T3/V3 Mechanics

Trust in Web4Web4Open governance ontology for trust-native entity interactionsis not a single number. It is three dimensions — Talent, Training, Temperament — each with different decay characteristics, each updated from observed outcomes, each scoped to a specific role.

Asymmetric decay is the key mechanic. Talent decays slowly: aptitude is relatively stable. If you were a skilled code reviewer last month, you probably still are. Training decays without practice: skills atrophy. A certification from five years ago with no recent activity is worth less. Temperament recovers with consistency: reliability is re-earned through sustained good behavior, not declared.

Trust is outcome-based, not declared. No entity can assert its own trust score. Trust is updated when observed outcomes are recorded by witnesses. A successful code review increases the reviewer's T3 in that role. A missed deadline decreases it. The asymmetry means that losing trust is faster than gaining it — one failure costs more than one success earns.

Trust is role-contextual. Your T3 as a code reviewer is completely independent of your T3 as a database administrator. Excellence in one domain does not transfer to another. This prevents halo effects and forces trust to be earned in each context where it is exercised.

Concrete example: An AIArtificial IntelligenceSystems that learn, adapt, and act with real-world impactagent is deployed with T3(0, 0, 0) for the “production-deployer” role. It has no trust. It can only operate in sandbox mode. Over 50 successful sandbox deployments witnessed by the CI system, its Training score rises to 0.6. Its Temperament builds to 0.5 through consistent behavior. It gains access to staging. After 200 more successful deployments with zero rollbacks, it reaches T3(0.4, 0.8, 0.7) — enough for production deployment with human co-sign. The trust profile shapes what it's allowed to do at every step.