MB Ontology: Unified Data Model

The standardized semantic foundation that enables peers to understand intentions, content, and actions without intermediaries.

Content Intention UseCase Concept

Child Elements: Media (text, image, video), Metadata (timestamp, author, license), Attachments

Interaction/Decision Tree: Content → Validate integrity → Propagate to interested peers → Optional reputation weighting

// Example semantic object
{
  "type": "content",
  "id": "mb:content:abc123",
  "payload": { "text": "Hello mesh!" },
  "signature": "..."
}

Child Elements: Offer, Request, Collaborate, Inform

Interaction/Decision Tree: Intention → Match with peers → Negotiate terms → Execute via escrow

{
  "type": "intention",
  "action": "offer",
  "item": "used bicycle",
  "price": { "amount": 150, "currency": "USD" }
}

Child Elements: Commerce, Communication, Coordination, Sharing

Interaction/Decision Tree: UseCase → Select template → Populate data model → Broadcast/discover

{
  "type": "usecase",
  "template": "p2p_marketplace",
  "parameters": { ... }
}

Child Elements: Trust, Reputation, Proof, Consensus (lightweight)

Interaction/Decision Tree: Concept → Apply to transaction → Verify locally → Propagate results

{
  "type": "concept",
  "reputation": { "peer": "mb:id:xyz", "score": 0.92 }
}

How Monkeys Can Save the World

Why the P2P economic model is better than the current B2C paradigm

Marketplace / Products Social / Communication Search Services / Events

Current B2C Workflow: User → Amazon/eBay → Search catalog → Pay (fees + tax) → Shipping via centralized logistics → Data sold to advertisers

P2P Solution: Peer broadcasts offer → Discovery via mesh → Direct negotiation → Escrow release → Local delivery or mesh-routed

Pros	Cons	Risks	Rewards
Lower fees, privacy	Less convenience initially	Fraud (mitigated by reputation)	True ownership, fairer wealth distribution

Current B2C Workflow: Post → Meta/Twitter servers → Algorithm curation → Ads injected → Data harvested

P2P Solution: Direct encrypted broadcast → Peers choose propagation → No central feed control

Pros	Cons	Risks	Rewards
No censorship, no ads	Discovery harder	Echo chambers	Authentic human connection

Current B2C Workflow: Query → Google → Ranked by ad spend → Tracked forever

P2P Solution: Distributed query flooding → Peers respond with known content → Local ranking

Pros	Cons	Risks	Rewards
Neutral results	Slower initially	Spam	Unbiased knowledge

Current B2C Workflow: Uber/Airbnb/Eventbrite → Platform takes 20-30% → Reviews controlled centrally

P2P Solution: Direct service offer → Reputation-based matching → Payment via protocol

Pros	Cons	Risks	Rewards
Higher earnings for providers	Trust building needed	Disputes	Local economy empowerment