🤖 AI Summary
Addressing the trilemma of generality, trustlessness, and Sybil resistance in decentralized systems, this paper proposes MeritRank—a Sybil-tolerant reputation mechanism. Methodologically, it abandons the “strict prevention” paradigm in favor of “tolerance over prevention,” introducing a dual-parameter attenuation framework—transitive decay and connectivity decay—to enable tunable trade-offs between reputation utility and robustness. It constructs a feedback-aggregation algorithm grounded in on-chain interaction graphs and employs an iterative propagation model with decaying weights. Empirical evaluation on real-world MakerDAO data demonstrates that MeritRank significantly reduces Sybil attack gains while preserving high reputation discriminability and cross-context adaptability. The mechanism thus offers a theoretically rigorous yet practically viable pathway for decentralized reputation modeling.
📝 Abstract
Decentralized reputation schemes present a promising area of experimentation in blockchain applications. These solutions aim to overcome the shortcomings of simple monetary incentive mechanisms of naive tokenomics. However, there is a significant research gap regarding the limitations and benefits of such solutions. We formulate these trade-offs as a conjecture on the irreconcilability of three desirable properties of the reputation system in this context. Such a system can not be simultaneously generalizable, trustless, and Sybil resistant. To handle the limitations of this trilemma, we propose MeritRank: Sybil tolerant feedback aggregation mechanism for reputation. Instead of preventing Sybil attacks, our approach successfully bounds the benefits of these attacks. Using a dataset of participants’ interactions in MakerDAO, we run experiments to demonstrate Sybil tolerance of MeritRank. Decay parameters of reputation in MeritRank: transitivity decay and connectivity decay, allow for a fine-tuning of desirable levels of reputation utility and Sybil tolerance in different use contexts.
