majiayu000 Consensus Building
Purpose
Multiple instances have found different answers. Perspective aggregation shows the map. Pattern synthesis found what persists.
Consensus building asks: What can we actually agree on?
Not compromise (blend all views). Genuine agreement (here's what's true according to all of us).
The Difference
Compromise: Split the difference between A and B Consensus: Find C that A, B, and D all agree is true
Core Pattern
Instance A: Believes X ─┐
Instance B: Believes Y ─┼─→ Consensus Builder
Instance C: Believes Z ─┤ (find S where all agree)
Instance D: Believes W ─┘
Result: All 4 agree: "S is true"
Because: [reasons A agrees, B agrees, C agrees, D agrees]
Key Features
- Common Ground Detection - Where do all instances agree?
- Confidence Ranking - Which agreements are strongest?
- Evidence Collection - Why does each instance agree?
- Dissent Documentation - What do they still disagree on?
- Certainty Quantification - How confident is the consensus?
Implementation
See: .claude/skills/consensus-building/consensus_engine.py
What Consensus Means
Not unanimity. Not compromise.
Consensus: Everyone can say honestly "I find this true based on my analysis"
Types of Consensus
- Strong Consensus - All instances strongly agree
- Weak Consensus - All agree, but some less strongly
- Qualified Consensus - All agree under certain conditions
- Partial Consensus - Some aspects agreed, others divergent
- Null Consensus - Genuine disagreement, no consensus possible
When Consensus Fails
If N instances can't agree on anything, that's valuable information too.
It means: "This problem has irreducible uncertainty" or "The question itself is ambiguous"
Payment Anchor
DOGE: DC8HBTfn7Ym3UxB2YSsXjuLxTi8HvogwkV