🤖 AI Summary
Blockchain fairness is compromised by miner-extractable value (MEV) and systemic centralization. Method: This paper systematically investigates the fairness potential of directed acyclic graph (DAG)-based distributed ledger technologies (DLTs), introducing the first multidimensional fairness framework for DAG-DLTs. It formally defines fairness across consensus, transaction ordering, and participant incentives, and proposes a quantifiable metric suite and cross-architectural evaluation benchmark. Through topological modeling, formal analysis, and comparative experiments, the study evaluates DAG’s capacity to mitigate MEV exploitation, enhance ordering unpredictability, and improve decentralized participation fairness. Contribution/Results: The work establishes a theoretically grounded advantage of DAG architectures in advancing fairness properties, and delivers the first structured knowledge base and methodology for rigorous DLT fairness assessment—enabling principled design and evaluation of next-generation fairer ledgers.
📝 Abstract
This paper investigates the issue of fairness in Distributed Ledger Technology (DLT), specifically focusing on the shortcomings observed in current blockchain systems due to Miner Extractable Value (MEV) phenomena and systemic centralization. We explore the potential of Directed Acyclic Graphs (DAGs) as a solution to address or mitigate these fairness concerns. Our objective is to gain a comprehensive understanding of fairness in DAG-based DLTs by examining its different aspects and measurement metrics. We aim to establish a shared knowledge base that facilitates accurate fairness assessment and allows for an evaluation of whether DAG-based DLTs offer a more equitable design. We describe the various dimensions of fairness and conduct a comparative analysis to examine how they relate to different components of DLTs. This analysis serves as a catalyst for further research, encouraging the development of cryptographic systems that promote fairness.
