A Mean Field Game Model of Staking Systemand A Reinforcement Learning Framework for Parameter Optimization
Jinyan Guo, Qevan Guo, Chenchen Mou, Jingguo Zhang · Research Square · 2024
Abstract In this paper, we present a Mean Field Game (MFG) approach to model thestaking system in crypto industry and propose a reinforcement learning frame-work for parameter optimization. Under log utility, we derive the optimal stakingstrategy for miners. Then we develop the dynamics of staking reward rate andstaking ratio using the MFG fixed point condition. Based on our MFG model, wepropose a reinforcement learning framework to optimally decide the inflation rateof the staking system, aiming to increase the staking ratio or market cap of theblockchain project. We provide a few numerical experiments incorporating realstatistical data from IoTeX to validate our approach. Our proposed model andframework offer a brand new robust method for parameter optimization in thestaking system, contributing to the fields of tokenomics design in crypto industry.