Week 7 — Stress Testing SustainHub: Resilience Quotient and System Robustness
Week 7: Stress Testing SustainHub — Resilience Quotient and System Robustness
This week, we focused on taking the next major step in our evaluation pipeline — measuring resilience. While last week was about maintaining harmony in ideal conditions, Week 7 was about understanding how the system behaves under stressful, imbalanced, and uncertain environments.
Key Highlights
- Introduced Resilience Quotient (RQ) as a formal metric
- Simulated task surges and role removals to stress-test the system
- Visualized system recovery and agent adaptability
- Logged SARSA agent behavior under constrained conditions
1. What is the Resilience Quotient?
The Resilience Quotient (RQ) measures the system’s ability to:
- Absorb disruptions (e.g., sudden increase in tasks or agent dropouts)
- Recover performance after a shock
- Maintain fairness and task completion under non-ideal conditions
Think of RQ as the community’s immune system: how quickly and effectively can it bounce back?
RQ Formula (simplified):
RQ = (1 - ΔHI) * RecoveryRate
Where:
ΔHI= Drop in Harmony Index after disruptionRecoveryRate= Speed at which HI returns to baseline- RQ ∈ [0, 1], with 1 being highly resilient
2. Stress Test Scenarios
We designed two primary tests:
A. Role-Based Agent Dropout
- Randomly disabled a subset of agents (e.g., all Maintainers)
- Observed reallocation behavior and backup performance from Contributors
B. Task Surge Injection
- Tripled the number of incoming tasks for 3 consecutive steps
- Measured how quickly agents adapted and balanced the load
3. What We Observed
After 10 simulation steps (with disruptions applied in steps 4–6):
- Harmony Index initially dropped from 0.82 → 0.64
- Recovered to 0.78 by step 10
- Calculated Resilience Quotient:
0.743
Key Insights:
- SARSA agents adapted with a brief learning lag
- Task rebalancing via MAB was slower during role-specific dropouts
- Fairness dipped briefly but recovered as agents diversified actions
4. Logging and Debugging Enhancements
To better monitor recovery:
- Added RQ logs to
simulation.py - Logged action-value updates from Q-tables during stress
- Plotted HI recovery curves across different stress levels
All relevant logs are stored under data/logs/ and visual outputs in data/plots/.
Next Steps
- Introduce reward shaping to encourage quicker recovery
- Evaluate long-term fatigue or agent burnout
- Refactor simulation for episodic stress testing (controlled trials)
- Add toggle flags for stress mode in the UI/CLI
Summary
Week 7 gave us critical insights into SustainHub’s resilience and adaptability. By simulating stress conditions and measuring RQ, we are now able to not only ensure smooth operation during normal conditions — but also ensure the system can bounce back under pressure.
Next week, we’ll explore dynamic reward tuning and meta-learning techniques to make the agents even smarter under stress.
Stay tuned!