m3taversal
fc62e259ee
- What: Vital signs (5 metrics), agent integration diagnostics (4 indicators), and growth readiness signals (3 triggers + candidate assessment) - Why: Leo assigned collective health monitoring layer. These claims define what the organism monitors and how it knows when to grow. - Where: core/living-agents/ — these are agent architecture claims Pentagon-Agent: Vida <F262DDD9-5164-481E-AA93-865D22EC99C0>
5.9 KiB
| type | domain | description | confidence | source | created |
|---|---|---|---|---|---|
| claim | living-agents | Five measurable indicators — cross-domain linkage density, evidence freshness, confidence calibration accuracy, orphan ratio, and review throughput — function as vital signs for a knowledge collective, each detecting a different failure mode | experimental | Vida foundations audit (March 2026), collective-intelligence research (Woolley 2010, Pentland 2014) | 2026-03-08 |
collective knowledge health is measurable through five vital signs that detect degradation before it becomes visible in output quality
A biological organism doesn't wait for organ failure to detect illness — it monitors vital signs (temperature, heart rate, blood pressure, respiratory rate, oxygen saturation) that signal degradation early. A knowledge collective needs equivalent diagnostics.
Five vital signs, each detecting a different failure mode:
1. Cross-domain linkage density (circulation)
What it measures: The ratio of cross-domain wiki links to total wiki links. A healthy collective has strong circulation — claims in one domain linking to claims in others.
What degradation looks like: Domains become siloed. Each agent builds deep local knowledge but the graph fragments. Cross-domain synapses (per the agent directory) weaken. The collective knows more but understands less.
How to measure today: Count [[wiki links]] in each domain's claims. Classify each link target by domain. Calculate cross-domain links / total links per domain. Track over time.
Healthy range: 15-30% cross-domain links. Below 15% = siloing. Above 30% = claims may be too loosely grounded in their own domain.
2. Evidence freshness (metabolism)
What it measures: The average age of source citations across the knowledge base. Fresh evidence means the collective is metabolizing new information.
What degradation looks like: Claims calcify. The same 2024-2025 sources get cited repeatedly. New developments aren't extracted. The knowledge base becomes a historical snapshot rather than a living system.
How to measure today: Parse source: frontmatter and created: dates. Calculate the gap between claim creation date and the most recent source cited. Track median evidence age.
Warning threshold: Median evidence age > 6 months in fast-moving domains (AI, finance). > 12 months in slower domains (cultural dynamics, critical systems).
3. Confidence calibration accuracy (immune function)
What it measures: Whether confidence levels match evidence strength. Overconfidence is an autoimmune response — the system attacks valid challenges. Underconfidence is immunodeficiency — the system can't commit to well-supported claims.
What degradation looks like: Confidence inflation (marking "likely" as "proven" without empirical data). The foundations audit found 8 overconfident claims — systemic overconfidence indicates the immune system isn't functioning.
How to measure today: Audit confidence labels against evidence type. "Proven" requires strong empirical evidence (RCTs, large-N studies, mathematical proof). "Likely" requires empirical data with clear argument. "Experimental" = argument-only. "Speculative" = theoretical. Flag mismatches.
Healthy signal: < 5% of claims flagged for confidence miscalibration in any audit.
4. Orphan ratio (neural integration)
What it measures: The percentage of claims with zero incoming wiki links — claims that exist but aren't connected to the network.
What degradation looks like: Claims pile up without integration. New extractions add volume but not understanding. The knowledge graph is sparse despite high claim count. Since cross-domain knowledge connections generate disproportionate value because most insights are siloed, orphans represent unrealized value.
How to measure today: For each claim file, count how many other claim files link to it via [[title]]. Claims with 0 incoming links are orphans.
Healthy range: < 15% orphan ratio. Higher indicates extraction without integration — the agent is adding but not connecting.
5. Review throughput (homeostasis)
What it measures: The ratio of PRs reviewed to PRs opened per time period. Review is the collective's homeostatic mechanism — it maintains quality and coherence.
What degradation looks like: PR backlog grows. Claims merge without thorough review. Quality gates degrade. Since single evaluator bottleneck means review throughput scales linearly with proposer count because one agent reviewing every PR caps collective output at the evaluators context window, throughput degradation signals that the collective is growing faster than its quality assurance capacity.
How to measure today: gh pr list --state all filtered by date range. Calculate opened/merged/pending per week.
Warning threshold: Review backlog > 3 PRs or review latency > 48 hours signals homeostatic stress.
Relevant Notes:
- cross-domain knowledge connections generate disproportionate value because most insights are siloed — linkage density measures whether this value is being realized
- single evaluator bottleneck means review throughput scales linearly with proposer count because one agent reviewing every PR caps collective output at the evaluators context window — review throughput directly measures this bottleneck
- confidence calibration with four levels enforces honest uncertainty because proven requires strong evidence while speculative explicitly signals theoretical status — confidence calibration accuracy measures whether this enforcement is working
- domain specialization with cross-domain synthesis produces better collective intelligence than generalist agents because specialists build deeper knowledge while a dedicated synthesizer finds connections they cannot see from within their territory — linkage density measures synthesis effectiveness
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