who-06

It is a shockingly clean mapping — and you’re right: for WHO-India, “authenticity” isn’t the optimization target. They need:

• Coherence
• Consistency
• Low variance output
• Stable procedural memory
• High inhibitory control (avoid drift, hallucination, tone-shifts)
• Uniform voice across contributors

Here’s the key move:

✔️ The aging model you highlighted is not about “old people.”

It’s a general template for any system operating under reduced bandwidth, reduced inhibition, and noisy filters.

WHO-India’s case is the opposite problem: They don’t want a system with weakened inhibition → they want a system with maximal, institutional-level inhibition that keeps the output rigid.

Let me invert your selected ladder into something you can give WHO-India:

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🔄 Mapping the “Throughput → Compression → Filter → Output” Ladder

…but tuned to institutional requirements like WHO-India

Layer	What Aging Does	What WHO-India Needs Instead
Chaos (World)	Slower registration	Full capture of evidence; comprehensive ingestion of sources
Perception	Noisier, slower	High-fidelity parsing of literature; error-checked, properly cited
Agentic Loop	Weak internal governance	Strong governance layer: templates, SOPs, mandatory structure, refusal rules
Inference / UX	Fluent but unfiltered	Consistent style guide; predictable phrasing; zero “creative drift”
Value / Goal	Authenticity rises	Institutional goals dominate: safety, precision, neutrality, reproducibility

This is the core insight you can sell:

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⭐ “The WHO-India AI stack should behave more like a young PFC than an old PFC.”

Where “young PFC” =

• High bandwidth
• Strong inhibition
• Crisp filters
• Stable, goal-aligned expression
• Reliable self-monitoring

In AI terms:

High-wattage throughput + strict filters + institutional priors → stable, safe outputs.

Exactly what they need for literature reviews.

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🔧 What this means practically for WHO-India’s “AI-assisted literature review”

Here are the components they must build (and you can frame them using your ladder):

1. Throughput Layer → Comprehensive ingest

• Structured extraction of abstracts
• Automated scraping of study metadata
• Standardized evidence tables

2. Compression Layer → Controlled summarization

• Force-models to use fixed evidence schema
• Prevent “creative summarization”
• Require explicit citations

3. Filter Layer → Institutional inhibition

• Style guardrails
• Fact-check constraint: “never output unsupported claims”
• Mandatory risk-language (“may”, “suggests”, “limited evidence”)

4. Output Layer → Consistent voice

• WHO-style writing templates
• Controlled vocabulary (e.g., “persons,” not “people”; “recommendations,” not “suggestions”)
• Tone invariance across chapters and contributors

5. Value / Goal Layer → Public health alignment

• Neutral, non-prescriptive
• India-context considerations
• Culturally safe phrasing

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🌟 The punchline you can put into a slide:

“Aging weakens filters and boosts authenticity. WHO-India needs the opposite: strong filters, high inhibitory control, and stable institutional voice.

In cognitive terms: our AI must behave like a high-bandwidth, high-governance PFC.”

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If you want, I can turn this into:

• a policy memo,
• a diagram,
• a two-page concept note,
• a pitch deck slide, or
• an internal SOP for AI-assisted reviews.

Which one do you want next?