  We look at discovering the impact of market microstructure on equitability
for market participants at public exchanges such as the New York Stock Exchange
or NASDAQ. Are these environments equitable venues for low-frequency
participants (such as retail investors)? In particular, can market makers
contribute to equitability for these agents? We use a simulator to assess the
effect a market marker can have on equality of outcomes for consumer or retail
traders by adjusting its parameters. Upon numerically quantifying market
equitability by the entropy of the price returns distribution of consumer
agents, we demonstrate that market makers indeed support equitability and that
a negative correlation is observed between the profits of the market maker and
equitability. We then use multi objective reinforcement learning to
concurrently optimize for the two objectives of consumer agent equitability and
market maker profitability, which leads us to learn policies that facilitate
lower market volatility and tighter spreads for comparable profit levels.

Multiagent Systems