In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal coalition welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.

翻译：在在线广告市场中，越来越多的广告主委托竞价代理参与广告拍卖。这些代理专门设计在线算法并代表客户进行出价。通常情况下，代理掌握着多位广告主的信息，因此她有可能通过协调出价，帮助客户获得比独立出价更高的效用。本文研究了预算约束下重复第二价格拍卖中的协同在线出价算法。我们提出的算法能够确保每位客户获得比独立出价时最优策略更高的效用。通过分析对称情形下广告主虚报预算的动机，我们证明这些算法能实现联盟福利最大化。我们的证明结合了在线学习与均衡分析技术，克服了与多维基准竞争的技术难点。基于合成数据与真实数据的实验进一步验证了算法的性能。据我们所知，这是首次在带约束的在线重复拍卖中考虑竞价者协同问题。