claim: This Article introduces the “generative reasonable person,” an LLM-based method for estimating how ordinary people judge reasonableness. Using Silicon Randomized Controlled Trials (S-RCTs), it replicates three published studies across negligence, consent under deception, and contract interpretation with nearly 10,000 simulated decisions. Models reproduce subtle, counterintuitive lay patterns that diverge from doctrinal expectations (e.g., social conformity over cost–benefit analysis in...

paper_title: The Generative Reasonable Person

evidence_quote: [p. 1] The Generative Reasonable Person Yonathan A. Arbel This Article introduces the generative reasonable person, a new tool for estimating how ordinary people judge reasonableness. As claims about AI capabilities often outpace evidence, the Article proceeds empirically: adapting randomized controlled trials to large language models, it replicates three published studies of lay judgment across negligence, consent, and contract interpretation, drawing on nearly 10,000 simulated decisions. The findings reveal that modelscan replicate subtle patterns that run counter to textbook treatment.Like human subjects, models prioritize social conformity over cos-tbenefit analysis when assessing...

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

confidence: machine-linked

limitations: Machine-linked claim. Use the evidence quote and PDF before treating it as a quotation or as a complete statement of the paper's position.


claim: The paper proposes a "generative reasonable person" to make lay reasonableness judgments observable at scale. Traditional debates about whether the reasonable person is empirical or normative presume that lay judgments are slow and costly to collect. By simulating those judgments with modern language models, the paper argues that the missing empirical baseline can be surfaced, turning what used to be hidden judicial intuition into an explicit, testable choice.

evidence_span: [p. 1] The Generative Reasonable Person Yonathan A. Arbel This Article introduces the generative reasonable person, a new tool for estimating how ordinary people judge reasonableness. As claims about AI capabilities often outpace evidence, the Article proceeds empirically: adapting randomized controlled trials to large language models, it replicates three published studies of lay judgment across negligence, consent, and contract interpretation, drawing on nearly 10,000 simulated decisions. The findings reveal that modelscan replicate subtle patterns that run counter to textbook treatment.Like human subjects, models prioritize social conformity over cos-tbenefit analysis when assessing...


claim: The study adapts randomized controlled trial designs to LLMs and replicates three published experiments spanning negligence, consent, and contract interpretation. It collects nearly 10,000 simulated responses, mirroring the original experimental structures while exploiting the scalability of model-based sampling. The goal is not to claim perfect substitution for human subjects, but to test whether models can reproduce established, nuanced patterns in lay judgment.


claim: In the negligence replication, models prioritize social conformity over cost-benefit analysis, a result that runs against textbook treatments of negligence doctrine. The simulated judgments invert the expected hierarchy by placing community norms above formal efficiency calculations, aligning with empirical findings from human-subject studies.


claim: In contract interpretation, the models reflect a form of lay formalism. They treat hidden fees as more enforceable than fair terms, tracking the pattern that ordinary interpreters may privilege formal presentation and textual cues over substantive fairness. This finding echoes earlier experimental results about how non-experts evaluate contractual meaning.


claim: Reasonable person standard; generative reasonable person; silicon sampling; Silicon Randomized Controlled Trials (S-RCTs / s-RCTs); stateless LLM sessions; persona prompting; negligence; Hand formula; custom vs efficiency; social norms; deception; consent; material lie vs essential lie; contract interpretation; hidden fees; fairness vs consent vs enforceability; lay formalism; simulated juries; calibration; judicial intuition; regulatory testing; empirical guardrails

evidence_quote: guessing by precisely the elitecsriticized for being most removed from ordinary life.11 This Article providessuch a tool.It develops a methodology for eliciting reasonableness judgments from large language mode(lsLLMs) , called “Silicon Randomized Controlled Trials ” (s-RCT) .12 The approach treats independent model sessions as experimental subjects, leveraging their statelessness to enable controlled comparisons that isolate lay reasoning patterns from doctrinal recall or model sycophancy. It uses this methodology to successfully replicate with LLMs the results of human subject experiments. Across three replications involving over 10,000 simulated judgments, leading models have...