Equitable Marketplace Mechanism Design
Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch

TL;DR
This paper introduces a reinforcement learning approach to design dynamic, equitable, and profitable marketplace fee schedules that adapt to diverse trader strategies, demonstrated through a simulated stock exchange.
Contribution
It presents a novel RL framework for simultaneously learning adaptable fee schedules and trader strategies to promote equity and profitability in marketplaces.
Findings
The learned fee schedule favors investor classes with higher equitability weights.
The approach balances trader profits and marketplace revenue effectively.
Insights from simulations inform equitable marketplace design principles.
Abstract
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…
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Taxonomy
TopicsAuction Theory and Applications · Economic theories and models · Financial Markets and Investment Strategies
