标题： 硅基理性人：人工智能能预测普通人群如何判断合理性吗？ 显示英文标题

标题： The Silicon Reasonable Person: Can AI Predict How Ordinary People Judge Reasonableness?

摘要： 在日常生活中，人们做出无数合理的判断，这些判断决定了在各种情境下的适当行为。 预测这些判断对法律体系构成挑战，因为法官的直觉可能与更广泛的社会观点不一致。 本文研究大型语言模型（LLMs）是否能够学习识别驱动人类合理判断的模式。 通过在多个法律情境中进行随机对照试验，比较人类和模型的判断，涉及超过10,000次模拟判断，我们证明某些模型不仅捕捉到表面反应，还可能捕捉到其潜在的决策结构。 值得注意的是，这些系统在过失认定中优先考虑社会线索而非经济效率，尽管与教科书中的处理方式相矛盾，但与人类行为相似。 这些发现表明有实际应用：法官可以将直觉与更广泛的趋势进行校准，立法者可以测试政策解释，资源有限的当事人可以预览其论点的接受度。 随着人工智能代理越来越多地自主做出现实世界的决策，了解它们是否已内化可识别的伦理框架，对于预测其行为变得至关重要。 显示英文摘要

摘要： In everyday life, people make countless reasonableness judgments that determine appropriate behavior in various contexts. Predicting these judgments challenges the legal system, as judges' intuitions may not align with broader societal views. This Article investigates whether large language models (LLMs) can learn to identify patterns driving human reasonableness judgments. Using randomized controlled trials comparing humans and models across multiple legal contexts with over 10,000 simulated judgments, we demonstrate that certain models capture not just surface-level responses but potentially their underlying decisional architecture. Strikingly, these systems prioritize social cues over economic efficiency in negligence determinations, mirroring human behavior despite contradicting textbook treatments. These findings suggest practical applications: judges could calibrate intuitions against broader patterns, lawmakers could test policy interpretations, and resource-constrained litigants could preview argument reception. As AI agents increasingly make autonomous real-world decisions, understanding whether they've internalized recognizable ethical frameworks becomes essential for anticipating their behavior.