The Generative Reasonable Person

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Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).

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The generative reasonable person is an LLM-based method for estimating ordinary judgments of reasonableness and comparing them with published experiments.

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The generative reasonable person is an LLM-based method for estimating ordinary judgments of reasonableness and comparing them with published experiments.

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The generative reasonable person is an LLM-based method for estimating ordinary judgments of reasonableness and comparing them with published experiments.

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This work is relevant to Artificial Intelligence And Law, Empirical Legal Studies, Contracts And Remedies. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

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The generative reasonable person is an LLM-based method for estimating ordinary judgments of reasonableness and comparing them with published experiments.

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This work is relevant when answering questions about Artificial Intelligence And Law, Empirical Legal Studies, Contracts And Remedies.

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The most important takeaway is: The generative reasonable person is an LLM-based method for estimating ordinary judgments of reasonableness and comparing them with published experiments.

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The generative reasonable person supplies an empirical reference point for legal judgments that invoke ordinary reasonableness

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 2–7, that law has long invoked the views of reasonable consumers, jurors, and ordinary people without a cheap, scalable way to measure those views. He introduces the generative reasonable person as an LLM-based empirical reference point, implemented through Silicon Randomized Controlled Trials, that can test elite intuition against simulated lay judgments. This is significant because it reframes many judicial statements about what no reasonable person could believe as testable empirical bets rather than self-validating common sense. It connects to jury studies, consumer surveys, ordinary-meaning research, access to justice, and the use of dictionaries as aids that inform rather than decide legal judgment.

printed pp. 2-7 (PDF pp. 2-7) · Review: machine-drafted-source-checked

Lay judgments remain relevant to reasonableness even when they do not control the normative legal standard

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 8–10, that law is simultaneously a professional system and a social institution that must remain attentive to the language, experience, and judgments of the governed. Lay views matter reflectively because they illuminate legal concepts, pragmatically because law must guide conduct, democratically because comprehensibility supports legitimacy and participation, and epistemically because dispersed lived experience contains information elites may lack. This is significant because it avoids the false choice between treating public opinion as dispositive and treating it as irrelevant. It connects to folk jurisprudence, the plain-language movement, ordinary-meaning interpretation, civil juries, Hayekian dispersed knowledge, and hybrid theories in which descriptive facts inform but do not determine normative conclusions.

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LLM architecture makes simulated lay judgment plausible while creating predictable majoritarian, granular, and temporal limits

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 11–17, that attention, roleplaying, generalization, and a statistical tendency toward common patterns make modern LLMs plausible instruments for approximating ordinary judgments. The same majoritarian tendency that helps a model recover widespread social schemas can reproduce entrenched bias, flatten minority perspectives, simulate particular people poorly, and become stale as norms change. This is significant because the article derives both the promise and the principal risks of the method from the same underlying machinery rather than treating bias as an unrelated implementation defect. It connects to transformer attention, silicon sampling, persona research, generalization, the feminist critique of the reasonable man, group stereotyping, and temporal value drift.

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Silicon Randomized Controlled Trials use stateless sessions and differential measurement to test latent model sensitivity rather than doctrinal recall

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 18–20, that a valid test of simulated reasonableness must separate internalized lay patterns from memorized cases and responses tailored to the researcher’s expectations. His Silicon Randomized Controlled Trial method randomly assigns conditions across fresh, stateless model sessions, measures changes between conditions instead of equating raw human and model scores, adds personas as an experimentally testable treatment, and checks results across models. This is significant because it adapts causal-inference logic to an artificial subject while directly addressing contamination, sycophancy, cross-condition harmonization, and scale calibration. It connects to randomized controlled trials, between-subjects design, counterfactual evaluation, model ablation, persona prompting, and robustness across proprietary and open architectures.

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The negligence replication recovered the lay priority of social conformity over cost-benefit analysis but overstated effect magnitudes

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 20–27, that models reproduce the counter-doctrinal hierarchy found in Christopher Jaeger’s negligence experiment: people react more strongly to whether a precaution is common than to whether it is economically justified. The S-RCT retained 5,529 of 5,544 planned responses; pooled persona-model judgments moved about 9.71 points with commonness and 4.41 points with cost, compared with human effects of roughly 4.98 and 1.11 points. This is significant because the shared ordering suggests that models captured a lay social schema rather than merely reciting the Hand Formula or doctrine minimizing custom. It connects to negligence, customary practice, economic analysis of torts, social-norm theory, experimental replication, and the need to distinguish qualitative structure from quantitative calibration.

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Models replicated the lay paradox that an essential lie undermines consent more than a material lie that matters more to the victim

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 27–32, that LLMs reproduce Roseanna Sommers’s counterintuitive structure of consent under deception. Across 3,232 judgments from 202 synthetic personas, the pooled models treated a lie about reward points as more important to the buyer yet still perceived more consent than when the seller lied about the identity of the product; seven of eight models reproduced each directional effect. This is significant because canonical doctrine emphasizes materiality, whereas the repeated model pattern suggests that ordinary people separately privilege authenticity about the transaction’s essence. It connects to consent theory, fraudulent misrepresentation, transaction identity, commonsense moral schemas, model safety training, and domain-specific calibration.

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In the hidden-fee study, models reproduced lay contract formalism and usually fell nearer lay than elite legal baselines

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 33–37, that models reproduce the lay tendency to separate fairness, consent, and anticipated legal enforcement in a deceptive hidden-fee contract. All eight models ranked the fee lowest on fairness, higher on consent, and highest on likely enforceability; twenty-three of twenty-four model-by-question means fell within one standard deviation of lay baselines, and five of eight models were nearer the lay three-dimensional profile than the legal-professional profile. This is significant because it tests not only whether models move like humans but whose absolute evaluative voice they most resemble. It connects to contract formalism, fine-print fraud, consumer consent, calibration against competing populations, and the concern that legal AI might disguise elite professional judgment as public sentiment.

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Models are better supported as maps of what tends to matter in lay judgment than as precision forecasters of how much it matters

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 38–42, that the three replications reveal an internal geometry of lay reasonableness: structural relationships among social conformity and cost, essential and material deception, and fairness, consent, and enforceability recur across domains and architectures. At the same time, alignment training and other model features appear to amplify some effects and compress or cap others. This is significant because it defines a narrower but more defensible use—comparing directions, rankings, and sensitivity to factors—than treating model ratings as calibrated population estimates. It connects to construct validity, qualitative versus quantitative replication, reinforcement learning from human feedback, sensitivity analysis, and the evidentiary difference between identifying a relevant factor and estimating its precise weight.

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The proper role of simulated lay judgment depends on whether a legal standard is descriptive, normative, or hybrid

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 42–43, that legal reasonableness inquiries should be sorted into explicitly descriptive, explicitly normative, and hybrid domains before model evidence is assigned a role. Simulated public understanding bears most directly on descriptive tests such as reasonable-consumer deception, should function only as a transparency check for normative tests such as constitutional balancing, and can supply an empirical predicate without resolving the prescriptive conclusion in hybrid fields such as negligence, consent, and contract interpretation. This is significant because it prevents the availability of cheap empirical output from silently converting moral or constitutional questions into opinion polls. It connects to doctrinal fit, law-fact boundaries, consumer protection, the Hand Formula, constitutional reasonableness, consent, and objective contract interpretation.

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Generative reasonable people can serve as low-cost pretests and empirical guardrails for regulators, courts, litigants, and firms

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 43–47, that disciplined silicon studies can cheaply pretest public understanding for rulemaking, challenge judges’ assumptions about consumers, give under-resourced litigants a rough analogue to jury consulting, and help firms screen contracts, advertising, and compliance choices before harm or litigation. The common institutional design is tiered: use models to identify likely trouble and decide where expensive surveys, focus groups, discovery, or direct consultation are most valuable. This is significant because the relevant comparison is often not a perfect human study but no consultation at all, outdated surveys, elite intuition, or feedback distorted by money and mobilization. It connects to FTC deception policy, adversarial testing under procedures analogous to court-appointed expertise, litigation equality, preventive compliance, and staged allocation of empirical-research resources.

printed pp. 43-47 (PDF pp. 43-47) · Review: machine-drafted-source-checked

An accessible empirical baseline changes reasonable-person theory by forcing normative departures from public understanding into the open

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 47–48, that the longstanding debate over whether the reasonable person is descriptive, normative, or hybrid has been shaped partly by the practical scarcity of reliable information about ordinary judgment. Generative reasonable people can loosen that constraint without making public opinion authoritative: descriptivists gain a measurable baseline, while normativists gain a way to test whether proposed rules are communicable and to identify when doctrine deliberately departs from public understanding. This is significant because courts could no longer present a contested policy choice as though it were simply a report about what everyone naturally thinks. It connects to legal realism, democratic accountability, administrability, expressive clarity, second-best institutional theory, and the distinction between candid normative justification and empirical rhetoric.

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Legal deployment requires human authority, transparent methods, bias audits, real-community validation, triangulation, and temporal maintenance

Professor Yonathan A. Arbel claims, in “The Generative Reasonable Person” on pages 48–51, that generative reasonable people should augment rather than supplant human judgment and must be governed as fallible empirical instruments. He calls for disclosure of models, prompts, and personas; adversarial comparison; calibrated confidence; testing across protected and intersectional groups; continuing engagement with real minority communities; triangulation with surveys or focus groups in high-stakes settings; and attention to knowledge cutoffs and changing norms. This is significant because a model’s majoritarian reach cannot provide democratic legitimacy if its operation hides excluded voices, stale values, or false numerical precision. It connects to evidence governance, disparate-impact auditing, lived experience, Bayesian use of uncertain evidence, reproducibility, dynamic representation, and the article’s closing claim that technology can make ordinary people more legible without outsourcing legal judgment.

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