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Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study
ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 ; : 3592-3602, 2023.
Article in English | Scopus | ID: covidwho-20244490
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. © 2023 ACM.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 Year: 2023 Document Type: Article