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.

翻译：理解多单位拍卖中的投标行为对研究人员而言仍是一项持续的挑战。尽管多单位拍卖被广泛应用，但关于常见多单位拍卖中投标行为、收入排名及效率的理论见解仍然有限。本文利用人工智能，特别是强化学习，作为一种无模型学习方法，模拟三种在实践中广泛采用的多单位拍卖中的投标行为。我们引入了六种适用于多单位拍卖学习与投标的算法，并通过一个示例对它们进行了比较。本文强调了在拍卖设计中使用人工智能的重要性，尤其是在优化多单位拍卖设计方面。