让理性决策框架支持价值观的动态优化，揭示价值迭代如何让博弈变共赢。
Learning the Value of Value Learning
- 用统一框架同时建模认知与价值的双重修正
- 证明价值信息能带来帕累托改进，使零和博弈转为正和
- 适合研究伦理决策、多智能体协作的学者参考
标准决策框架处理事实不确定性，但假设选项与价值固定不变。本文将Jeffrey-Bolker框架扩展至建模价值的精炼，并证明了价值信息的价值定理。在多智能体场景中，我们证明相互价值精炼将特征性地把零和博弈转化为正和互动，并在纳什讨价还价中实现帕累托改进。这些结果表明，理性选择框架可扩展以建模价值精炼。通过在单一形式系统下统一认知与价值精炼，我们拓宽了理性选择的理论基础，并阐明了伦理反思的规范地位。
原文摘要 · Abstract (English)
Standard decision frameworks address uncertainty about facts but assume fixed options and values. We extend the Jeffrey-Bolker framework to model refinements in values and prove a value-of-information theorem for axiological refinement. In multi-agent settings, we establish that mutual refinement will characteristically transform zero-sum games into positive-sum interactions and yield Pareto-improvements in Nash bargaining. These results show that a framework of rational choice can be extended to model value refinement. By unifying epistemic and axiological refinement under a single formalism, we broaden the conceptual foundations of rational choice and illuminate the normative status of ethical deliberation.
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