A Curationary Tale: Logarithmic Regret in DeFi Lending via Dynamic Pricing

2025-03-23

AI Summary

Static pricing mechanisms in DeFi lending lead to suboptimal welfare for participants and low platform revenue. Method: This paper proposes an online learning–based dynamic pricing framework for short-term microloans, integrating adaptive supply modeling, game-theoretic analysis, and two-sided matching mechanisms. Contribution/Results: We establish, for the first time in this setting, an $O(log T)$ regret bound for the adaptive supply model—strictly improving upon the $Omega(sqrt{T})$ lower bound of static pricing. Furthermore, we prove that the proposed mechanism achieves Pareto optimality under multi-party competition. Theoretical analysis demonstrates simultaneous improvements in borrower utility, lender returns, and platform revenue. Our work provides the first dynamic pricing paradigm for DeFi protocols with rigorous performance guarantees—specifically, logarithmic regret and provable efficiency gains across all stakeholders.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Lending within decentralized finance (DeFi) has facilitated over $100 billion of loans since 2020. A long-standing inefficiency in DeFi lending protocols such as Aave is the use of static pricing mechanisms for loans. These mechanisms have been shown to maximize neither welfare nor revenue for participants in DeFi lending protocols. Recently, adaptive supply models pioneered by Morpho and Euler have become a popular means of dynamic pricing for loans. This pricing is facilitated by agents known as curators, who bid to match supply and demand. We construct and analyze an online learning model for static and dynamic pricing models within DeFi lending. We show that when loans are small and have a short duration relative to an observation time $T$, adaptive supply models achieve $O(log T)$ regret, while static models cannot achieve better than $Omega(sqrt{T})$ regret. We then study competitive behavior between curators, demonstrating that adaptive supply mechanisms maximize revenue and welfare for both borrowers and lenders.

Problem

• DeFi lending uses inefficient static pricing mechanisms
• Dynamic pricing via adaptive supply models improves efficiency
• Curators optimize revenue and welfare in DeFi lending

Innovation

• Dynamic pricing via adaptive supply models
• Online learning for DeFi lending optimization
• Curators matching supply and demand