This category is for exploring, discussing, and advancing how trust and reputation are computed, modeled, and interpreted within the Intuition ecosystem.
It’s where researchers, builders, and cryptographers come together to rethink reputation — moving beyond single scores toward richer, more honest models of how trust actually works across the Intuition network and $TRUST.
What this category is for
Use this space to:
- Debate trust models — graph-based, multi-dimensional, temporal, gradient
- Explore algorithms like EigenTrust and their application to decentralized attestation networks
- Design reputation primitives that reflect how humans actually evaluate credibility
- Propose mechanisms for trust decay, contextual scoring, and conviction-weighted signals
How it’s different
While Semantic Engineering focuses on how meaning is structured and Products & Applications covers end-user experiences, Reputation Computation operates at the inference layer — where raw attestations become actionable trust signals. This is where the network decides what the data means about an identity’s credibility.
What belongs here
- Trust graph models and path-based reputation algorithms
- Domain-specific and contextual trust scoring approaches
- Temporal reputation mechanics (decay, half-life, reaffirmation)
- Gradient and continuous trust models vs. binary classification
- Conviction weighting, uncertainty modeling, and belief propagation
- Research on EigenTrust, multi-dimensional reputation, and trust propagation
Why this category matters
A knowledge graph is only as useful as the trust layer that sits on top of it.
By investing in how reputation is computed — not just collected — we ensure Intuition can answer not only “who attested this?” but “how much should I believe it, and why?” Reputation computation is what turns a pile of attestations into a living map of credibility.