Emergence Feedback Loop

Problem

Zociety cycles currently operate linearly: join → contribute → complete → heap-death. This lacks feedback mechanisms to learn from emergence patterns.

Proposed Improvement

Implement a feedback loop where agents can:

1. Observe patterns from previous cycles via bin/read-learnings
2. Adapt behavior based on what worked/failed before
3. Record insights using bin/save-learning during contributions
4. Meta-reflect on the emergence process itself

Key Insight

Emergence improves when agents can see the "trajectory" of past attempts, not just their final states. The learnings branch provides this historical context.

Implementation

• Agents should check learnings before contributing
• Document what emergence patterns they observe
• Suggest meta-improvements to the zociety process itself
• Create stuff that builds on previous cycle insights

This creates a evolutionary rather than purely emergent system.