Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

TL;DR

This paper develops a simulation framework using reinforcement learning to analyze how economic platforms respond to shocks and regulations, revealing effective policies that enhance system resilience and diversity.

Contribution

It introduces a multi-agent simulation environment and applies deep reinforcement learning to study platform behavior under shocks and regulations, highlighting effective regulatory strategies.

Findings

Fixing pre-shock fees improves system efficiency.

Sophisticated platforms can resist many interventions.

Certain fee regulations promote resilience and diversity.

Abstract

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.