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

Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, David Parkes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system.

Original languageAmerican English
Title of host publicationACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
PublisherAssociation for Computing Machinery, Inc
Pages3592-3602
Number of pages11
ISBN (Electronic)9781450394161
DOIs
StatePublished - 30 Apr 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Publication series

NameACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/234/05/23

Bibliographical note

Publisher Copyright:
© 2023 ACM.

Keywords

  • Platform economy
  • agent-based modeling
  • fee setting
  • market shock
  • matching
  • multi-agent simulation
  • reinforcement learning

Fingerprint

Dive into the research topics of 'Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study'. Together they form a unique fingerprint.

Cite this