## Line Chart: Performance of Different PRM Aggregation Techniques ### Overview The chart compares the accuracy (%) of five PRM (Probabilistic Risk Management) aggregation techniques across six generation rollouts (2⁰ to 2⁶). Accuracy is measured on the y-axis (80–88%), while the x-axis represents exponential growth in generation rollouts. Five distinct lines represent different aggregation methods, with performance trends varying significantly. --- ### Components/Axes - **X-Axis (Generation Rollouts)**: Labeled with powers of 2 (2⁰ to 2⁶), indicating exponential progression. - **Y-Axis (Accuracy %)**: Ranges from 80% to 88%, with gridlines at 2% intervals. - **Legend**: Positioned in the top-right corner, mapping colors to techniques: - Blue: Majority Vote - Orange: PRM-Last-Max - Green: PRM-Last-Sum - Red: PRM-Min-Max - Purple: PRM-Min-Sum --- ### Detailed Analysis 1. **Majority Vote (Blue)**: - Starts at 80% (2⁰) and increases steadily. - Reaches ~86% at 2⁶, showing consistent improvement. - Slope: Linear upward trend. 2. **PRM-Last-Max (Orange)**: - Begins at 80% (2⁰), dips slightly at 2¹ (~79.8%), then rises. - Peaks at ~82% at 2⁶. - Slope: Gradual upward trend with minor fluctuations. 3. **PRM-Last-Sum (Green)**: - Starts at 80% (2⁰), rises sharply to ~85% by 2³. - Plateaus near 86% at 2⁶. - Slope: Steep initial growth, then stabilizes. 4. **PRM-Min-Max (Red)**: - Begins at 80% (2⁰), increases gradually to ~82% at 2⁶. - Slope: Slow, linear upward trend. 5. **PRM-Min-Sum (Purple)**: - Starts at 80% (2⁰), surges to ~86.5% by 2³. - Maintains ~86.5% accuracy through 2⁶. - Slope: Rapid initial growth, then plateaus. --- ### Key Observations - **Top Performers**: PRM-Min-Sum (purple) and PRM-Last-Sum (green) achieve the highest accuracy (~86–86.5%) by 2⁶. - **Dip Anomaly**: PRM-Last-Max (orange) shows a temporary drop at 2² (~80.5%) before recovering. - **Consistency**: Majority Vote (blue) and PRM-Min-Max (red) exhibit steady but slower growth. - **Exponential Scaling**: All techniques improve as generation rollouts increase, but PRM-Min-Sum and PRM-Last-Sum outperform others significantly. --- ### Interpretation The data suggests that **PRM-Min-Sum** and **PRM-Last-Sum** are the most effective aggregation techniques for maximizing accuracy across generations. Their rapid initial improvement and plateau at high accuracy levels indicate robustness in scaling. In contrast, **PRM-Last-Max** and **PRM-Min-Max** lag behind, with the former showing a notable dip at 2² that may reflect instability in intermediate generations. The **Majority Vote** method, while reliable, underperforms compared to PRM-based techniques. The exponential growth in generation rollouts correlates with improved performance, emphasizing the importance of iterative refinement in PRM systems. The anomaly in PRM-Last-Max warrants further investigation into its behavior during transitional phases.