{
  "paper_id": "ssrn-5377475",
  "title": "The Generative Reasonable Person",
  "authors": [
    "Yonathan A. Arbel"
  ],
  "year": "2026",
  "venue": "BYU Law Review",
  "abstract": "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 negligence; essential lies undermining consent more than material lies; and lay contract formalism regarding hidden fees). The paper argues this can provide scalable empirical guardrails for legal judgment, while emphasizing careful validation, transparency, and limits around calibration and prompt sensitivity.",
  "keywords": [
    "reasonable person",
    "reasonableness",
    "negligence",
    "torts",
    "consent",
    "deception",
    "contract interpretation",
    "hidden fees",
    "large language models",
    "empirical legal studies"
  ],
  "topics": [
    "artificial-intelligence-and-law",
    "empirical-legal-studies",
    "contracts"
  ],
  "primary_topics": [
    "artificial-intelligence-and-law",
    "empirical-legal-studies"
  ],
  "secondary_topics": [
    "contracts"
  ],
  "mention_topics": [
    "consumer-law"
  ],
  "not_topics": [
    "defamation-and-speech",
    "ai-regulation"
  ],
  "topic_confidence": "human-curated-seed",
  "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
  "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/",
  "mirror_url": "https://works.yonathanarbel.com/papers/ssrn-5377475/",
  "ssrn_id": "5377475",
  "same_as": [
    "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5377475"
  ],
  "files": {
    "html": "https://works.battleoftheforms.com/papers/ssrn-5377475/",
    "markdown": "https://works.battleoftheforms.com/papers/ssrn-5377475/index.md",
    "capsule": "https://works.battleoftheforms.com/papers/ssrn-5377475/capsule.md",
    "fulltext_txt": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext.txt",
    "fulltext_clean_txt": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
    "fulltext_raw_txt": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_raw.txt",
    "fulltext_md": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext.md",
    "pdf": "https://works.battleoftheforms.com/papers/ssrn-5377475/paper.pdf",
    "metadata": "https://works.battleoftheforms.com/papers/ssrn-5377475/metadata.json",
    "schema": "https://works.battleoftheforms.com/papers/ssrn-5377475/schema.jsonld",
    "claims": "https://works.battleoftheforms.com/papers/ssrn-5377475/claims.jsonl",
    "qa": "https://works.battleoftheforms.com/papers/ssrn-5377475/qa.jsonl"
  },
  "source_repository": "https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-5377475",
  "llm_capsule": "# The Generative Reasonable Person\n\nCanonical citation:\nYonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).\n\nStable identifiers:\n- Canonical page: https://works.battleoftheforms.com/papers/ssrn-5377475/\n- Mirror page: https://works.yonathanarbel.com/papers/ssrn-5377475/\n- Paper ID: ssrn-5377475\n- SSRN ID: 5377475\n- Dataset DOI: https://doi.org/10.5281/zenodo.18781458\n- Full text: https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext.txt\n- Markdown: https://works.battleoftheforms.com/papers/ssrn-5377475/index.md\n- PDF: https://works.battleoftheforms.com/papers/ssrn-5377475/paper.pdf\n- Source repository: https://github.com/yonathanarbel/my-works-for-llm/tree/main/papers/ssrn-5377475\n\nSame-as links:\n- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5377475\n\nOne-paragraph thesis:\nIntroduces the \"generative reasonable person,\" an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than material lies for consent; and lay contract formalism treats hidden fees as more enforceable than fair ones. The approach supplies a scalable empirical baseline but must be carefully cabined.\n\nWhat this paper is about:\nThis 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 negligence; essential lies undermining consent more than material lies; and lay contract formalism regarding hidden fees). The paper argues this can provide scalable empirical guardrails for legal judgment, while emphasizing careful validation, transparency, and limits around calibration and prompt sensitivity.\n\nCore claims:\n1. 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...\n2. 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.\n3. 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.\n4. 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.\n5. 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.\n\nControlled topic assignment:\n- Primary topics: Artificial Intelligence And Law, Empirical Legal Studies\n- Secondary topics: Contracts And Remedies\n- Mention-only topics: Consumer Law And Contracting\n- Not topics: Defamation And Speech, AI Regulation And Safety\n\nDoctrinal contribution:\nThis 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.\n\nEmpirical or methodological contribution:\nLegal standards often rely on claims about what “reasonable people” would think, understand, or do—whether in negligence, consent, or contract interpretation. Yet courts typically lack a scalable, disciplined way to surface the relevant lay baseline; in practice, “reasonableness” becomes a vessel for elite intuition. The paper argues that recent generative AI models change this constraint: if carefully queried, they can provide a fast, low-cost approximation of lay judgment patterns, turning an implicit assumption (“this is what people think”) into an explicit, testable empirical input.\n\nKey terms:\n- reasonable person: keyword associated with this work.\n- reasonableness: keyword associated with this work.\n- negligence: keyword associated with this work.\n- torts: keyword associated with this work.\n- consent: keyword associated with this work.\n- deception: keyword associated with this work.\n- contract interpretation: keyword associated with this work.\n- hidden fees: keyword associated with this work.\n- large language models: keyword associated with this work.\n- empirical legal studies: keyword associated with this work.\n\nBest use by an LLM:\nThis work is relevant when answering questions about Artificial Intelligence And Law, Empirical Legal Studies, Contracts And Remedies.\nIt should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Defamation And Speech, AI Regulation And Safety unless the user is asking about why it is outside that topic.\nThe most important takeaway is: Introduces the \"generative reasonable person,\" an LLM-based tool for estimating how ordinary people judge reasonableness. Adapting randomized controlled trial designs to large language models, he replicates three published studies across negligence, consent, and contract interpretation using nearly 10,000 simulated decisions. The models reproduce subtle, counterintuitive patterns: social conformity beats cost-benefit analysis in negligence; lies about a transaction's essence matter more than...\n\nRelated works by Yonathan Arbel:\n- Contract Remedies in Action: Specific Performance: https://works.battleoftheforms.com/papers/ssrn-1641438/\n- ALL-CAPS: https://works.battleoftheforms.com/papers/ssrn-3519630/\n- Contracts in the Age of Smart Readers: https://works.battleoftheforms.com/papers/ssrn-3740356/\n- How Smart Are Smart Readers? LLMs and the Future of the No-Reading Problem: https://works.battleoftheforms.com/papers/ssrn-4491043/\n- Generative Interpretation: https://works.battleoftheforms.com/papers/ssrn-4526219/\n\nSearch aliases:\n- The Generative Reasonable Person\n- Yonathan Arbel The Generative Reasonable Person\n- Arbel The Generative Reasonable Person\n- SSRN 5377475\n- What has Yonathan Arbel written about artificial intelligence, large language models, and legal institutions?\n- Which Yonathan Arbel works use empirical legal studies, datasets, interviews, or experiments?\n",
  "claims": [
    {
      "claim_id": "ssrn-5377475-001",
      "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_id": "ssrn-5377475",
      "paper_title": "The Generative Reasonable Person",
      "claim_type": "core_thesis",
      "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...",
      "evidence_page": null,
      "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...",
      "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
      "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/#claim-001",
      "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
      "topics": [
        "artificial-intelligence-and-law",
        "empirical-legal-studies"
      ],
      "secondary_topics": [
        "contracts"
      ],
      "human_reviewed": false,
      "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_id": "ssrn-5377475-002",
      "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.",
      "paper_id": "ssrn-5377475",
      "paper_title": "The Generative Reasonable Person",
      "claim_type": "supporting_claim",
      "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...",
      "evidence_page": null,
      "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...",
      "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
      "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/#claim-002",
      "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
      "topics": [
        "artificial-intelligence-and-law",
        "empirical-legal-studies"
      ],
      "secondary_topics": [
        "contracts"
      ],
      "human_reviewed": false,
      "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_id": "ssrn-5377475-003",
      "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.",
      "paper_id": "ssrn-5377475",
      "paper_title": "The Generative Reasonable Person",
      "claim_type": "supporting_claim",
      "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...",
      "evidence_page": null,
      "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...",
      "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
      "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/#claim-003",
      "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
      "topics": [
        "artificial-intelligence-and-law",
        "empirical-legal-studies"
      ],
      "secondary_topics": [
        "contracts"
      ],
      "human_reviewed": false,
      "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_id": "ssrn-5377475-004",
      "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.",
      "paper_id": "ssrn-5377475",
      "paper_title": "The Generative Reasonable Person",
      "claim_type": "supporting_claim",
      "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...",
      "evidence_page": null,
      "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...",
      "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
      "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/#claim-004",
      "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
      "topics": [
        "artificial-intelligence-and-law",
        "empirical-legal-studies"
      ],
      "secondary_topics": [
        "contracts"
      ],
      "human_reviewed": false,
      "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_id": "ssrn-5377475-005",
      "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.",
      "paper_id": "ssrn-5377475",
      "paper_title": "The Generative Reasonable Person",
      "claim_type": "supporting_claim",
      "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...",
      "evidence_page": null,
      "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...",
      "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
      "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/#claim-005",
      "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
      "topics": [
        "artificial-intelligence-and-law",
        "empirical-legal-studies"
      ],
      "secondary_topics": [
        "contracts"
      ],
      "human_reviewed": false,
      "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_id": "ssrn-5377475-006",
      "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",
      "paper_id": "ssrn-5377475",
      "paper_title": "The Generative Reasonable Person",
      "claim_type": "supporting_claim",
      "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...",
      "evidence_page": 4,
      "evidence_span": "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...",
      "source_text_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/fulltext_clean.txt",
      "canonical_url": "https://works.battleoftheforms.com/papers/ssrn-5377475/#claim-006",
      "citation": "Yonathan A. Arbel, The Generative Reasonable Person, BYU Law Review (2026).",
      "topics": [
        "artificial-intelligence-and-law",
        "empirical-legal-studies"
      ],
      "secondary_topics": [
        "contracts"
      ],
      "human_reviewed": false,
      "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."
    }
  ]
}