  We study the behavior of an economic platform (e.g., Amazon, Uber Eats,
Instacart) under shocks, such as COVID-19 lockdowns, and the effect of
different regulation considerations imposed on a platform. To this end, we
develop a multi-agent Gym environment of a platform economy in a dynamic,
multi-period setting, with the possible occurrence of economic shocks. Buyers
and sellers are modeled as economically-motivated agents, choosing whether or
not to pay corresponding fees to use the platform. We formulate the platform's
problem as a partially observable Markov decision process, and use deep
reinforcement learning to model its fee setting and matching behavior. We
consider two major types of regulation frameworks: (1) taxation policies and
(2) platform fee restrictions, and offer extensive simulated experiments to
characterize regulatory tradeoffs under optimal platform responses. Our results
show that while many interventions are ineffective with a sophisticated
platform actor, we identify a particular kind of regulation -- fixing fees to
optimal, pre-shock fees while still allowing a platform to choose how to match
buyer demands to sellers -- as promoting the efficiency, seller diversity, and
resilience of the overall economic system.
