Neuroeconomics promises to ground welfare analysis in neural and computational evidence about how people value outcomes, learn from experience and exercise self-control. At the same time, policy and commercial actors increasingly invoke neural data to justify paternalistic regulation, "brain-based" interventions and new welfare measures. This paper asks under what conditions neural data can legitimately inform welfare judgements for policy rather than merely describing behaviour. I develop a non-empirical, model-based framework that links three levels: neural signals, computational decision models and normative welfare criteria. Within an actor-critic reinforcement-learning model, I formalise the inference path from neural activity to latent values and prediction errors and then to welfare claims. I show that neural evidence constrains welfare judgements only when the neural-computational mapping is well validated, the decision model identifies "true" interests versus context-dependent mistakes, and the welfare criterion is explicitly specified and defended. Applying the framework to addiction, neuromarketing and environmental policy, I derive a Neuroeconomic Welfare Inference Checklist for regulators and for designers of NeuroAI systems. The analysis treats brains and artificial agents as value-learning systems while showing that internal reward signals, whether biological or artificial, are computational quantities and cannot be treated as welfare measures without an explicit normative model.

翻译：神经经济学旨在通过神经与计算证据，揭示个体如何评估结果、从经验中学习以及实施自我控制，从而为福利分析提供基础。与此同时，政策制定者与商业机构日益援引神经数据，以支持家长式监管、“基于脑科学”的干预措施及新型福利度量。本文探讨在何种条件下，神经数据能够合法地为政策相关的福利判断提供依据，而非仅用于行为描述。我构建了一个非实证的、基于模型的框架，该框架连接三个层次：神经信号、计算决策模型与规范性福利标准。在行动者-评论家强化学习模型中，我将从神经活动到潜在价值与预测误差、再至福利主张的推理路径形式化。研究表明，仅当神经-计算映射关系得到充分验证、决策模型能区分“真实”利益与情境依赖性错误，且福利标准被明确定义并论证时，神经证据才能对福利判断产生约束作用。通过将该框架应用于成瘾行为、神经营销与环境政策领域，我为监管机构及神经人工智能系统设计者推导出一份《神经经济学福利推断核查清单》。分析将大脑与人工智能体视为价值学习系统，同时指出：无论是生物性还是人工的内部奖励信号，均为计算量值，若缺乏明确的规范性模型，则不可直接视为福利度量标准。