Current reputation mechanisms in Web3 typically fall into three conventional approaches: static Sybil filters that remain unchanged over time, identity-based verification systems relying on user credentials, and snapshot point systems capturing data at fixed intervals.

FairScale introduces a fundamentally different paradigm. Rather than relying on static mechanisms, it operates dynamically, constantly adapting to network conditions. The system shifts focus from identity verification to behavior analysis—evaluating actual on-chain actions rather than user credentials. Most importantly, FairScale functions in real-time, making reputation assessments at the exact moment decisions need to be made, rather than relying on historical snapshots.

This real-time, behavior-driven approach enables more accurate risk assessment and fairer governance outcomes across decentralized protocols and platforms.