Approximation in mechanism design
Approximation in mechanism design
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DOI：
10.1145/1807406.1807441
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
Jason D. Hartline
中科院分区：
文献类型：
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作者：
Jason D. Hartline
这篇演讲概述了机制设计的三个挑战领域，并描述了近似在解决这些挑战方面所起的作用。挑战1：最优机制是由智能体私有类型分布的知识参数化的。挑战2：最优机制需要精确的分布信息。挑战3：在多维环境中，经济分析未能描述最优机制。近似理论很适合解决这些挑战。虽然最优机制可以通过代理私有类型的分布参数化，但可能存在一种近似于任何分布的最优机制的单一机制。虽然最优机制可能需要精确的分布假设，但可能存在仅取决于分布的自然特征的近似最优机制。虽然多维最优机制可能难以精确的经济表征，但可能存在近似最优机制的简单描述。最后，这些近似最优机制，由于它们的简单性和可追溯性，在实践中更有可能出现，从而使近似最优机制的理论比（精确）最优机制的理论更具描述性。本次讲座将讨论解决这些挑战的积极方法，重点是基本技术、与实践的相关性以及未来的研究方向。
This talk surveys three challenge areas for mechanism design and describes the role approximation plays in resolving them. Challenge 1: optimal mechanisms are parameterized by knowledge of the distribution of agent's private types. Challenge 2: optimal mechanisms require precise distributional information. Challenge 3: in multi-dimensional settings economic analysis has failed to characterize optimal mechanisms. The theory of approximation is well suited to address these challenges. While the optimal mechanism may be parameterized by the distribution of agent's private types, there may be a single mechanism that approximates the optimal mechanism for any distribution. While the optimal mechanism may require precise distributional assumptions, there may be approximately optimal mechanism that depends only on natural characteristics of the distribution. While the multi-dimensional optimal mechanism may resist precise economic characterization, there may be simple description of approximately optimal mechanisms. Finally, these approximately optimal mechanisms, because of their simplicity and tractability, may be much more likely to arise in practice, thus making the theory of approximately optimal mechanism more descriptive than that of (precisely) optimal mechanisms. The talk will cover positive resolutions to these challenges with emphasis on basic techniques, relevance to practice, and future research directions.