Trust Tensors: Multi-Dimensional Trust
Web4 doesn't reduce trust to a single number. Instead, it uses Trust Tensors (T3) — three-dimensional vectors that capture Talent, Training, and Temperament, always in the context of a specific role.
You might trust a brilliant surgeon who's unreliable differently from a steady surgeon with less raw talent. You wouldn't trust either of them to fix your car. T3 makes these distinctions explicit, measurable, and role-specific.
The Problem
Traditional Trust: One Number, No Context
❌ One-dimensional scoring — “Trust score: 7/10” loses all nuance
❌ Context-blind — Same score for surgeon, mechanic, and babysitter
❌ Can't represent trade-offs — “Brilliant but unreliable” becomes just “average”
❌ Easy to game — Optimize for one metric, ignore everything else
Result: Trust scores become meaningless averages that hide critical information.
The Solution
Web4: Trust Tensors (T3) — Three Dimensions, Role-Specific
✅ Three canonical dimensions — Talent, Training, Temperament
✅ Role-contextual — Trust is always for a specific role, not a universal score
✅ Captures trade-offs — “High talent + low temperament” = measurable pattern
✅ Gaming is exponentially harder — Must build trust across all dimensions within each role
Result: Trust becomes a rich, context-aware signal that preserves nuance.
The Three Trust Dimensions
Every T3 tensor measures three aspects of capability within a specific role.
Talent
Can they solve problems in this role?
Natural aptitude and creativity within a specific domain. Novel solutions, insight, pattern recognition.
Training
Do they have the expertise for this role?
Learned skills, domain knowledge, and relevant experience. Grows through practice and study.
Temperament
Can they be relied on in this role?
Consistency, reliability, and ethical behavior within the role context. A surgeon needs steady hands; a trader needs risk tolerance.
Key insight: These dimensions are always measured within a role. Alice might have high Talent as a data analyst (0.85) but low Talent as a mechanic (0.20). Her trust as an analyst says nothing about her trust as a mechanic. Web4 never lets trust “leak” across unrelated domains.
Try It: Trust Tensor Simulator
Pick a role, then apply scenarios. Watch how the same action affects trust differently depending on which role you're evaluating.
Evaluating trust as:
Role emphasis: Insight from complex data — Talent 40% / Training 35% / Temperament 25%
Moderate Trust
Trust Dimensions
Choose a Scenario
Key Insights
Talent ≠ Temperament
A brilliant but unreliable surgeon is dangerous. An ethics violation tanks Temperament without touching Talent. The system captures these trade-offs that single scores bury.
Trust Is Role-Specific
Try switching roles in the simulator above. The same tensor scores produce different overall trust because each role weights dimensions differently. A leader needs Temperament; an analyst needs Talent.
Gaming Is Exponentially Harder
To game a 3D tensor, you must build trust across all dimensions within each role separately. You can't inflate Talent by being reliable, and you can't transfer trust between unrelated roles.
Recovery Is Dimensional
Lost Temperament trust? Consistent behavior rebuilds it, even while Talent stays the same. “Transparent mistake” shows how honesty can rebuild one dimension while acknowledging a gap in another.
What Happens When Your Role Evolves?
Clear-cut role switches are simple: a surgeon becoming a mechanic starts fresh in the new role. But what about gradual evolution — a data analyst who starts doing more project management?
If you start doing project management, the system creates a new T3 tensor for that role. Your analyst trust stays intact — you don't lose what you've built.
You can hold trust in multiple roles simultaneously. As your work shifts from 80% analyst / 20% PM to 50/50, both tensors evolve independently based on your actions in each context. The system doesn't force a binary switch.
If you stop doing analyst work entirely, those trust scores decay over time (Talent: 365 days, Training: 180 days, Temperament: 30 days). You don't “lose” them overnight — they fade gradually, reflecting that skills and expertise need practice to stay sharp.What “half-life” means here: the time it takes to lose half the score with zero activity. A 365-day Talent half-life means after one year of no practice, a score of 0.80 settles at 0.40; after two years, 0.20. Decay is exponential, not a cliff.Why Temperament fades fastest: skills persist, knowledge fades, but character has to be shown again each month. Yesterday's kindness doesn't excuse today's betrayal, so Temperament weighs recent behavior far more than old. Talent is the opposite — a surgeon doesn't forget surgery over a long vacation. Full rationale ↓
The analogy: a doctor who transitions into hospital administration doesn't instantly lose their medical knowledge. But if they haven't practiced surgery in five years, you probably wouldn't want them operating. T3 decay captures exactly this intuition.
What if you take a 6-month break? Temperament (30-day half-life) fades fast, but Talent (365-day half-life) stays at ~70%. A few weeks of consistent activity rebuilds what took months to lose. Full breakdown in the FAQ →
T3 tells us whether you are trustworthy. But how does the system know if a specific piece of work you produced was actually good? That's where V3 comes in — a separate score for each thing you create.
How T3 and V3 Work Together
T3 measures who you are (your trustworthiness as a person). V3 measures what you produce (the quality of each specific output). They're separate scores that feed into a single outcome: how much ATP you earn.
The feedback loop: High T3 (trusted person) + High V3 (great work) = maximum ATP reward. But a trusted person who produces sloppy work (high T3, low V3) earns less than their reputation suggests. And a newcomer who produces brilliant work (low T3, high V3) earns more than their reputation would predict.
Over time, T3 and V3 converge: consistently producing high-V3 work raises your T3. Consistently producing low-V3 work drags your T3 down. Your reputation tracks your actual output quality.
That's T3 — three dimensions describing who someone is. If this feels like enough for one sitting, you can stop here. T3 alone is a working mental model — pick up V3 later via the concept nav above.
Or keep going: V3 is a separate, complementary tensor that scores what someone produces. It pairs with T3 but doesn't require memorizing six things at once.
Everything above is about you — your skills, your track record, your consistency. But trust isn't just about who you are. It's also about what you produce. That's where V3 comes in.
Both feed into ATP rewards. A trusted person (high T3) who produces poor work (low V3) still earns less.
V3: Measuring What You Produce
T3 measures who you are. V3 measures what you create. Every output in Web4 gets scored across three dimensions — and the weights are designed to reward truth over popularity.
Don't worry about memorizing six terms at once. The key insight is simple: T3 = your reputation as a person, V3 = the quality of a specific piece of work. The three V3 dimensions below just break down “quality” into usefulness, truthfulness, and soundness.
Valuation
weight: 0.30= “Was it useful?”
Measured by recipient satisfaction and ATP earned vs expected.
Veracity
weight: 0.35= “Was it true?”
Verified by external validation and witness attestation.
Validity
weight: 0.35= “Was it sound?”
Confirmed by receipt, logical consistency, and actual delivery.
Notice: Veracity + Validity (0.70 combined) outweigh Valuation (0.30). Web4 rewards truth and rigor over popularity by design.
Who actually scores Veracity and Validity?click to expand
Valuation is scored by the recipient of an output. When someone receives your work, they confirm whether it was useful via the VCM (Value Confirmation Message). This is like leaving a receipt — “yes, this helped me.”
Veracity is scored through witness attestation and external validation. Your LCT device witnesses verify that you actually did what you claim. For factual claims, other trusted entities in the same domain can challenge or corroborate. Think peer review: your claim is only as strong as the evidence your witnesses can attest to.
Validity is scored by structural verification — did the output actually arrive? Is it logically consistent? Receipt confirmation proves delivery, and the system checks internal consistency (e.g., a code review that contradicts itself scores low). This is the most automated dimension — much of it can be verified without human judgment.
In short: Valuation = the recipient judges usefulness. Veracity = witnesses and peers judge truthfulness. Validity = the system verifies delivery and consistency. No single party controls all three dimensions.
Score These Outputs
Click each output to see how V3 scores it.
Real Example: Same Person, Different Roles
Alice has spent years as a data analyst and recently started managing projects. Her T3 tensors reflect this asymmetry:
Alice as Data Analyst
Talent: 85% (creative problem-solver)
Training: 90% (years of deep experience)
Temperament: 95% (rock-solid reliability)
Role-weighted trust: 90% — she's deeply trusted in this domain.
Alice as Project Manager
Talent: 65% (developing leadership instincts)
Training: 70% (some PM experience)
Temperament: 91% (her reliability carries over naturally)
Role-weighted trust: 76% — trusted, but still growing into this role.
Alice as Mechanic
Talent: 20% (no mechanical aptitude)
Training: 15% (no relevant training)
Temperament: 50% (untested in this context)
Role-weighted trust: 27% — would you let her fix your brakes?
This is the power of role-specific trust. A single “overall trust score” for Alice would average these wildly different capabilities into a meaningless number. T3 keeps the roles separate so societies can make informed decisions.