On Decision-Valued Maps and Representational Dependence

2026-02-13 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

Decision-valued maps formalize how different representations of the same data can lead to varying discrete outcomes when processed by a computational engine. This framework records which representations preserve an outcome and which alter it, associating each representation within a defined family with its produced discrete result. The paper introduces DecisionDB, an infrastructure that logs, replays, and audits these relationships using content-addressed identifiers stored in write-once form. DecisionDB supports representational sweeps, replay verification, and post-hoc auditing of the full provenance chain. An empirical demonstration using a graph routing problem showed that one representation parameter preserved decision identity across its tested range (0.5 to 1.0), while another induced a discrete identity change between values 0.25 and 0.5, highlighting the sensitivity of outcomes to representational choices.

Key takeaway

For AI Scientists evaluating the robustness of analytical pipelines, understanding representational dependence is crucial. Decision-valued maps and DecisionDB provide a concrete framework to systematically test how discrete outcomes change or persist under varying input representations. You should consider integrating this approach to identify "persistence regions" and "fractures" in your models, ensuring that critical decisions are stable across expected representational variations before deployment.

Key insights

Decision-valued maps reveal how representational choices impact discrete outcomes, enabling auditable decision reuse.

Principles

• Decision identity can persist or change with representation.
• Content-addressing ensures deterministic, auditable provenance.

Method

DecisionDB materializes decision-valued maps by systematically varying representations, executing an engine, and applying an equivalence policy to reduce raw output to a discrete decision identity, all logged as immutable, content-addressed artifacts.

In practice

• Use DecisionDB to map outcome stability across representations.
• Define equivalence policies for discrete outcome identification.
• Employ replay verification to confirm provenance chain integrity.

Topics

• Decision-Valued Maps
• Representational Dependence
• DecisionDB
• Provenance Systems
• ML Reproducibility

Best for: AI Scientist, AI Researcher, Research Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.