论文标题
随机仿射拍卖的可微分经济学
Differentiable Economics for Randomized Affine Maximizer Auctions
论文作者
论文摘要
自动化机制设计的最新方法，即可区分的经济学，代表了通过富功能近似器的拍卖，并通过梯度下降来优化其性能。可区分经济学的理想拍卖架构将是完全策略性的，支持多个竞标者和项目，并且足以代表最佳（即最大化最大化）机制。到目前为止，这种架构还不存在。有一些单品牌方法（Menunet，Rochetnet）始终具有策略性，并且可以代表最佳机制。遗憾是多价出现的，可以近似任何机制，但仅是策略性的。我们提出了一个支持多个投标人并且具有完全策略性的体系结构，但不一定代表最佳机制。该体系结构是经典的仿射最大化拍卖（AMA），经过修改以提供彩票。通过使用基于梯度的优化工具，我们现在可以训练彩票AMA，与收入中的先前方法竞争或超越先前的方法。
A recent approach to automated mechanism design, differentiable economics, represents auctions by rich function approximators and optimizes their performance by gradient descent. The ideal auction architecture for differentiable economics would be perfectly strategyproof, support multiple bidders and items, and be rich enough to represent the optimal (i.e. revenue-maximizing) mechanism. So far, such an architecture does not exist. There are single-bidder approaches (MenuNet, RochetNet) which are always strategyproof and can represent optimal mechanisms. RegretNet is multi-bidder and can approximate any mechanism, but is only approximately strategyproof. We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism. This architecture is the classic affine maximizer auction (AMA), modified to offer lotteries. By using the gradient-based optimization tools of differentiable economics, we can now train lottery AMAs, competing with or outperforming prior approaches in revenue.
