We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilitates matching between buyers and sellers. In return, such a marketplace typically charges fees for facilitating trade. The goal of this work is to design a dynamic fee schedule for the marketplace that is equitable and profitable to all traders while being profitable to the marketplace at the same time (from charging fees). Since the traders adapt their strategies to the fee schedule, we present a reinforcement learning framework for simultaneously learning a marketplace fee schedule and trading strategies that adapt to this fee schedule using a weighted optimization objective of profits and equitability. We illustrate the use of the proposed approach in detail on a simulated stock exchange with different types of investors, specifically market makers and consumer investors. As we vary the equitability weights across different investor classes, we see that the learnt exchange fee schedule starts favoring the class of investors with the highest weight. We further discuss the observed insights from the simulated stock exchange in light of the general framework of equitable marketplace mechanism design.
翻译:我们考虑的是由贸易战略和目标各不相同的贸易商组成的贸易市场。市场允许供应商列出其货物清单,便利买方和卖方之间的匹配。反过来,这种市场通常为便利贸易收取费用。这项工作的目的是为所有贸易商设计一个既公平又有利可图的动态市场收费时间表,同时(从收费中)为市场有利可图。由于贸易商根据收费时间表调整其战略,我们提出了一个强化学习框架,以便同时学习市场收费时间表和贸易战略,利用利润和公平性加权优化目标来适应这一收费时间表。我们详细介绍了在模拟证券交易所中与不同类型投资者特别是市场制造者和消费投资者使用拟议办法的情况。随着我们在不同投资者类别之间变更公平权重,我们看到所学的汇率时间表开始有利于具有最高权重的投资者类别。我们进一步根据公平市场机制设计的总体框架,讨论模拟股票交易所观察到的见解。