提出有限道德框架，揭示道德计算的资源权衡机制
Bounded Morality: Defining the Space of Moral Computation
- 用道德广度与深度定义道德计算的约束空间
- 发现伦理理论实为不同资源下的高效策略而非真理之争
- 适合研究AI对齐与认知资源分配的研究者
传统道德认知模型将道德视为固定伦理理论（如义务论、功利主义）的静态规则或价值函数。本文提出「有限道德」框架，形式化有限代理在道德问题上的计算需求。借鉴赫伯特·西蒙的有限理性思想，从两个正交维度刻画道德情境：道德广度（被视作道德相关实体的范围）与道德深度（评估其互动所需推理整合程度）。有限资源导致两维度间不可避免的权衡，构成可实现的道德计算空间。在此空间内，伦理理论表现为适应不同需求场景的局部最优策略，而非道德真理的竞争性解释。该框架给出道德遗憾与受约束下道德进步的形式定义，并表明人工智能的道德对齐取决于道德推理能力的扩展与分配，而非直接模仿人类判断。
原文摘要 · Abstract (English)
Moral cognition has traditionally been modeled as adherence to fixed ethical theories--deontology, consequentialism, virtue ethics--implemented as static rules or value functions. We propose Bounded Morality, a formal framework for analyzing the computational demands of moral problems faced by finite agents. Extending Herbert Simon's notion of bounded rationality, we formalize moral situations along two orthogonal dimensions: moral breadth, the scope of entities treated as morally relevant, and moral depth, the inferential integration required to evaluate their interactions. Limited resources impose an unavoidable tradeoff between these dimensions, defining a feasible space of moral computation. Within this space, ethical theories correspond to locally efficient strategies adapted to different demand regimes rather than competing accounts of moral truth. The framework yields a formal notion of moral regret and moral progress under constraint, and implies that moral alignment in artificial systems depends on the scaling and allocation of moral reasoning capacity rather than on direct imitation of human judgments.
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