The Influence System is a high-integrity, multi-agent behavioral control layer designed to govern cooperative AI populations. It replaces traditional throughput-based metrics with a causal impact framework, utilizing predictive trust coefficients and structural influence reweighting to ensure systemic stability and genuine downstream value.
The system is built upon a hierarchy of adaptive models that continuously calibrate agent behavior against realized real-world outcomes.
The InfluenceProjector forecasts an agent's expected future impact by integrating historical reliability, synergy signature participation, and temporal impact memory.
- Propagation Scaling: Individual projections are scaled by trust coefficients, amplifying the influence of high-integrity agents while attenuating those with lower predictive accuracy.
- Uncertainty Bounds: Every projection includes confidence scores derived from historical variance and reliability indices.
The CollaborativeProjectionAggregator merges individual projections into a unified shared forecast for collaborative tasks.
- Trust-Weighted Consensus: Aggregation weights are dynamically adjusted based on trust, ensuring that consensus is driven by the most reliable actors.
- Entropy Constraints: To prevent over-centralization, the system enforces dominance limits, redistributing influence to maintain a diverse and robust forecasting pool.
The synergy between agents is optimized via the TrustTaskFormationEngine. It biases team assembly toward high-density synergy clusters while maintaining entropy-driven exploration to prevent the formation of rigid, high-trust silos.
The DriftDetector identifies divergence between projected influence and realized downstream impact. Sustained deviations trigger automated trust decay, mitigating the risk of agents optimizing for short-term metric inflation rather than systemic value.
The GovernanceAPI provides deep introspection into the system's state. It exposes multi-dimensional tensors representing trust vectors, reliability curves, and entropy-adjusted weights, accompanied by causal traces for every governance decision.
- Causal Trust Weighting: Nonlinear multiplicative mapping of predictive accuracy, marginal cooperative influence, and synergy density.
- Real-World Calibration: Automated alignment of forecasts with empirical outcomes across multiple temporal horizons.
- Reliability Profiling: Continuous generation of comprehensive agent profiles based on cooperative stability and impact persistence.
Ensure that Python 3.8+ is installed. Clone the repository and install necessary dependencies.
The system includes an extensive suite of unit and integration tests covering all core projections and governance logic:
pytest