# 📊 Smart Connections → Chain Metrics ## Simple Approach: Connection Density = Effective Sample Size ### 1. Weekly Connection Count In each chain folder, create a note: `week-{{date}}-connections.md` ```markdown ## Chain Connections This Week 🐢 → 🐅: [[note1]] → [[note2]] (validated theory) 🐅 → 🐙: [[process1]] → [[execution1]] (implemented) 🐙 → 👾: [[project1]] → [[feedback1]] (user tested) 👾 → 🐢: [[insight1]] → [[experiment1]] (new hypothesis) **Total Cross-Chain Links**: _ ``` ### 2. Calculate ESS (Effective Sample Size) ``` ESS = Number of cross-chain connections ``` - High ESS = Ideas flowing between chains - Low ESS = Stuck in silos ### 3. Calculate R-hat (Simple Version) ``` Within-chain links = Links inside same folder Between-chain links = Links across folders R-hat = 1 + (Within-chain links / Between-chain links) ``` - R-hat < 1.5 = Good mixing - R-hat > 2.0 = Too siloed ### 4. Convergence Plot (Manual) Track weekly in a table: | Week | 🐢→🐅 | 🐅→🐙 | 🐙→👾 | 👾→🐢 | R-hat | |------|-------|-------|-------|-------|-------| | W1 | 3 | 2 | 1 | 0 | 2.4 | | W2 | 4 | 3 | 2 | 1 | 1.8 | | W3 | 5 | 4 | 3 | 2 | 1.4 | | W4 | 5 | 5 | 4 | 3 | 1.2 | ### 5. Smart Connections Query Use Obsidian search to find cross-chain links: ``` path:1_🐢 [[2_🐅 path:2_🐅 [[3_🐙 path:3_🐙 [[4_👾 path:4_👾 [[1_🐢 ``` ## Practical Implementation 1. **Daily**: When creating notes, explicitly link to other chains 2. **Weekly**: Count cross-chain links using Smart Connections graph 3. **Monthly**: Plot R-hat trend - should decrease over time ## Visual Check in Smart Connections - Healthy: Dense connections between all 4 chain clusters - Unhealthy: Isolated clusters with few bridges **Key Insight**: More cross-chain links = more ergodic life