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Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning
Greg d'Eon, Neil Newman, Kevin Leyton-Brown
https://arxiv.org/abs/2402.19420

Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning
Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to understand analytically, making it difficult for bidders to determine how to behave and for designers to optimize auction rules to ensure desirable outcomes such as high revenue or welfare. In this paper, we investigate whether multi-agent reinforcement learning (MARL) algorithms can be used to understand iterative combinatorial auctions, given that these algorithms h…