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

Authors:

Gary Qiurui Ma,

Alexander Trott,

David ParkesAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 3592 - 3602

https://doi.org/10.1145/3543507.3583523

Published: 30 April 2023 Publication History

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.

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Cited By

Dütting PFeng ZNarasimhan HParkes DRavindranath S(2024)Optimal Auctions through Deep Learning: Advances in Differentiable EconomicsJournal of the ACM10.1145/363074971:1(1-53)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3630749
Pan KHu YHan LSun HCheng DLiang YSerra ESpezzano F(2024)Cross-contextual Sequential Optimization via Deep Reinforcement Learning for Algorithmic TradingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680101(4811-4818)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680101
Zhao SYuan XWu RHu ZLiu HWang KHu YLv TFan CTong XHan JZheng YHao JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO EconomiesCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648344(443-452)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648344
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Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Publication History

Published: 30 April 2023

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Additional experiment settings and results. https://dl.acm.org/doi/10.1145/3543507.3583523#Platform_Design_under_Shocks_Supplementary.pdf

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Cited By

Dütting PFeng ZNarasimhan HParkes DRavindranath S(2024)Optimal Auctions through Deep Learning: Advances in Differentiable EconomicsJournal of the ACM10.1145/363074971:1(1-53)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3630749
Pan KHu YHan LSun HCheng DLiang YSerra ESpezzano F(2024)Cross-contextual Sequential Optimization via Deep Reinforcement Learning for Algorithmic TradingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680101(4811-4818)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680101
Zhao SYuan XWu RHu ZLiu HWang KHu YLv TFan CTong XHan JZheng YHao JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO EconomiesCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648344(443-452)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648344
Zhu XShen DWang HHao Y(2024)FCNet: Fully Complex Network for Time Series ForecastingIEEE Internet of Things Journal10.1109/JIOT.2024.344990311:24(40127-40139)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3449903
Teusch JGremmel JKoetsier CJohora FSester MWoisetschläger DMüller J(2023)A Systematic Literature Review on Machine Learning in Shared MobilityIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33343934(870-899)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3334393

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