Part 2 – Why Strong Governance Drifts: Translation Drift in Institutional Decision Systems

Programme Context

This preprint forms part of the research programme The Coherence Problem: How Institutions Learn, Drift, and Realign, which studies institutional decision systems as interpretive learning systems operating under conditions of complexity, scale, and delayed feedback.

The programme integrates four complementary components:
(1) architecture — the formal structure of decision-system learning,
(2) mechanism — translation drift as a structural source of misalignment,
(3) measurement — methods for observing translation coherence, and
(4) design — governance as interpretive maintenance in AI-mediated environments.

Together, the papers examine how organisations determine what matters, how meaning becomes encoded in governance artefacts, how translation drift arises as intent moves across governance layers, and how institutions can observe, maintain, and deliberately realign interpretive coherence over time.

Supporting materials, working documents, and programme structure are available via the Open Science Framework (OSF):  https://osf.io/9cvky/

Description (Part 2)

This preprint forms part of the research programme The Coherence Problem: How Institutions Learn, Drift, and Realign, which studies institutional decision systems as interpretive learning systems operating under conditions of complexity and delayed feedback.

This paper introduces translation drift — the gradual loss of interpretive coherence as strategic intent is translated across layered governance systems. It explains why capable institutions can experience strategic drift not despite strong governance, but because of it, as layered interpretation, local optimisation, and delayed feedback allow small shifts in meaning to accumulate over time.

The paper reframes organisations as interpretive learning systems and shifts the unit of analysis from individual decisions to the translations through which meaning moves. By identifying translation drift as a structural mechanism rather than a case-specific failure, it provides a theoretical foundation for analysing long-horizon decision systems and institutional strategy.

Relevant for organisational learning, governance design, portfolio management, institutional strategy, and complex decision environments.

Version 1.01 (Feb 2026)
Minor theoretical positioning refinements, boundary clarification, formalisation schematic, and language precision updates. No change to core argument. This version supersedes V1.0 and introduces no change to the core theoretical claim.

Version 1.02 update: This version adds an explicit cross-reference to the AI-Augmented Impact Frames architectural paper to clarify the conceptual positioning of this article within the broader research programme. No arguments, definitions, figures, or claims have been changed. The revision improves scholarly traceability and programme coherence only.

Version 2.00: This release consolidates the manuscript within the full research programme structure. Cross-paper terminology has been harmonised, the unit-of-analysis statement has been standardised across the series, and reference architecture has been aligned. No changes have been made to the formal decision-learning architecture, measurement logic, boundary conditions, or theoretical claims.