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Artificial Intelligence for Multi-Unit Auction design

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  • Peyman Khezr
  • Kendall Taylor

Abstract

Understanding bidding behavior in multi-unit auctions remains an ongoing challenge for researchers. Despite their widespread use, theoretical insights into the bidding behavior, revenue ranking, and efficiency of commonly used multi-unit auctions are limited. This paper utilizes artificial intelligence, specifically reinforcement learning, as a model free learning approach to simulate bidding in three prominent multi-unit auctions employed in practice. We introduce six algorithms that are suitable for learning and bidding in multi-unit auctions and compare them using an illustrative example. This paper underscores the significance of using artificial intelligence in auction design, particularly in enhancing the design of multi-unit auctions.

Suggested Citation

  • Peyman Khezr & Kendall Taylor, 2024. "Artificial Intelligence for Multi-Unit Auction design," Papers 2404.15633, arXiv.org, revised Apr 2024.
    • Handle: RePEc:arx:papers:2404.15633
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    File URL: http://arxiv.org/pdf/2404.15633
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