Economic Layer is an adaptive economic and behavioral control system designed for agentic ecosystems. It moves beyond traditional throughput-based metrics to govern agents based on real-world causal impact, predictive synergy, and cooperative intelligence.
The system provides a sophisticated governance kernel that monitors, analyzes, and regulates the behavior of autonomous agents within a collaborative environment. By analyzing multi-dimensional impact vectors and structural influence, it ensures system stability, encourages long-term value creation, and prevents exploitative gaming of reward mechanisms.
Provides a transparent interface for inspecting the current state of the ecosystem. It allows for querying system state tensors, retrieving policy mutation history, and inspecting the structural influence weights of individual agents.
Enables risk-free policy testing by simulating the downstream impact of proposed governance parameter shifts using historical data. It predicts how changes will affect trust variance and cooperative intelligence before they are committed.
Identifies agents that are optimizing for short-term metric inflation rather than genuine long-term impact. It compares projected impact against realized outcome deviations to detect and mitigate gaming behavior.
Calculates and amplifies the non-linear value created when agents collaborate. It identifies high-density synergy clusters where the collective impact exceeds the sum of individual contributions.
Dynamically biases task formation toward team compositions that are predicted to yield the highest synergy, while enforcing diversity constraints to prevent system over-centralization.
Adjusts the influence of agent projections on system-wide forecasting based on their historic calibration accuracy and cooperative stability. This ensures that the most reliable and stable contributors have the greatest influence on governance outcomes.
A rule-driven engine that maps ecosystem indicators to automated parameter shifts. It uses versioned transformation rules to dynamically adjust synergy multipliers, trust weights, and temporal scales in response to changing system conditions.
Enforces invariant health constraints across the ecosystem. It monitors state tensors for violations (e.g., excessive influence concentration or declining diversity) and triggers corrective policy mutations to return the system to a healthy regime.
Protects the ecosystem from volatility by analyzing time-series fluctuations in reward allocation and trust weighting. It automatically dampens adjustment amplitudes when instability thresholds are exceeded to preventing runaway feedback loops.
The layer uses multi-dimensional Cooperative State Tensors to categorize the ecosystem into distinct regimes:
- Healthy: High synergy, balanced trust, and diverse collaboration.
- Unstable: High variance in trust or declining predictive accuracy.
- Concentrated: Excessive influence concentration in a small subset of agents.
- Short-Term Bias: Optimization for immediate results at the expense of long-horizon impact.
kernel/: The core logic engines for synergy, behavior, and governance.models/: Standardized data structures for governance signals and system state.tests/: Comprehensive test suite covering behavioral drift, counterfactual simulations, and state transitions.
- Python 3.8+
- pytest (for running the test suite)
Clone the repository and prepare the environment.
git clone <repository-url>
cd economic_layer
pip install pytestTo verify the integrity of the control layer and its sub-modules, run the automated test suite:
pytestEvery governance action, policy mutation, and state transition is designed to be fully auditable. The system reconstructs the causal path behind every metric, providing explanations for why specific influence weights or trust coefficients were assigned.