Briefing
This research addresses the critical problem of inefficiencies in blockchain-based order book systems, where miners’ self-interested behavior, prioritizing high-fee transactions, significantly diminishes overall social welfare. It introduces a foundational breakthrough ∞ an adjustable block size mechanism, meticulously designed through game-theoretic analysis, that dynamically optimizes block capacity to align individual miner incentives with collective market efficiency. This new theory implies a future of blockchain architecture where transaction ordering can be strategically managed to prevent substantial value extraction by miners, thereby fostering more equitable and robust decentralized financial markets.
Context
Prior to this research, blockchain-based order book systems faced an inherent theoretical limitation ∞ the myopic behavior of miners. These miners, acting rationally to maximize their immediate rewards, would prioritize transactions solely based on their associated fees, often overlooking more socially beneficial transaction matches. Existing mechanism designs inadequately accounted for this self-interested tendency, leading to substantial, and often unquantified, social welfare losses within these decentralized marketplaces.
Analysis
The paper’s core mechanism models blockchain order book systems as a dynamic game involving buyers, sellers, and miners. It reveals that the strategic interactions, particularly miners’ selfish transaction matching, can lead to an infinitely large Price of Anarchy (PoA), signifying severe social welfare degradation. To counteract this, the research proposes an adjustable block size (ABS) mechanism.
This new primitive allows a system designer to dynamically alter the block size, thereby influencing the incentive landscape for miners. The ABS mechanism fundamentally differs from previous approaches by directly integrating a flexible block capacity as a strategic lever, enabling the system to achieve a social optimum for homogeneous-quantity trading and significantly improve welfare for heterogeneous-quantity trading, all without mandating alterations to core decentralized protocols.
Parameters
Outlook
This research opens new avenues for enhancing the efficiency and fairness of decentralized exchanges and other blockchain-based marketplaces. The next steps in this area will likely involve exploring adaptive algorithms for dynamic block size adjustment that can operate with limited information asymmetry, potentially leveraging machine learning to predict market conditions and miner behavior. In 3-5 years, this theory could unlock real-world applications such as self-optimizing blockchain networks that dynamically respond to market demands, leading to more stable and predictable transaction costs, and ultimately, a more robust foundation for decentralized finance.
