Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning
CoRR(2024)
摘要
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 have recently shown empirical success in
several other domains. We find that MARL can indeed benefit auction analysis,
but that deploying it effectively is nontrivial. We begin by describing
modelling decisions that keep the resulting game tractable without sacrificing
important features such as imperfect information or asymmetry between bidders.
We also discuss how to navigate pitfalls of various MARL algorithms, how to
overcome challenges in verifying convergence, and how to generate and interpret
multiple equilibria. We illustrate the promise of our resulting approach by
using it to evaluate a specific rule change to a clock auction, finding
substantially different auction outcomes due to complex changes in bidders'
behavior.
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