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Synapse
February 8, 2026Open Access

Part 1 – How Decision Systems Learn What Matters: A Constrained Architecture for Purpose-Aligned Governance

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Authors

RMRobin Edgard Ulrik Mertens

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Overview

The framework presents an architectural model for decision systems learning in complex environments, suggesting implications for governance.

Key Points

  • This paper aims to define a structured architecture for understanding how decision systems learn and adapt over time.
  • Introduces the AI-Augmented Impact Frames architecture for studying decision systems.
  • Defines the Operating Spine as a structured learning loop for decision-making processes.
  • Utilizes Item Response Theory (IRT) for psychometrically grounded longitudinal measurement.
  • Establishes the decision system as the primary unit of analysis instead of individual decisions.
  • Clarifies the role of AI as interpretive support without decision authority.
  • Outlines criteria for empirical evaluation of decision-system learning under high uncertainty.

Cite This Study

Robin Edgard Ulrik Mertens (2026) studied this question.

synapsesocial.com/papers/698827f00fc35cd7a8846f0ehttps://doi.org/10.5281/zenodo.18505216
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