We formalize Prescriptive Artificial Intelligence as a distinct paradigm for human-AI decision collaboration in high-stakes environments. Unlike predictive systems optimized for outcome accuracy, prescriptive systems are designed to recommend and audit human decisions under uncertainty, providing normative guidance while preserving human agency and accountability. We introduce four domain-independent axioms characterizing prescriptive systems and prove fundamental separation results. Central among these is the Imitation Incompleteness theorem, which establishes that supervised learning from historical decisions cannot correct systematic decision biases in the absence of external normative signals. Consequently, performance in decision imitation is bounded by a structural bias term epsilon_bias rather than the statistical learning rate O(1/sqrt(n)). This result formalizes the empirically observed accuracy ceiling in human decision imitation tasks and provides a principled criterion for when automation should be replaced by epistemic auditing. We demonstrate the computational realizability of the framework through an interpretable fuzzy inference system, applied as a stress test in elite soccer decision-making, where it reveals systematic decision latency and risk states obscured by outcome and status quo biases. The proposed framework establishes Prescriptive AI as a general, realizable class of decision-support systems applicable across safety-critical domains in which interpretability, contestability, and normative alignment are essential.

翻译：本文将规范性人工智能（Prescriptive Artificial Intelligence）形式化为高风险环境中人机决策协作的独特范式。与以结果准确性为优化目标的预测系统不同，规范性系统旨在不确定性条件下对人类决策进行推荐与审计，在保留人类自主权与问责制的同时提供规范性指导。我们提出刻画规范性系统的四个领域无关公理，并证明其基础分离结果。其中核心为"模仿不完备定理"：该定理证明，在缺乏外部规范性信号的情况下，基于历史决策的监督学习无法修正系统性决策偏差。因此，决策模仿的性能上限由结构性偏差项ε_bias决定，而非统计学习速率O(1/√n)。该结果形式化了人类决策模仿任务中经验观测到的准确率上限，并为自动化应何时被认知审计所取代提供了原则性判据。我们通过可解释模糊推理系统证明了该框架的计算可实现性，并将其应用于精英足球决策中的压力测试。实验揭示出被结果偏差与现状偏差所掩盖的系统性决策延迟与风险状态。所提出的框架将规范性AI确立为一类通用、可实现且可部署于安全关键领域的决策支持系统——此类系统的核心需求是可解释性、可争议性与规范性对齐。