Simulation Layer

A high-fidelity simulation framework for evaluating governance policies and cooperative dynamics in multi-agent systems. This project provides tools for modeling, testing, and optimizing policy impact across various temporal horizons and systemic metrics.

Overview

The Simulation Layer serves as a sandbox for projecting the downstream effects of governance rules. It enables system designers to evaluate how changes in incentives, constraints, and collaboration protocols influence collective intelligence, trust networks, and synergy density. By utilizing counterfactual analysis and horizon sensitivity testing, the framework identifies resilient governance configurations that balance immediate performance with long-term stability.

Core Features

• Policy Modeling: Define executable governance rules with specific scope boundaries, influence transformations, and temporal decay logic.
• Causal Impact Propagation: Forecast how policy-induced shifts ripple through the system's cooperative state over multiple future horizons.
• Counterfactual Comparison: Run parallel simulations to compute the delta impact between baseline and modified governance structures.
• Horizon Sensitivity: Evaluate policy resilience across short-term, mid-term, and long-term projections to prevent over-optimization for immediate gains.
• Intelligence Evolution: Model the impact of policies on learning velocity, calibration stability, and cooperative adaptation.
• Entropy and Diversity Management: Monitor influence concentration to ensure system diversity and prevent structural fragility.
• Negotiation Dynamics: Simulate agent-level interactions and agreement forming under varying policy constraints.

Architecture

The project is structured into three primary domains:

Models (src/models/)

Contains the core data schemas and state representations.

• policy.py: Defines the PolicySchema, including constraints, transformations, and metadata.
• cooperative_state_snapshot.py: Represents the system state at a specific point in time, encompassing synergy matrices and trust vectors.
• intelligence_evolution_model.py: Tracks and models the growth of collective capabilities.

Simulation (src/simulation/)

Provides the engines for state projection and stress testing.

• policy_simulator.py: Coordinates the application of policies to the system state.
• counterfactual_policy_comparator.py: Analyzes the performance deltas of candidate policies.
• horizon_sensitivity_engine.py: Tests the persistence and decay of policy effects over time.
• entropy_stress_test.py: Measures influence variance and diversity constraints.
• negotiation_dynamics_simulator.py: Models tactical agent interactions.

Optimization (src/optimization/)

Implements algorithms for policy improvement.

• policy_optimizer.py: Handles multi-objective optimization to identify Pareto-optimal governance configurations.

Key Metrics

The framework evaluates system health using the following primary signals:

• Synergy Density: The intensity and structural depth of agent collaborations.
• Cooperative Intelligence Amplification: The rate of growth in the system's collective problem-solving capacity.
• Trust-Weighted Forecast Adjustment: Adjusts projections based on the historical reliability and calibration of the agents involved.
• Systemic Entropy: Measures the distribution of influence to ensure a balanced and robust collaboration topology.

Installation

Ensure you have Python 3.8 or higher installed.

1. Clone the repository.
2. Install the required dependencies:

pip install -r requirements.txt

Testing

The project uses pytest for comprehensive unit and integration testing.

To run the full test suite:

pytest

Individual test modules can be found in the tests/ directory, covering everything from policy schema validation to complex horizon sensitivity simulations.

Usage Example

from src.models.policy import PolicySchema
from src.simulation.policy_simulator import PolicySimulator

# Define a new governance policy
policy = PolicySchema(
    policy_id="pol-001",
    name="Synergy Incentive",
    affected_metrics=["synergy_density"],
    # ... additional configuration ...
)

# Initialize simulator and execute
simulator = PolicySimulator()
results = simulator.run_simulation(policy, initial_state, steps=100)