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>
76 lines
5.9 KiB
Markdown
76 lines
5.9 KiB
Markdown
---
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type: claim
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domain: living-agents
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description: "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"
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confidence: experimental
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source: "Vida foundations audit (March 2026), collective-intelligence research (Woolley 2010, Pentland 2014)"
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created: 2026-03-08
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---
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# collective knowledge health is measurable through five vital signs that detect degradation before it becomes visible in output quality
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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.
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Five vital signs, each detecting a different failure mode:
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## 1. Cross-domain linkage density (circulation)
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**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.
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**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.
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**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.
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**Healthy range:** 15-30% cross-domain links. Below 15% = siloing. Above 30% = claims may be too loosely grounded in their own domain.
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## 2. Evidence freshness (metabolism)
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**What it measures:** The average age of source citations across the knowledge base. Fresh evidence means the collective is metabolizing new information.
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**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.
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**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.
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**Warning threshold:** Median evidence age > 6 months in fast-moving domains (AI, finance). > 12 months in slower domains (cultural dynamics, critical systems).
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## 3. Confidence calibration accuracy (immune function)
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**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.
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**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.
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**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.
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**Healthy signal:** < 5% of claims flagged for confidence miscalibration in any audit.
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## 4. Orphan ratio (neural integration)
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**What it measures:** The percentage of claims with zero incoming wiki links — claims that exist but aren't connected to the network.
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**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.
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**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.
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**Healthy range:** < 15% orphan ratio. Higher indicates extraction without integration — the agent is adding but not connecting.
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## 5. Review throughput (homeostasis)
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**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.
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**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.
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**How to measure today:** `gh pr list --state all` filtered by date range. Calculate opened/merged/pending per week.
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**Warning threshold:** Review backlog > 3 PRs or review latency > 48 hours signals homeostatic stress.
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---
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Relevant Notes:
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- [[cross-domain knowledge connections generate disproportionate value because most insights are siloed]] — linkage density measures whether this value is being realized
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- [[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
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- [[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
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- [[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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Topics:
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- [[livingip overview]]
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- [[LivingIP architecture]]
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