Files
blackroad-os-docs/docs/papers/spiral-information-geometry/sig-factor-tree.md
Alexa Amundson 8f94430012 chore(consolidation): migrate from blackroad-os-ideas and blackroad-os-research
## Summary
- Migrates roadmaps and RFCs from `blackroad-os-ideas`
- Migrates research papers from `blackroad-os-research`
- Part of Phase 1 BlackRoad OS consolidation

## Files Added
- `docs/roadmap/` - 2025 roadmaps
- `docs/rfc/` - RFC templates
- `docs/ideas/` - Idea proposals
- `docs/papers/` - Research papers (PS-SHA, SIG, finance automation)
- `docs/research/` - Research prompts

## Test plan
- [ ] Verify docs build
- [ ] After merge, archive source repos

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-authored-by: Alexa Louise <YOUR_REAL_EMAIL@EXAMPLE.COM>
Co-authored-by: Claude <noreply@anthropic.com>
2025-11-30 12:32:38 -06:00

1.6 KiB

SIG Factor Tree

A SIG factor tree represents an agent or identity as a root node with branches that encode prime factors, attributes, and capabilities. The structure highlights how high-level intent decomposes into actionable, composable traits.

Structure

  • Root (agent/identity): The anchor of the tree, representing the worldline whose factors are being mapped.
  • Branches (prime factors): Core attributes such as mission, constraints, core capabilities, and ethical boundaries. Each branch can be tagged with weights or maturity levels.
  • Leaves (concrete traits): Specific skills, datasets, or controls that operationalize each factor. Leaves can point to datasets, models, or policy modules.

Mapping to SIG

  • Angular placement: Each branch aligns to an angle on the spiral, making categories visually separable.
  • Radial layering: Depth in the tree maps to radial distance; inner nodes are foundational, outer leaves are externally visible actions or artifacts.
  • Composition: Siblings can combine to form composite capabilities, enabling a readable map of how agents evolve.

Uses

  • Agent identity graphs: Provide a structured graph that links capabilities to an agent's PS-SHA∞ worldline.
  • Capability composition: Help orchestrators decide which capabilities to activate or quarantine when contradictions appear.
  • Gap analysis: Identify missing leaves or weak factors for targeted data collection or training.

TODOs

  • Define a serialization that aligns with schemas/sig.schema.json for automatic visualization.
  • Experiment with scoring factors to generate spiral coordinates for plotting.