Engineering and Applied Sciences Journal
ISSN: 3067-8005 | DOI: 10.64030/3067-8005
Adrian Erckenbrack
Autonomous systems (AS) are increasingly deployed across transportation, logistics, robotics, critical infrastructure, and other safety-critical environments. Yet a fundamental governance observability challenge persists within contemporary oversight architectures. Existing regulatory, certification, liability, insurance, and safety-management frameworks provide substantial visibility into outcomes including crashes, incidents, recalls, compliance actions, operational restrictions, and safety events while providing comparatively limited visibility into the operational conditions that influence whether effective supervision, intervention, restoration, and oversight remain possible. Consequently, critical governance degradation may remain difficult to detect and evaluate until intervention opportunities have narrowed and AS failures become visible through accidents, recalls, regulatory actions, liability events, or other adverse outcomes. This study introduces the Erckenbrack Recursive Authority Dynamics Model (ERADM), an operational observability architecture designed to measure governance-relevant conditions within increasingly autonomous systems. Unlike frameworks developed primarily from theoretical assumptions, ERADM was constructed through an evidence-to model approach. Historical failures, regulatory interventions, investigations, liability disputes, recalls, operational restrictions, and governance breakdowns were first examined using publicly available records. Recurring governance conditions including intervention effectiveness, supervisory responsiveness, rollback feasibility, adaptation tempo, synchronization effects, infrastructure dependency, operational flexibility, and authority-transition dynamics were then identified across independent cases and formalized as measurable variables. Consequently, the historical evidence preceded the model & variables; the model & variables did not precede the evidence. The framework introduces measurable variables including Authority Elasticity Index (AEI), Override Effectiveness Rate (OER), Rollback Feasibility Time (RFT), Closed-Loop Self-Improvement Interval (CLSI), Operational Space Compression Ratio (OSCR), Synchronization Intensity (S), Infrastructure Coupling (K), Human Supervisory Response Horizon (H), Recursive Leverage Factor (RLF), Adoption-Driven Authority Transfer (ADAT), and the composite Authority Transition Index (Θ). These variables were derived from recurring empirical governance conditions identified through historical failure reconstructions, regulatory interventions, safety investigations, operational disruptions, liability disputes, and corrective actions across multiple domains. In addition to their empirical origins, each variable is linked to statutory authorities, regulatory obligations, certification requirements, enforcement mechanisms, insurance considerations, or liability doctrines that become relevant when the AS underlying condition contributes to operational risk or governance failure. Together, the variables provide a structured mechanism for observing, reconstructing, and evaluating governancerelevant operational conditions that are frequently material to regulatory oversight, safety assurance, liability assessment, and risk management, yet often remain only partially observable within existing reporting architectures. The framework is evaluated through comparative analysis of AS case studies, including Uber ATG, Tesla ADS/FSD, Cruise, and Waymo, together with examination of existing regulatory, certification, insurance, and safety-management frameworks. Results indicate that many governance-relevant conditions remain only partially observable or entirely unobservable within contemporary oversight architectures despite their recurring significance in investigations, regulatory actions, and corrective interventions. This limitation arises not because the conditions are absent, but because existing oversight architectures primarily measure outcomes, compliance, and safety events rather than the operational conditions that precede and influence those outcomes. Consequently, evidence related to intervention effectiveness, supervisory responsiveness, rollback feasibility, infrastructure dependency, synchronization effects, and operational flexibility is often fragmented or not systematically measured. Using an inductive comparative reconstruction methodology, publicly available investigative, regulatory, operational, and liability records were analyzed to identify recurring governance conditions across independent cases. ERADM variables were created and retained only when recurring governance conditions were repeatedly observed across independent cases and demonstrated operational relevance together with regulatory, insurance, or liability significance. Reconstruction analyses demonstrate that several ERADM variables can be estimated from publicly available evidence, supporting governance observability as a measurable systems property and establishing an evidence-to-model methodology in which recurring historical evidence informed variable selection before framework construction. The principal contribution of ERADM is the introduction of an operational observability and measurement layer through which governance-relevant conditions can be systematically observed, reconstructed, and empirically assessed leading up to and following a catastrophic or deadly incident. Results indicate that many governance-relevant conditions remain only partially observable or entirely unobservable within contemporary oversight architectures despite their recurring significance in investigations, regulatory actions, and corrective interventions. Reconstruction analyses nevertheless demonstrate that several ERADM variables can be estimated using publicly available evidence, supporting governance observability as a measurable systems property. Finally, ERADM is designed to operate alongside established systems-safety, risk-management, and governance frameworks—including STAMP/CAST, the NIST AI Risk Management Framework, and Safety Management Systems (SMS) by providing a measurable observability layer through which governance-relevant operational conditions can be observed, reconstructed, and evaluated. The framework's principal novelty therefore lies in treating governance observability itself as a measurable systems property and in operationalizing that property through a structured set of empirically derived variables.