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摘要翻译：
我们确定了第一个静态可信的多项目加性拍卖机制，该机制实现了一个常数的最优收益因子。这是设计两部分关税拍卖的更一般框架的一个实例，它采用了Cai等人[CDW16]的二元框架。给定一个（不一定是激励相容的）拍卖形式$a$满足某些技术条件，我们的框架增加了对每个竞拍者的个性化进场费，在进入拍卖之前必须支付这笔费用。这些报名费只取决于投标人类型的先前分布，特别是独立于已实现的投标。我们的框架可以用于许多常见的拍卖格式，如同时第一价格、同时第二价格和同时全付拍卖。如果采用全薪拍卖，我们证明了所得到的机制是可信的，因为拍卖人在观察代理出价后不能因偏离所述机制而受益。如果使用二级价格拍卖，我们获得了一个真实的$O(1)$-近似的机制，具有固定的入门费，可以通过在线学习技术进行调整。我们对第一价格和全部支付的结果是多维环境下非真实机制的第一收益保证；文献中的一个悬而未决的问题[RST17]。
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英文标题：
《Simple, Credible, and Approximately-Optimal Auctions》
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作者：
Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis
Syrgkanis, Santhoshini Velusamy
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最新提交年份：
2020
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Science and Game Theory 计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Economics 经济学
二级分类：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
We identify the first static credible mechanism for multi-item additive auctions that achieves a constant factor of the optimal revenue. This is one instance of a more general framework for designing two-part tariff auctions, adapting the duality framework of Cai et al [CDW16]. Given a (not necessarily incentive compatible) auction format $A$ satisfying certain technical conditions, our framework augments the auction with a personalized entry fee for each bidder, which must be paid before the auction can be accessed. These entry fees depend only on the prior distribution of bidder types, and in particular are independent of realized bids. Our framework can be used with many common auction formats, such as simultaneous first-price, simultaneous second-price, and simultaneous all-pay auctions. If all-pay auctions are used, we prove that the resulting mechanism is credible in the sense that the auctioneer cannot benefit by deviating from the stated mechanism after observing agent bids. If second-price auctions are used, we obtain a truthful $O(1)$-approximate mechanism with fixed entry fees that are amenable to tuning via online learning techniques. Our results for first price and all-pay are the first revenue guarantees of non-truthful mechanisms in multi-dimensional environments; an open question in the literature [RST17].
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PDF链接：
https://arxiv.org/pdf/2002.06702
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