Sean Mansfield
Purpose This project defines domain-specific falsification tests for the Systemic Dynamic Trajectory Law (SDTL), focusing on three fields where early failure detection is both measurable and mission-critical: Control theory and engineered systems Medicine and physiological regulation High-reliability and regulated industries The goal is not to defend SDTL, but to make it easy to disprove under the strongest plausible conditions. Core Claim (test target) Claim: In regulated systems, a sustained loss of maneuverability produces a predictable collapse of system trajectories that is detectable before observable failure. Unified Operational Definitions (observer-independent) System: An entity maintaining state within constraints via feedback or control. Regulation: Any mechanism (controller, protocol, physiology, policy) that acts to keep outputs within bounds. Maneuverability: The system’s reachable set in state space over a defined horizon. Sustained loss of maneuverability: A persistent contraction of the reachable set across consecutive windows. Trajectory collapse: A transition from multi-path control to path locking or narrowing that precedes constraint violation. Domain-Specific Falsification Targets 1️⃣ Control Theory / Engineered Systems Examples: Feedback-controlled mechanical systems Power grids HVAC / industrial automation Autonomous or semi-autonomous controllers Testable quantities: Controllability rank or condition number Reachable set volume (or proxy) Control authority margins Actuator saturation frequency Falsification conditions: System fails without prior contraction of reachable set Sustained maneuverability loss occurs with no trajectory narrowing Control restoration does not restore trajectory flexibility 2️⃣ Medicine / Physiological Regulation Examples: Cardiovascular instability Respiratory failure Sepsis progression Neurodegenerative decline Testable quantities: Variability metrics (HRV, respiratory variability) Adaptive range under perturbation Response latency to intervention Multivariate state flexibility Falsification conditions: Acute failure with no detectable decline in adaptive range Sustained decline without subsequent collapse Recovery without restored maneuverability 3️⃣ High-Reliability / Regulated Industries Examples: Aviation safety Financial institutions Infrastructure systems Healthcare delivery systems Testable quantities: Policy flexibility Decision latency Option-space compression Escalation thresholds Falsification conditions: Collapse without prior option-space narrowing False positives where narrowing is common but failure rare Independent observers disagree on maneuverability trends Decisive Falsification Criteria (SDTL fails if ANY occur) False negative: Failure occurs with no prior sustained maneuverability loss False positive: Maneuverability loss occurs repeatedly without collapse Non-specificity: Same signature appears in stable systems at similar rates Observer dependence: Independent analysts cannot agree on scoring Intervention contradiction: Restoring maneuverability does not alter trajectory Expected Boundary Conditions (where SDTL should fail) Systems dominated by exogenous shocks Systems without meaningful regulation Discontinuous redesigns or resets Unobservable internal states These are treated as scope limits, not model successes. Minimum Viable Falsification Study For each system: Define state variables and constraints Choose maneuverability metrics (domain-appropriate) Define lead-time window Score trajectory narrowing Compare against failure timestamps All protocols will be preregistered. Outputs Domain-specific falsification protocols Scoring rubrics and datasets False positive / false negative rates Public revision or withdrawal if falsified Commitment If SDTL fails under these criteria, the failure will be preserved and published. No post-hoc reinterpretation.