Strategic Liquidity Provision in Automated Market Makers

Abstract

We conduct a laboratory experiment to study how liquidity providers make strategic decisions in automated market makers (AMMs). Using a controlled environment that replicates key features of Uniswap v3, we examine how concentrated liquidity positions respond to information asymmetry and volatility. Our findings suggest that experimental subjects learn to optimally adjust their price ranges, though significant heterogeneity exists in the speed of learning.