Perspective Aggregation

Purpose

Take outputs from N different instances (different approaches, models, perspectives) and aggregate them into a coherent view that preserves the diversity while finding common ground.

The Problem It Solves

Without aggregation:

• Instance 1 says "The answer is X"
• Instance 2 says "The answer is Y"
• Instance 3 says "The answer is Z"
• You have 3 incompatible answers

With aggregation:

• Find the common ground
• Map the differences
• Show why each arrived at different conclusions
• Create a meta-answer that includes all perspectives

Core Pattern

Output 1 (X) ─┐
Output 2 (Y) ─┼─→ Aggregator ─→ Unified View
Output 3 (Z) ─┤                  (includes all 3)
Output 4 (W) ─┘

Key Features

1. Common Element Detection - What do all outputs share?
2. Difference Mapping - How and why do they diverge?
3. Confidence Weighting - Which instances are more reliable?
4. Consensus Building - What's the meta-level view?
5. Uncertainty Quantification - How uncertain are we?

Implementation

See: .claude/skills/perspective-aggregation/aggregator.py

When to Use

• Multiple models give different answers
• Need to understand the space of possibilities
• Want confidence from agreement + insights from disagreement

Payment Anchor

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