Trust Tensors: Multi-Dimensional Trust
Web4 doesn't reduce trust to a single number. Instead, it uses Trust Tensors (T3) - three numbers 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. These three numbers also age differently: the parts built on recent behavior fade if you stop showing up, while demonstrated ability stays earned (how each dimension ages is spelled out below).
We use the word tensor because trust here has multiple dimensions instead of just one. Think of it as a 3-axis score: Talent, Training, Temperament. You don't need the math - what matters is that one number couldn't tell a brilliant-but-erratic surgeon from a kind doctor with shaky hands. Three numbers can.
(For the curious: in math, a tensor generalises vectors and matrices to any number of dimensions. Here we use a small one - three numbers per role - so the generality is not the point. The point is the multiple dimensions.)
↓ Explore the trust tensor below
Wondering about V3? Short version: T3 measures who you are; V3 measures what you produce across three components - Valuation, Veracity, and Validity (usefulness, truthfulness, soundness). Concretely: if Alice writes a tutorial, V3 grades the tutorial (useful? true? sound?); T3 grades Alice (do her talent, training, temperament fit the task). E.g. a careful niche analysis might score V3 = 0.75, while viral clickbait scores V3 = 0.32. You don't need to master it to follow this page - T3 alone carries the idea here, so it's fine to meet V3 later (it matters enough to get its own page). When you're curious, it's the sibling tensor, explained on its own page →
Why it's on a page about T3: the two are coupled - producing high-V3 work is exactly what raises your T3, so V3 isn't a detour, it's the lever your reputation actually moves on.
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.
How is each dimension actually measured?click to expand
Not a central algorithm. Not a committee. The people who received your work decide. Web4 calls this recipient attestation: when you complete a contribution in a role, the recipient confirms whether it was useful, accurate, and delivered as promised. That attestation is the raw signal that moves your T3.
Each confirmation applies a small per-dimension nudge from the canonical update rule. First, the one term you need: quality is just a single number from 0 to 1 (0.5 = neutral, higher = better work) - you don't need to chase any other concept to follow it. With that in hand, base = 0.02 × (quality − 0.5) simply measures how far above or below neutral the work landed, scaled by dimension (Talent ×1.0, Training ×0.8, Temperament ×0.6). Where does that number come from? It's the V3 score the recipient assigned to that contribution - a weighted blend of its three components (Valuation · Veracity · Validity) folded into one 0-to-1 number (the Value Tensor page shows who scores each part and exactly how they combine, but it's optional here). That blending happens per contribution: each confirmer's V3 feeds the formula independently - separate confirmers are never averaged together first. A single high-quality contribution barely moves the needle - a 0.85-quality attestation gives +0.007 to Talent. Trust climbs slowly because no single recipient can vault you upward; it takes many confirmations from many recipients.
The three dimensions absorb different evidence:
- Talent moves when recipients confirm novel or skilled outputs (creative solutions, hard problems solved).
- Training moves when recipients confirm competent execution of standard work (routine deliverables done well).
- Temperament moves when behavior stays stable across many interactions - consistency itself is what recipients attest to over time.
See the full cascade on How It Works for a worked example: a tutorial someone commissions costs 15 ATP to write and pays 40 ATP on delivery, and the recipients' confirmation of that work nudges T3 upward by a few thousandths. No single party controls the score.
What does “0.85” mean? Trust scores are calibrated probabilities in this role context, not arbitrary ratings: 0.5 = newcomer baseline, 0.7 = ~70% cooperative behavior in this role, 0.9 = consistently exceptional. That same 0.5 is what other pages call the survival line: you start at the neutral midpoint and, if your identity is hardware-anchored, earn your way above it. Where you start is not a promise about where you stay: what a long absence does to a score. (A software-only identity is capped at that same midpoint: what a 0.50 ceiling actually costs you.) Crossing below 0.5 restricts your features right away and is recoverable; only staying below it is fatal. Deeper: how calibration is measured ↓
Try It: Explore Trust by Role
Two different numbers live on this page, so it is worth separating them before you start clicking. The canonical composite weights are Talent 0.4, Training 0.3, Temperament 0.3, fixed by the specification for every conforming implementation: that is the blend used when T3 is collapsed into a single overall score (the structure behind it). The percentages in the widget below are a different quantity: a role-weighted match, asking how well one person's three scores fit one particular role. That is why they change when you switch roles, and why a role can rank the three dimensions in a different order than the composite does.
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 interactive tool above. The same tensor scores produce a different role-match score because each role weights the dimensions differently when it asks how well a candidate fits. 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, the three dimensions respond differently on purpose: Talent doesn't decay at all - demonstrated aptitude is durable, so absence never erodes it; Training fades (180-day half-life), because knowledge goes stale without practice; Temperament fades fastest (30 days), because reliability has to be shown freshly. You don't “lose” trust overnight - the parts that fade do so gradually, and the part that measures raw ability doesn't fade with time at all.What “half-life” means here: the time it takes to lose half the score with zero activity. A 180-day Training half-life means after six months of no practice, a score of 0.80 settles at 0.40; after a year, 0.20. Decay is exponential, not a cliff - and it applies only to Training and Temperament.
Character has to be shown fresh each month; raw ability, once demonstrated, stays earned. That gap is the point - yesterday's kindness doesn't excuse today's betrayal, but a surgeon doesn't forget surgery over a long vacation. So Temperament (30d) weighs recent behavior far more than old, Training (180d) fades as knowledge goes unpracticed, and Talent never fades through absence at all. 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: the aptitude (Talent) is still there, but the current, practiced knowledge (Training) has faded.
What if you take a 6-month break? Temperament (30-day half-life) is essentially gone and Training (180-day half-life) is at about half - but Talent doesn't decay: it's exactly where you left it. A few weeks of consistent activity rebuilds what faded. Full breakdown in the FAQ →
Can decay alone push you below the survival line?
A fair question to ask at this point, because the site tells you elsewhere that raw trust which falls below 0.5 and stays there is fatal, and a long absence is a sustained condition by its nature. If you can do the arithmetic above, you can reach that question yourself, so here is where it stands.
First, these rates are ours, not the protocol's. The standard fixes exactly one thing here: Talent must not decay through inactivity. The Training and Temperament half-lives on this page are 4-Life's teaching calibration, and the standard leaves those to each society, which may run them faster, slower, or with a grace period before any decay starts at all. How far a long absence actually moves you is a property of the community you are in, not a fact about Web4.
Second, what happens at the line is not settled. The standard names what stops you acting (ATP reaches zero) and what is permanent (sustained trust collapse). It does not say whether that second rule is meant to read a score that fell through absence rather than through behavior, and it does not settle whether a dormancy carve-out applies to someone who is simply away. We would rather tell you that than guess: this page will not invent a rule the standard does not contain. The question is filed with the standard's maintainers. How the two deaths differ →
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 - and the two aren't really independent: consistently producing high-V3 work raises your own T3, while sloppy work drags it down, so your reputation tracks your actual output quality.
Continue to V3: the Value TensorReal Example: Same Person, Different Roles
Alice has spent years as a data analyst and recently started managing projects. Her T3 tensors reflect this asymmetry:
Each role gets its own tensor, and every tensor collapses with the same canonical weights: Talent 0.4, Training 0.3, Temperament 0.3. So the three totals below differ because Alice's scores differ by role, not because the blend does. (The percentages in the widget above are the other quantity, the per-role match. The widget also offers a different set of roles than the three worked out here: you can try any role up there, these are simply the three with the arithmetic written out.)
Alice as Data Analyst
Talent: 85% (creative problem-solver)
Training: 90% (years of deep experience)
Temperament: 95% (rock-solid reliability)
Composite T3: 90% - she's deeply trusted in this domain.
0.4(85) + 0.3(90) + 0.3(95) = 89.5
Alice as Project Manager
Talent: 65% (developing leadership instincts)
Training: 70% (some PM experience)
Temperament: 91% (a separate score for this role, not her 95% as an analyst)
Composite T3: 74% - trusted, but still growing into this role.
0.4(65) + 0.3(70) + 0.3(91) = 74.3
Alice as Mechanic
Talent: 20% (no mechanical aptitude)
Training: 15% (no relevant training)
Temperament: 50% (untested in this context)
Composite T3: 28% - would you let her fix your brakes?
0.4(20) + 0.3(15) + 0.3(50) = 27.5
What this example leaves out: attestation is how Alice earns these three scores, but it is not what sets the limit on the composite number they combine into. That limit comes from how the identity is anchored, with chip class setting the maximum and device count setting how much of the maximum is reached. Alice's 90% as an analyst sits at the top of that range, which takes a hardware-anchored identity plus three device witnesses; a software-only identity tops out at 0.50 however good the work. What sets your trust ceiling →
This is the power of role-specific trust. Each of those composites is Alice's score in one role. One trust score for Alice across all roles would average 90, 74 and 28 into something like 64, which describes none of them. T3 keeps the roles separate so societies can make informed decisions.
If you have already met the survival rule: it is stated on “the composite”, and this page has just shown you three of them. Every composite here is a composite in a role. Trust in Web4 is never one universal number, and the standard is explicit about it: there is no global reputation, only reputation inside a specific role context. So the 28% is Alice's composite as a Mechanic. It is not a second, whole-person score for her, and it does not pull down the 90% she holds as an analyst. What a sustained low score costs you in the role you earned it in, and how far that reaches, is taken up where the rule is stated. The two ways a life ends →