MeritRank: Sybil Tolerant Reputation for Merit-based Tokenomics

TL;DR

MeritRank addresses the challenge of building Sybil-tolerant, merit-based tokenomics in decentralized settings by enhancing feedback aggregation with three decay mechanisms: transitivity, connectivity, and epoch. The approach accepts that full Sybil resistance is unlikely in open systems, and instead bounds the attacker’s gain by attenuating the influence of distant, poorly connected, or aging contributions. Using MakerDAO interaction data, the paper demonstrates that transitivity and connectivity decay substantially reduce attacker gains and preserve informativeness, while epoch decay can be counterproductive. The work provides a practical framework for balancing generalizability and trustlessness with security against Sybil attacks, and sets the stage for further refinement of decay strategies in decentralized reputation. Overall, MeritRank offers a concrete, executable path to safer merit-based tokenomics in DAOs and similar ecosystems.

Abstract

Decentralized reputation systems are emerging as promising mechanisms to enhance the effectiveness of token-based economies. Unlike traditional monetary incentives, these systems reward participants based on the actual value of their contributions to the network. However, the advantages and challenges associated with such systems remain largely unexplored. In this work, we investigate the inherent trade-offs in designing a decentralized reputation system that is simultaneously generalizable, trustless, and Sybil-resistant. Specifically, `generalizable' means that the system can assess various types of contributions across different contexts, `trustless' indicates that it functions without the need for a central authority to oversee reputations, and `Sybil-resistant' refers to its ability to withstand manipulations by fake identities, i.e., Sybil attacks. We propose MeritRank, a Sybil-tolerant reputation system based on feedback aggregation from participants. Instead of entirely preventing Sybil attacks, our approach effectively limits the benefits that attackers can gain from such strategies. This is achieved by reducing the perceived value of the attacker's and Sybil nodes' contributions through the application of decay mechanisms -- specifically, transitivity decay, connectivity decay, and epoch decay. Using a dataset of participant interactions in MakerDAO, we conducted experiments to demonstrate the Sybil tolerance of MeritRank.

Figures (12)

• Figure 1: The “Decentralized Reputation Trilemma” illustrates the irreconcilability of three desirable properties: Generalizable, Sybil-resistant, and Trustless. The triangle’s edges represent approaches that sacrifice one property: Trusted Oracles sacrifice Trustlessness, Cryptographic Proofs sacrifice Generalizability, and Feedback Aggregation sacrifices Sybil resistance.
• Figure 2: Merit-Based Tokenomics system model.
• Figure 3: Example of a feedback graph. Edge weights represent the total feedback assigned by a participant about another participant.
• Figure 4: An example of a Sybil attack on graph $G = (V=\{k,i,...\}, E=\{(k,i), (i,k), ...\}, w)$ with Sybil nodes $S = \{s_0, s_1, s_2\}$, two attack edges $E_a = \{(i,s_0),(k,s_2)\}$ and Sybil edges $E_S = \{(s_0,s_1), (s1,s2), (s2,s_0)\}$.
• Figure 5: Sybil attack strategies. A beneficial Sybil attack is a combination of these three strategies.

Theorems & Definitions (8)

• Definition 3.1: Reputation Score
• Definition 3.2: Allocation score
• Definition 4.1: Sybil Attack
• Definition 4.2: Sybil Attack Strategies
• Definition 4.4: Sybil Tolerance
• Definition 4.5: Parallel Attack Bound
• Definition 4.6: Serial Attack Bound
• Definition 4.7: Bounded Transitivity