On Decision-Valued Maps and Representational Dependence
This addresses the problem of ensuring computational consistency across data representations for researchers and practitioners working with reproducible systems.
The paper formalizes decision-valued maps to track how different data representations affect computational outcomes, and introduces DecisionDB infrastructure that logs, audits, and deterministically replays these relationships with exact identifier matching.
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.