关于决策值映射与表征依赖
On Decision-Valued Maps and Representational Dependence
AI总结：
本文提出决策值映射的概念，通过DecisionDB基础设施记录和审计不同数据表示与决策结果的关系，将决策重用视为可机械检查的条件。
摘要：
将计算引擎应用于同一数据的不同表示可以产生不同的离散结果，某些表示保持结果不变，而其他表示则完全改变结果。决策值映射记录哪些表示保持结果不变，哪些改变结果，将每个声明的表示家族成员与它产生的离散结果关联起来。本文正式定义了决策值映射，并描述了DecisionDB，这是一个基础设施，通过从写入一次形式中存储的内容和制品计算出的标识符来记录、回放和审计这些关系。确定性回放可以从存储的制品中精确恢复每个记录的决策标识符，所有三个标识字段都与持久化的值匹配。该贡献将表示空间划分为持久区域和边界，并将决策重用视为可机械检查的条件。
英文摘要：
A computational engine applied to different representations of the same data can produce different discrete outcomes, with some representations preserving the result and others changing it entirely. A decision-valued map records which representations preserve the outcome and which change it, associating each member of a declared representation family with the discrete result it produces. This paper formalizes decision-valued maps and describes DecisionDB, an infrastructure that logs, replays and audits these relationships using identifiers computed from content and artifacts stored in write-once form. Deterministic replay recovers each recorded decision identifier exactly from stored artifacts, with all three identifying fields matching their persisted values. The contribution partitions representation space into persistence regions and boundaries, and treats decision reuse as a mechanically checkable condition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
