Assistant Professor

近年来，计算机科学和经济学在思想和研究目标上出现了趋同。计算机科学促进了一种新型计算平台的发展，这种平台需要经济和工程思维来进行适当的设计和研究；主要的例子是那些由互联网创建和启用的系统。经济学带来了有助于分析这些系统的量化模型和工具。与此同时，很明显，这些模型和工具的计算性质对于它们用于研究具有数千甚至数百万相互作用的个人的系统至关重要。在这一认识的推动下，计算机科学家在过去十年里对博弈论和经济学进行了全新的、计算性的研究。一个主要的关注点是算法机制设计，在这个过程中，我们寻求计算效率高的系统，这些系统设计得如此巧妙，以至于用户的自私行为有助于推进设计者的目标。这项建议的目标是从两个方向丰富算法机制设计--在PI看来--这两个方向在文献中没有得到充分的探索。我们的目标是(I)了解拍卖的复杂性如何影响其性能，以及(Ii)将算法机制设计应用于拍卖以外的系统。*到目前为止，算法机制设计主要关注各种目标的最优拍卖设计。然而，最优拍卖通常是复杂的，因此在实际中很难实现。本提案的前半部分旨在了解拍卖的复杂性如何影响其绩效，并提出具有可证明的绩效保证的新的简单实用的拍卖。我将使用理论计算机科学和应用概率的工具(1)在某些基本和基本的多项目多投标人设置中搜索简单和(接近)最优拍卖，(2)确定最优拍卖具有简单格式的设置，以及(3)研究在简单的非真实拍卖博弈中为组合拍卖问题寻找贝叶斯-纳什均衡的计算复杂性。该提案的第二部分旨在将算法机制设计的适用范围扩大到拍卖以外的环境。我将引入经济学的概念、思想和技术，(1)设计数据科学的机制-激励工作人员以低成本为统计估计提供高质量的数据，(2)设计计算高效的公平资源分配机制。**

Recent years have seen a convergence of ideas and research goals between Computer Science and Economics. Computer Science enabled the development of a new genre of computational platforms that require economic as well as engineering thinking for their proper design and study; primary examples are those systems created and enabled by the Internet. Economics brought to the discussion quantitative models and tools useful to analyze these systems. At the same time, it became clear that the computational nature of these models and tools is crucial for them to be used to study systems with thousands or even millions of interacting individuals. Motivated by this realization, computer scientists have taken in the past decade a fresh, computational look at Game Theory and Economics. One major focus is Algorithmic Mechanism Design, in which we seek for computational efficient systems that are so cleverly designed that users' selfish behavior helps advance the designer's objectives. The goal of this proposal is to enrich Algorithmic Mechanism Design from two directions that - to the PI's opinion - has not been sufficiently explored in the literature. We aim (i) to understand how the complexity of an auction affects its performance, and (ii) to apply Algorithmic Mechanism Design to systems beyond auctions.******Algorithmic Mechanism Design has mainly focused on optimal auction design for various objectives so far. However, the optimal auction is usually complicated and thus hard to implement in practice. The first half of this proposal aims to understand how the complexity of an auction affects its performance, and propose new simple practical auctions with provable performance guarantees. I will employ tools from Theoretical Computer Science and Applied Probability (1) to search for simple and (nearly-) optimal auctions in certain basic and fundamental multi-item multi-bidder settings, (2) to identify settings where the optimal auctions have simple formats, and (3) to study the computational complexity of finding a Bayes-Nash equilibrium in simple non-truthful auction games for the combinatorial auction problem. The second part of this proposal aims to expand the applicability of Algorithmic Mechanism Design to settings beyond auctions. I will import concepts, ideas and techniques from Economics (1) to design mechanisms for data science - incentivizing workers to provide high quality data for statistical estimation with low cost, and (2) to design computational efficient mechanism for fair resource allocation. **