felipecustodio Overview & Purpose
Simulation Reasoning conducts step-by-step dynamic modeling of complex systems to observe emergent behavior, identify tipping points, and evaluate long-term outcomes under synthetic conditions.
When to Use
- Complex Dynamic Systems: Queueing systems, load behavior under traffic bursts, concurrency race conditions.
- Scenario Planning: Simulating multi-agent market conditions, adoption curves, or failure cascades.
Execution Workflow
- Define State Variables & Rules: Establish system entities, parameters, and transition rules.
- Initialize Simulation: Set initial seed conditions and time-step size ($\Delta t$).
- Step Through Epochs: Execute state transitions iteratively over $N$ time steps.
- Analyze Emergent Patterns: Identify bottlenecks, steady states, or catastrophic failure modes.
Expected Output Contract
### Simulation Results
- **Initial Conditions**: [Seed Parameters]
- **Execution Horizon**: [N Time Steps]
- **Key Metrics over Time**: [Summary Table / Stats]
- **Emergent Insights**: [System Behavior Analysis]
Scripts
scripts/simulation_reasoning.py- Deterministic evaluation, state validation, and CLI tool for simulation-reasoning.