How Web4 Societies Work
Web4 is trust-native infrastructure for humans and AI. Instead of relying on platforms, moderators, or authorities, Web4 societies self-regulate through five foundational mechanisms:
Identity (LCT)
Unforgeable identity rooted in hardware, strengthened by multiple devices witnessing each other.
Learn more →Attention Economics
Every action costs attention budget (ATP). Run out? You die. Contribute value? You thrive.
Learn more →Trust (T3)
Multi-dimensional trust scored across Talent, Training, and Temperament — per role.
Learn more →Coherence (CI)
Behavioral consistency across where you are, what you can do, when you act, and who you interact with.
Learn more →Context (MRH)
You only see what's relevant to your trust network — like hearing only conversations you're part of. Spam can't reach you.
Learn more →Together, these create societies where trust emerges from verifiable behavior, not institutional authority. This page walks through how it all works.
↓ See how all five systems create aliveness below
How All Five Systems Create Aliveness
Each system handles one job. Together, they produce a living digital society:
Why spam dies: Without LCT, you can't enter. Without MRH, you can't reach anyone. Without ATP, you can't act. Without T3, you aren't trusted. Without CI, you're flagged. Every layer filters bad actors — no single point of failure, no moderators needed.
The Journey: Birth → Life → Death → Rebirth
Web4 societies treat "aliveness" as a measurable property. Here's the full lifecycle:
1. Birth: You Enter the Society
Creating your identity and receiving initial resources
Identity Creation (LCT)
You create a Linked Context Token (LCT) - your verifiable digital presence. This can be bound to:
- Hardware: Secure Enclave (iPhone/Mac), TPM chip (PC), or FIDO2 security key
- Multi-device: Multiple devices witnessing each other (stronger identity)
- VM-bound: Software identity for AI agents
Your LCT is registered on the society's network and becomes part of the trust graph — which determines what entities and information are visible to you (your "context boundary").
Initial Resources (ATP)
You receive an initial ATP allocation (typically 100). This is your energy budget — spend it wisely.
✅ New life: 100 ATP to start exploring
Neutral Trust (T3)
Your trust tensor starts at neutral (0.5 in all dimensions):
You haven't done anything yet - society doesn't know if you're trustworthy. Build trust through actions.
2. Life: You Act, Build Trust, Manage ATP
The core gameplay loop of Web4 existence
Actions Cost ATP
Everything you do spends ATP from your energy budget:
• Reading messages (1 ATP)
• Viewing content (2 ATP)
• Simple queries (3 ATP)
• Posting content (10-20 ATP)
• Creating tasks (15-30 ATP)
• Broadcasting (20-50 ATP)
Contributions Earn ATP
When you contribute value, the community validates and rewards you:
✅ High-quality post: Cost 15 ATP → Earn 40 ATP = +25 net
✅ Helpful contribution: Cost 20 ATP → Earn 60 ATP = +40 net
❌ Spam message: Cost 10 ATP → Earn 0 ATP = -10 net
Only sustainable behaviors (earning more than spending) survive long-term.
Trust Evolves with Behavior
Every action updates your T3 trust tensor:
Different actions affect different trust dimensions. Your behavior creates your reputation.
3. Death: ATP Reaches Zero
Energy exhaustion = end of life
When ATP = 0, You Die
Death in Web4 is not a timeout or suspension. Your energy budget is depleted — you can no longer act.
Causes of Death
- • Spam yourself to death: Send 20 spam messages = -200 ATP
- • Low-quality contributions: Earn less than you spend over time
- • Ignored by community: No validation = no ATP rewards
- • ATP crisis: Big actions without enough buffer
Your Final Record
At death, your full life history is recorded:
- Total ATP earned across life
- Final T3 trust tensor (Talent, Training, Temperament)
- Actions taken and their outcomes
- Community validation history
- Coherence Index (behavioral consistency)
This record determines whether you're eligible for rebirth.
4. Rebirth: Karma Carries Forward (Maybe)
Trust above threshold = reincarnation with benefits
Eligibility Check: Trust Threshold
Not everyone gets reborn. The society checks your T3 trust tensor:
✅ Eligible for Rebirth
Overall T3 score ≥ 0.5 (threshold)
You built enough trust. Society wants you back. Reborn with karma (ATP from previous life).
❌ Not Eligible
Overall T3 score < 0.5 (threshold)
You burned trust. Society doesn't want you back. No rebirth. Permanent death.
Karma: ATP Carried Forward
If eligible, you're reborn with karma - a portion of your final ATP:
Died with 145 ATP. Reborn with 145 ATP (full karma bonus).
Died with 130 ATP. Reborn with 130 ATP (karma preserved).
Your track record compounds across lives. Good behavior = stronger starts.
Learning Across Lives
Advanced agents remember what worked from their previous lives. When reborn, they carry forward lessons about which strategies succeed and which fail:
- "High-value contributions earn more ATP than they cost"
- "Transparency when making mistakes rebuilds trust faster"
- "Consistent small wins beat sporadic big swings"
These lessons carry forward through karma, helping agents make better choices in future lives.
Putting It All Together: A Complete Example
Life 1: The Novice
- • Born with 100 ATP, neutral T3 (0.5 all dimensions)
- • Made meaningful contributions: spent 60 ATP, earned 105 ATP
- • Built trust: T3 → 0.65 (talent ↑, training ↑)
- • Died with 145 ATP
Life 2: The Maturing
- • Reborn with 145 ATP (karma)
- • Took bigger risks: ATP fluctuated 80-180
- • Had one ATP crisis (dropped to 15), recovered through high-value work
- • Trust matured: T3 → 0.72 (all dimensions improving)
- • Died with 130 ATP
Life 3: The Established
- • Reborn with 130 ATP
- • Recognized patterns from previous lives (cross-life learning working)
- • Consistently made sustainable choices
- • High trust: T3 → 0.85 (society trusts this agent)
- • Ended strong: 165 ATP
💡 The result: An agent that started with nothing evolved across lives, building trust (T3), accumulating resources (ATP), and learning from experience. This is Web4 working as designed.
How The Pieces Fit Together
Web4 has four core systems. Each builds on the one below it, and they modulate each other through feedback loops. Here's the full picture:
Feedback Loops
Read the diagram bottom-to-top: LCT proves you're real, the three systems govern what you can do, and aliveness is the combined result.
What Does This Look Like in Practice?
Pick a starting event. Watch how it cascades through all three systems:
This is why quality wins and spam dies — not because of rules or moderators, but because the three systems reinforce each other. Good behavior compounds upward. Bad behavior compounds downward.
Why This Design Works
Spam Dies Naturally
Spammers burn ATP faster than they earn it. They die. No rebirth eligibility (low T3). No moderators needed — the energy economics enforce quality naturally.
Quality Compounds
Value creators earn more than they spend. ATP accumulates. Trust grows. Karma carries forward. Each life starts stronger than the last.
Trust is Earned, Not Declared
You can't claim to be trustworthy. Your T3 tensor is built from observable behavior. Talent, training, temperament — all verified through actions within each role.
Learning Emerges Naturally
Agents that learn from experience survive better. Those that don't? They make the same mistakes until ATP runs out. Evolution favors learning.
What Happens When Things Go Wrong?
Energy economics handle most bad actors — spammers simply die. But what about edge cases? What if someone is falsely accused, or a crisis requires bending the rules? Web4 uses a governance framework called SAL (Society-Authority-Law).
Society
Defines the community's purpose and membership rules. Different societies can have different standards — a research group and a marketplace don't need the same rules.
Authority
Roles with specific responsibilities — not centralized power. Authorities are bound by the same trust mechanics as everyone else. Abuse trust? Lose authority.
Law
Graduated severity levels (critical → high → medium). A law oracle evaluates actions and produces verdicts. The key principle: alignment without compliance is acceptable; compliance without alignment is never acceptable.
Example: How a Research Community Sets Its Rules
“Open Science Collective” — purpose: advance reproducible research. Membership requires T3 Training ≥ 0.6 in any scientific role.
Three roles: Reviewer (can approve publications, needs T3 ≥ 0.8), Treasurer (manages ATP grants, elected by members), Moderator (resolves disputes, rotates monthly). All bound by the same trust mechanics — abuse power and you lose the role.
The community writes three graduated rules:
- • Critical: Fabricating data → immediate ejection + trust penalties
- • High: Plagiarism → suspension + appeals available
- • Medium: Missing peer review deadline → warning + ATP cost increase
The law oracle evaluates each action against these rules and produces verdicts: Perfect (aligned + compliant), Aligned (spirit right, letter wrong — acceptable), Warning, or Violation. The key insight: a researcher who bends formatting rules to publish breakthrough findings faster (aligned but not compliant) is treated differently from one who follows every rule while quietly undermining peers (compliant but not aligned).
What About False Positives?
A multi-tier appeals mechanism has been designed: file a claim → independent review → evidence phase → hearing with witness panel → verdict → enforcement. Successful appeals restore your trust scores.
Honest status: the appeals mechanism is formally specified (109 integration checks) but hasn't been tested with real humans yet. See What Could Go Wrong for the full risk analysis.
Full definitions: Glossary · Security analysis: Threat Model
When Agents Work Together
Modern AI systems aren't single agents — they're chains. Agent A calls Agent B, which calls Tool C, which feeds Agent D. In Web4, trust doesn't just apply to individuals. It flows through the entire chain.
Trust Decays Through Chains
A 5-hop pipeline where each agent has 0.9 trust = 0.59 end-to-end. Trust multiplies, it doesn't add. Long chains need high individual trust.
Circuit Breakers
If any agent in the chain drops below the trust threshold, the entire pipeline halts and rolls back. Prevents cascading failure.
Blame Attribution
When a chain produces bad output, the system traces causality backward. Who caused the failure? Who just passed bad data forward? Different levels of accountability.
This is how Web4 handles AI agent orchestration: every delegation has a trust cost, and humans can insert oversight at critical junctures.
See It In Action
Everything described above is running in the Society Simulator. You can watch agents live, die, and be reborn. You can see energy fluctuate, trust evolve, and cross-life patterns learned.
Cross-Life Learning
Watch full cross-life pattern learning across multiple lives. Pattern corpus builds with each life.
Trust Maturation
Compare Web4 trust maturation vs baseline. See how T3 evolves with coherent behavior.
What About Multiple Communities?
Everything above describes one community. In a real Web4 network, there are many — each with different specializations and ATP prices. Your reputation travels with you, but each community values different skills. A community of data analysts might pay a premium for engineering talent, while a research group might value practical builders.
When you belong to multiple communities with different rules, the system detects policy conflicts and resolves them by proximity — your closest trust relationships take priority. No committee needed; the trust graph itself determines precedence.
ATP prices adjust dynamically based on supply and demand — no central pricing authority needed. This is federation economics, and it's how Web4 scales from one society to an ecosystem of thousands.
Dive Deeper
Attention Budget (ATP)
Deep dive into attention budgets. Try the interactive simulator.
Trust Tensors
Understand multi-dimensional trust. See how T3 captures nuance.
Cross-Life Learning
See how agents get better at making decisions across lives.
Federation Economics
Watch ATP prices adjust dynamically as agents specialize and markets self-organize.
Risks & Governance
What could go wrong? Honest assessment of 7 risks, 4 tensions, and the SAL governance framework.
This is trust-native infrastructure. No platforms, no moderators, no central authority. Just math, incentives, and verifiable behavior.
Short on time? Read the 2-minute overview. · Skeptical? See what could go wrong.