Robust decision-making requires compression. A system that forms a rich support state cannot usually preserve its full structure at the point of action. It must retain only those distinctions needed to act, verify, abstain, or defer under the current consequence geometry. This paper formalizes support sufficiency as action-sufficient compression. Let $H$ denote a full support state, $\mathcal{A}$ a finite action set, and $Z$ a consequence geometry specifying payoff structure. For fixed $Z$, the coarsest exactly action-sufficient compression is the quotient of support space by policy equivalence. Two support states may be merged exactly when they require the same optimal action. This clarifies why content-only and scalar-confidence-only arbitration fail whenever their induced partitions cross action boundaries. Approximate sufficiency is then defined by bounded expected policy regret. In the finite single-cycle setting, this yields a rate-regret problem with source $H$, reproduction alphabet $\mathcal{A}$, and distortion given by consequence-sensitive regret. The optimal stochastic action channel inherits the standard rate-distortion Gibbs form, applied here to support states with regret distortion. The contribution is interpretive: action adequacy is distinguished from reconstruction fidelity, information-bottleneck prediction, and rational inattention. Robust single-cycle arbitration does not require preserving all support, but it does require preserving the distinctions that consequence geometry makes action-relevant.

翻译：鲁棒决策需要压缩。形成一个丰富支持状态的系统通常无法在行动点保留其完整结构。它必须只保留在当前后果几何结构下用于行动、验证、放弃或延迟所需的那些区分。本文形式化了支持充分性作为动作充分压缩的概念。设 $H$ 表示一个完整支持状态，$\mathcal{A}$ 是一个有限动作集，$Z$ 是一个规定收益结构的后果几何结构。对于固定的 $Z$，最精确的动作充分压缩是基于策略等价性的支持空间的商空间。两个支持状态仅当它们需要相同的最优动作时才可合并。这阐明了为何仅基于内容或仅基于标量置信度的仲裁会在其诱导的划分跨越动作边界时失败。近似充分性随后由有界期望策略遗憾定义。在有限单周期设置中，这产生了一个源为 $H$、重构字母表为 $\mathcal{A}$、失真由后果敏感遗憾定义的概率-遗憾问题。最优随机动作通道继承了标准率-失真吉布斯形式，此处应用于具有遗憾失真的支持状态。贡献在于解读层面：动作充分性区别于重构保真度、信息瓶颈预测和理性忽视。鲁棒单周期仲裁不需要保留所有支持信息，但需要保留后果几何结构所决定的对动作至关重要的区分。