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

1. Common Ground Detection - Where do all instances agree?
2. Confidence Ranking - Which agreements are strongest?
3. Evidence Collection - Why does each instance agree?
4. Dissent Documentation - What do they still disagree on?
5. 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

1. Strong Consensus - All instances strongly agree
2. Weak Consensus - All agree, but some less strongly
3. Qualified Consensus - All agree under certain conditions
4. Partial Consensus - Some aspects agreed, others divergent
5. 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