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雷达卡
摘要翻译：
拍卖是一个有严格规定的市场，规定交易者在市场上可以获得的信息和他们可以采取的可能行动。由于设计良好的拍卖可以获得理想的经济效果，因此它们被广泛应用于解决现实世界的优化问题，以及构建股票或期货交易所。拍卖还为经济理论提供了一个非常有价值的试验场，它们在基于计算机的控制系统中发挥着重要作用。拍卖机制设计的目的是操纵拍卖的规则，以达到特定的目的。经济学家传统上使用数学方法，主要是博弈论，来分析拍卖和设计新的拍卖形式。然而，由于拍卖的高度复杂性，通常对数学模型进行简化以获得结果，这使得从这些模型得到的结果很难应用到现实世界的市场环境中。因此，研究人员转向经验方法。本报告旨在综述设计拍卖机制和交易策略的理论和实证方法，并为该领域的进一步研究奠定基础。
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英文标题：
《An Investigation Report on Auction Mechanism Design》
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作者：
Jinzhong Niu, Simon Parsons
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field.
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PDF链接：
https://arxiv.org/pdf/0904.1258
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