A Curationary Tale: Logarithmic Regret in DeFi Lending via Dynamic Pricing
Tarun Chitra
TL;DR
This work constructs and analyze an online learning model for static and dynamic pricing models within DeFi lending, and shows that when loans are small and have a short duration relative to an observation time, adaptive supply models achieve regret, while static models cannot achieve better than $\Omega(\sqrt{T})$ regret.
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 $Ω(\sqrt{T})$ regret. We then study competitive behavior between curators, demonstrating that adaptive supply mechanisms maximize revenue and welfare for both borrowers and lenders.