Learning in repeated auctions
Learning in repeated auctions
复制标题
在重复拍卖中学习
DOI：
10.1561/2200000077
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Vianney Perchet
中科院分区：
文献类型：
--
作者：
Thomas Nedelec;Clément Calauzènes;N. Karoui;Vianney Perchet
拍卖理论历来关注的问题是设计向潜在买家出售单个物品的最佳方式，同时目标是最大化所产生的收入或创造的福利。这些结果依赖于代理之间的一些先前的贝叶斯知识和/或无限的计算能力。所有这些假设在在线广告等新市场中都不再得到满足：类似的商品被重复销售，代理商不可知论并试图互相操纵。另一方面，从长远来看，统计学习理论现在提供了工具来补充那些缺失的假设，因为代理能够从环境中学习来改进他们的策略。这项调查涵盖了重复拍卖学习的最新进展，从贝叶斯先验的最优一次性拍卖的传统经济研究开始。然后，我们将重点关注从投标人过去价值的数据集中学习这些机制的问题。将研究样本复杂性以及不同方法的计算效率。我们还将调查在线变体，其中收集这些数据需要集成成本（“边学习边赚钱”）。第二步，我们将进一步假设投标人在与同一卖家反复互动时也会适应该机制。我们将展示战略代理人如何实际操纵重复拍卖，以达到自己的优势。在这项独立调查的最后（提供了所使用的不同技术的提醒），我们将描述重复拍卖研究的新的有趣方向。
Auction theory historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing the revenue generated or the welfare created. Those results relied on some prior Bayesian knowledge agents have on each-other and/or on infinite computational power. All those assumptions are no longer satisfied in new markets such as online advertisement: similar items are sold repeatedly, agents are agnostic and try to manipulate each-other. On the other hand, statistical learning theory now provides tools to supplement those missing assumptions in the long run, as agents are able to learn from their environment to improve their strategies. This survey covers the recent advances of learning in repeated auctions, starting from the traditional economical study of optimal one-shot auctions with a Bayesian prior. We will then focus on the question of learning these mechanism from a dataset of the past values of bidders. The sample complexity as well as the computational efficiency of different methods will be studied. We will also investigate online variants where gathering those data has a cost to be integrated ("earning while learning"). In a second step, we will further assume that bidders are also adaptive to the mechanism as they interact repeatedly with the same seller. We will show how strategic agents can actually manipulate repeated auctions, at their own advantage. At the end of this stand-alone survey (reminders of the different techniques used are provided), we will describe new interesting direction of research on repeated auctions.
DOI：
10.1145/3219166.3219233
发表时间：
2017-11
期刊：
Proceedings of the 2018 ACM Conference on Economics and Computation
影响因子：
--
作者：
M. Braverman;Jieming Mao;Jon Schneider;Matt Weinberg
通讯作者：
M. Braverman;Jieming Mao;Jon Schneider;Matt Weinberg
动态激励感知学习：关联拍卖中的稳健定价
影响因子：
2.7
作者：
Negin Golrezaei, Adel Javanmard
通讯作者：
Negin Golrezaei, Adel Javanmard
不同买家行为下的稳健重复拍卖
DOI：
--
发表时间：
2018
期刊：
19th ACM conference on Economics and Computation
影响因子：
--
作者：
Agrawal, Shipra;Daskalakis, Constantinos;Mirrokni, Vahab;Sivan, Balasubramanian
通讯作者：
Sivan, Balasubramanian