and may differ widely in their reconstructions. This is significant because a normatively attractive target is not useful if institutions cannot estimate it reliably. It connects to information burden, judicial subjectivity, third-party reliance, motivated evidence, precision, accuracy.

purpose simulation faces serious objections concerning data, adjudicator burden, third-party reliance, precision, and accuracy. This matters because a normatively attractive target is not useful if institutions cannot estimate it reliably.

information burden
judicial subjectivity
third-party reliance
motivated evidence
precision
accuracy

The author treats these objections as reasonable rather than dismissing them.

Pages 25-26 state the principal institutional case against simulation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 26, that successful cases show that simulation can sometimes be predictable, information-efficient, sensible, and accurate. Krell, Bagchi's intimate, and commercial cases such as Nanakuli illustrate contexts in which purpose can be inferred without unlimited data. This is significant because institutional objections justify calibration rather than categorical rejection. It connects to Krell v. Henry, Nanakuli, commercial purpose, information efficiency, purpose inference, contextual adjudication.

successful cases show that simulation can sometimes be predictable, information-efficient, sensible, and accurate. This matters because institutional objections justify calibration rather than categorical rejection.

Krell v. Henry
Nanakuli
commercial purpose
information efficiency
purpose inference
contextual adjudication

Illustrative successes do not establish average performance across courts.

Page 26 argues that existing practice keeps simulation viable.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 26, that commercial expertise should affect how heavily adjudicators rely on inferred purpose. Posner's proposal gives experienced judges greater warrant to reason from business objectives. This is significant because method choice can be tailored to decisionmaker competence. It connects to commercial courts, judicial expertise, purpose inference, institutional competence, specialization, predictive accuracy.

commercial expertise should affect how heavily adjudicators rely on inferred purpose. This matters because method choice can be tailored to decisionmaker competence.

commercial courts
judicial expertise
purpose inference
institutional competence
specialization
predictive accuracy

Expertise may create overconfidence or industry capture and requires validation.

Page 26 identifies adjudicator training as one route to improving simulation.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 26, that purpose simulation requires a broader evidentiary dataset than language interpretation. Negotiation choices, commercial setting, party characteristics, recitals, and declarations of purpose help reconstruct objectives, while definitions improve linguistic accuracy. This is significant because contract design can supply different data depending on which model a future court will use. It connects to contract recitals, definition sections, negotiation history, commercial context, purpose evidence, contract design.

purpose simulation requires a broader evidentiary dataset than language interpretation. This matters because contract design can supply different data depending on which model a future court will use.

contract recitals
definition sections
negotiation history
commercial context
purpose evidence
contract design

Parties do not always include relevant data and may strategically shape the record.

Page 26 contrasts evidence useful for modeling purpose with evidence useful for modeling language.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 26–27, that human data-processing limits place an upper bound on purpose-based adjudication. Industry arbitrators may handle thick records in specialized domains, but ordinary judges and juries cannot integrate unlimited information. This is significant because the feasibility of an interpretive ideal depends on adjudicative technology. It connects to bounded rationality, data processing, arbitration, industry expertise, judicial capacity, purpose simulation.

human data-processing limits place an upper bound on purpose-based adjudication. This matters because the feasibility of an interpretive ideal depends on adjudicative technology.

bounded rationality
data processing
arbitration
industry expertise
judicial capacity
purpose simulation

Specialized decisionmakers also have cost, access, and legitimacy tradeoffs.

Pages 26-27 identify the traditional processing constraint.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 27, that language models may weaken the data-processing objection to purposive interpretation. Even a modest role in summarizing and organizing evidence changes how much contextual information adjudicators can realistically use. This is significant because new tools can shift the optimal interpretive method by changing institutional cost constraints. It connects to language models, legal technology, evidence summarization, judicial capacity, purpose simulation, interpretive change.

language models may weaken the data-processing objection to purposive interpretation. This matters because new tools can shift the optimal interpretive method by changing institutional cost constraints.

language models
legal technology
evidence summarization
judicial capacity
purpose simulation
interpretive change

Whether models can reliably learn industries or parties remains uncertain, and summary tools can introduce error.

Page 27 describes the embryonic integration of language models into adjudication.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 27, that advances in AI modeling may move future contract law from linguistic meaning toward party purposes. If subjectivity and processing limits decline, purpose simulation becomes comparatively more feasible and attractive. This is significant because interpretive doctrine is endogenous to available technology. It connects to AI adjudication, purposive interpretation, institutional constraints, legal evolution, modeling parties, contract doctrine.

advances in AI modeling may move future contract law from linguistic meaning toward party purposes. This matters because interpretive doctrine is endogenous to available technology.

AI adjudication
purposive interpretation
institutional constraints
legal evolution
modeling parties
contract doctrine

The claim is a forecast and depends on demonstrable reliability, legitimacy, and safeguards.

Page 27 predicts a technology-driven shift in interpretive emphasis.


Professor Yonathan Arbel claims, in the article “Time and Contract Interpretation: Lessons from Machine Learning” on pages 27, that contract specificity is a function of interpretive technology and interpretive technology responds to drafting. Better purpose inference can reduce the need for exhaustive state-contingent language. This is significant because the cost of writing a good contract depends on what future adjudicators can reconstruct. It connects to contract specificity, interpretive technology, drafting costs, state contingencies, legal services, institutional feedback.

contract specificity is a function of interpretive technology and interpretive technology responds to drafting. This matters because the cost of writing a good contract depends on what future adjudicators can reconstruct.

contract specificity
interpretive technology
drafting costs
state contingencies
legal services
institutional feedback

Reduced specificity can raise uncertainty if the promised interpretive improvement does not materialize.

Page 27 states the reciprocal relationship between contracts and interpretation tools.