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Learning to Mitigate AI Collusion on E-Commerce Platforms
Eric Mibuari · Gianluca Brero · David Parkes · Nicolas Lepore

Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion, where reinforcement learning algorithms learn to set collusive prices in a decentralized manner and through nothing more than profit feedback. We demonstrate that reinforcement learning (RL) can also be used by platforms to learn buy box rules that are effective in preventing collusion by RL sellers and to do so without reducing consumer choice. For this, we adopt the methodology of Stackelberg POMDPs, and demonstrate success in learning robust rules that continue to provide high consumer welfare together with sellers employing different behavior models or having out-of-distribution costs for goods.

Author Information

Eric Mibuari (Harvard University)
Gianluca Brero (Brown University)
David Parkes (Harvard University)

David C. Parkes is Gordon McKay Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He was the recipient of the NSF Career Award, the Alfred P. Sloan Fellowship, the Thouron Scholarship and the Harvard University Roslyn Abramson Award for Teaching. Parkes received his Ph.D. degree in Computer and Information Science from the University of Pennsylvania in 2001, and an M.Eng. (First class) in Engineering and Computing Science from Oxford University in 1995. At Harvard, Parkes leads the EconCS group and teaches classes in artificial intelligence, optimization, and topics at the intersection between computer science and economics. Parkes has served as Program Chair of ACM EC’07 and AAMAS’08 and General Chair of ACM EC’10, served on the editorial board of Journal of Artificial Intelligence Research, and currently serves as Editor of Games and Economic Behavior and on the boards of Journal of Autonomous Agents and Multi-agent Systems and INFORMS Journal of Computing. His research interests include computational mechanism design, electronic commerce, stochastic optimization, preference elicitation, market design, bounded rationality, computational social choice, networks and incentives, multi-agent systems, crowd-sourcing and social computing.

Nicolas Lepore (Meta)

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