System Coherence Monitor
Abstract
#!/usr/bin/env python3"""SCM v9.0 — System Coherence Monitor Models a Collective Predictive System (CPS) operating within anenvironment of other CPSs. All interactions are treated asinformation exchange between predictive systems on a sharedchannel space. Architecture: 13 state axes — coherence vector of the CPS (weighted, sum=1) 2 coupling parameters — OBS_cred, COH_cred (meta-state) The 13 axes describe the CURRENT state of the system. The 2 coupling parameters describe the system's ACCUMULATED trust / credibility — they carry historical hysteresis and act as a multiplicative factor on kappa (not as extra dimensions). D = theta(13 axes) * kappa_base(13 axes) * cred_mult(2 coupling) Features: - 6-channel signal model with credibility gate - Cascade propagation with system inertia - B2 structural drift - Coupled-system feedback Structural constants from Q6 graph geometry: theta_crit = 51/64 = 0.796875 theta_alert = 0.65 D_crit = 1.50 Time resolution: quarterly (dt = 0.25 year).""" import mathfrom typing import Dict, List, Tuple # =========================================================# 0) THRESHOLDS / CONSTANTS# ========================================================= THETA_CRIT = 51 / 64 # 0.796875THETA_ALERT = 0.65D_CRIT = 1.50 # =========================================================# 1) STATE AXES — 13-dimensional coherence vector (sum = 1.00)## Each axis measures a distinct capacity or alignment.# These are the CURRENT state of the CPS.# ========================================================= METRICS: List[Tuple[str, float]] = [ ("observation_fidelity", 0.10), ("institutional_reliability", 0.12), ("actuator_capacity", 0.07), ("inter_system_stability", 0.08), ("intra_system_alignment", 0.11), ("obligation_load", 0.08), ("exchange_trust", 0.07), ("reserve_weight", 0.06), ("endogenous_leverage", 0.05), ("throughput_capacity", 0.09), ("supply_autonomy", 0.08), ("energy_autonomy", 0.08), ("social_cohesion", 0.01),]W: Dict[str, float] = {k: w for k, w in METRICS}WSUM = sum(W.values()) # =========================================================# 2) DRIFT ALPHA (B2 degradation rates per axis)# ========================================================= DRIFT_ALPHA: Dict[str, float] = { "observation_fidelity": 1.00, "institutional_reliability": 0.85, "actuator_capacity": 0.70, "inter_system_stability": 0.60, "intra_system_alignment": 0.75, "obligation_load": 0.90, "exchange_trust": 0.95, "reserve_weight": 0.60, "endogenous_leverage": 0.80, "throughput_capacity": 0.75, "supply_autonomy": 0.45, "energy_autonomy": 0.35, "social_cohesion": 0.70,} # =========================================================# 3) SIGNAL LIBRARY (6-channel: OBS, ACT, CAP, COH, SIG, FIN)## Every perturbation is a signal received or emitted by the CPS.# OBS = observation channel (fidelity of system's world-model)# ACT = actuator channel (system's ability to act on decisions)# CAP = capacity channel (resource throughput)# COH = coherence/acceptance channel (systemic trust)# SIG = inter/intra-system signaling channel# FIN = metabolic channel (flow of exchange medium)# ========================================================= SIGNAL_LIB = { "CAPACITY_SHOCK": {"OBS": -0.05, "ACT": -0.05, "CAP": -0.35, "COH": -0.10, "SIG": 0.00, "FIN": -0.15}, "THROUGHPUT_STRESS": {"OBS": -0.05, "ACT": -0.20, "CAP": -0.30, "COH": -0.10, "SIG": 0.00, "FIN": -0.25}, "ENDOGENOUS_PERTURBATION": {"OBS": -0.15, "ACT": -0.30, "CAP": -0.15, "COH": -0.10, "SIG_INT": -0.30, "FIN": 0.00}, "EXOGENOUS_PERTURBATION": {"OBS": -0.05, "ACT": -0.20, "CAP": -0.25, "COH": -0.15, "SIG_EXT": -0.25, "FIN": -0.05}, "CHANNEL_NOISE": {"OBS": -0.35, "ACT": -0.05, "CAP": -0.05, "COH": -0.10, "SIG": 0.00, "FIN": -0.10}, "COHERENCE_RESTORE": {"OBS": 0.10, "ACT": 0.05, "CAP": 0.00, "COH": 0.25, "SIG": 0.00, "FIN": 0.00}, "CAPACITY_GAIN": {"OBS": 0.05, "ACT": 0.15, "CAP": 0.25, "COH": 0.05, "SIG": 0.00, "FIN": 0.05}, "COUPLED_STABILIZATION": {"OBS": 0.08, "ACT": 0.10, "CAP": 0.20, "COH": 0.05, "SIG_EXT": 0.30, "FIN": 0.10}, "COUPLED_DESTABILIZATION": {"OBS": -0.10, "ACT": -0.15, "CAP": -0.30, "COH": -0.15, "SIG_EXT": -0.35, "FIN": -0.20}, "REMOTE_LOAD_RETRACTION": { "OBS": 0.05, "ACT": 0.10, "CAP": 0.30, "COH": 0.05, "SIG_EXT": 0.25, "FIN": 0.20, }, "BOUNDARY_TRANSFER_EXCHANGE_ANCHOR": { "OBS": 0.10, "ACT": 0.05, "CAP": 0.15, "COH": 0.10, "SIG_EXT": 0.35, "FIN": 0.25, }, "SECONDARY_DOMAIN_DECOUPLING": { "OBS": 0.08, "ACT": 0.15, "CAP": 0.35, "COH": 0.15, "SIG_EXT": 0.20, "FIN": 0.25, }, "CONSTRAINT_REMOVAL_CHANNEL_RESET": { "OBS": 0.15, "ACT": 0.05, "CAP": 0.10, "COH": 0.30, "SIG_EXT": 0.30, "FIN": 0.10, }, "EXCHANGE_FLOW_RESTORATION": { "OBS": 0.10, "ACT": 0.10, "CAP": 0.25, "COH": 0.10, "SIG_EXT": 0.15, "FIN": 0.30, }, "TERTIARY_DOMAIN_DECOUPLING": { "OBS": 0.05, "ACT": 0.10, "CAP": 0.20, "COH": 0.10, "SIG_EXT": 0.15, "FIN": 0.15, }, "ENDOGENOUS_INTEGRITY_RECALIBRATION": { "OBS": 0.30, "ACT": 0.15, "CAP": 0.00, "COH": 0.35, "SIG_INT": 0.20, "FIN": 0.00, }, "OBSERVER_PROTOCOL_FRAMEWORK": { "OBS": 0.25, "ACT": 0.10, "CAP": 0.05, "COH": 0.20, "SIG_INT": 0.15, "FIN": 0.00, }, "COUPLED_FEEDBACK_POS": { "OBS": 0.05, "ACT": 0.03, "CAP": 0.05, "COH": 0.05, "SIG_EXT": 0.10, "FIN": 0.05, }, "COUPLED_FEEDBACK_NEG": { "OBS": -0.05, "ACT": -0.03, "CAP": -0.05, "COH": -0.05, "SIG_EXT": -0.10, "FIN": -0.05, },} # =========================================================# 4) CASCADE RULES (threshold-triggered propagation)## Cascades include system inertia: the degradation scales# with how far the triggering axis is below its threshold.# Near-threshold crossings produce weak cascades;# deep violations produce full-strength cascades.# ========================================================= CASCADES: List[Tuple[str, str, float, str, float]] = [ ("throughput_capacity", "
Fields of Study & Subjects
Nonlinear Dynamics and Pattern Formation
Neural Networks and Reservoir Computing
Protein Structure and Dynamics
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 10 March 2026
- Language
- EN
- Document type
- Book