Reinforcement Learning for Equitable Marketplace Fee Design

Summary

This document describes a reinforcement learning framework for designing marketplace fees while accounting for traders who adapt their strategies in response to those fees. The framework jointly learns a fee schedule and trader strategies, balancing profitability with an explicit equitability objective across trader classes.

The authors illustrate the approach in a simulated stock exchange with market makers and consumer investors. In the simulation, changing the relative weights assigned to investor classes shifts the learned fee schedule toward the class given greater weight. The example shows how equity goals can influence marketplace pricing alongside the operator’s revenue and participants’ profits. Its evidence is limited to the described simulation; the document does not report real-market testing or establish how the framework performs across other marketplace settings.

Key ideas

• Marketplace fees can shape trader behavior because participants adapt their strategies to the fee schedule.
• The framework learns trader strategies and marketplace fees together.
• A weighted objective balances profits with equitable outcomes across trader classes.
• In a simulated exchange, higher equity weight for a class leads the fee schedule to favor that class.

Tags

# Equitable Marketplace Mechanism Design


# Equitable Marketplace Mechanism Design









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.