Making Meaning Measurable: How to See Coherence in Decision Systems

TL;DR

It proposes a methodology to capture traces of meaning change before performance degradation, making the alignment state of decision systems measurable.

Problem

Organizations or AI systems often drift gradually in actual decision-making while formally pursuing the same goals, but there is a lack of methods to detect this drift in advance. Existing approaches can only recognize alignment problems after failure.

Approach

Introduces a latent variable called Translation Coherence, estimating alignment state through observable patterns in governance documents, allocation rules, and metrics. Uses the Operating Spine framework to trace how intent is translated into action across multiple governance layers.

Results & Contribution

Provides an empirical method to evaluate the durability and reliability of decision rules in public institutions, capital allocation, and AI environments. Although still at an early conceptual stage, it suggests a new direction for measuring alignment.