If identity answers who, reputation answers whether to trust. It is the memory of the coordination layer — the record that contribution leaves behind.
Trust is earned, not purchased
Most onchain signals of standing can be bought: a token balance, a rare NFT, a follower count. They measure capital, not contribution. ORYN reputation is different by design — it accrues from what a participant does, over time, and it cannot be acquired in a single payment.
This makes reputation expensive to fake and meaningful to read. An application that asks "can I trust this participant?" gets an answer grounded in history, not in a snapshot of someone's wallet.
Earned, not bought. Reputation reflects contribution accumulated over time and is resistant to purchase, transfer, or sudden inflation.
Reputation is contextual
Trust is not a single number. A participant might be deeply trusted in governance and unknown in design; a prolific contributor to one community and a newcomer to another. ORYN models reputation as contextual — scoped to a domain, a community, or a kind of contribution — rather than collapsing everything into one global score that means nothing everywhere.
Contribution as the source
Reputation is computed from contribution events: shipped work, governance participation, sustained community presence, vouches from already-trusted participants. The system favours consistency over intensity — a steady record outweighs a single burst — and it lets trust decay gently, so that reputation reflects who a participant is now, not only who they once were.
| Accrues from | Contribution, sustained participation, and vouches from trusted peers. |
|---|---|
| Scoped by | Context — domain, community, or kind of work. |
| Resists | Purchase, transfer, and sudden inflation. |
| Decays | Gradually, so it tracks present standing, not only past glory. |
Why it depends on identity and the graph
Reputation attaches to an identity and flows along the graph. Vouches are edges; contexts are clusters; trust can propagate — carefully — between connected participants. This is why reputation is the second primitive: it needs identity beneath it and the graph beside it.
Open questions
- How do we measure contribution without making it gameable?
- What is the right decay function for trust across different contexts?
- How far should reputation propagate along the graph before it becomes noise?
These questions drive Research #002 — Reputation Networks.