Ethical Attractors in Complex Networks: Observability Gradients, Bayesian Monitoring, and Smooth Actuation

We present a closed-loop framework for stabilising ethically constrained cooperative behaviour in stochastic networked systems under partial observability. The observer samples a subset of edges and records only aggregated edge-type counts, enabling privacy-compatible monitoring. A Bayesian detector maintains a Dirichlet belief over edge-type proportions with exponential forgetting, derives coarse ethical signals (cooperation, discord, entropy) and a volatility estimate, and computes a belief (b(t)) that the active constraint set is satisfied via Monte Carlo sampling. A smooth thermostat controller maps risk (1-b(t)) to a single continuous actuation variable that schedules micro-dynamics parameters (e.g., exploration noise and selection strength) without bang--bang interventions. When meso-level constraints are enabled, an optional topology actuator rewires a bounded number of edges to reduce cross-community dispersion. We provide a reproducible simulation and logging substrate and evaluate control performance and belief calibration along an observability gradient spanning sampling rate, noise, and aggregation level.