Data-Driven Market Design

市场是通过有约束力的合同促进商品和服务交换的机构;因此，它们按照规则运行。有时这些规则是有机产生的。当买家和卖家很容易找到对方，当谁做买卖并不重要，当市场产生简单的合同（例如，“我会以2美元的价格卖给你这罐可乐。否则，市场可能更难确定有效的规则（例如，比如“我明天会以60美元的价格把我的空余房间租给你，但你必须在晚上11点后保持安静”）。 当有效市场不是有机地产生时，规则可以明确地制定出来，这是市场设计和机制设计领域所倡导的观点（分别在2012年和2007年获得诺贝尔奖）。目标是证明市场实现了理想的结果（例如，匹配通过交易获得最大收益的买方和卖方），在问题所施加的约束条件下，并在对参与者行为的合理假设下。这些假设是典型的博弈论：粗略地说，参与者完全了解市场如何运作，并在其中采取"理性"行动，以最好地服务于他们的利益。这是一种强有力的方法;它既产生了优雅的一般理论，也产生了影响深远的应用，如搜索引擎关键字拍卖和肾脏交易。它也对人工智能产生了深远的影响，为解决多智能体系统中的信息融合和任务分配等长期挑战提供了实用的理论基础技术。 这种方法有一个关键的缺陷，这在2016年比20世纪中期奠定该领域基础时更加令人震惊。这个缺陷是市场设计几乎完全是一种分析（即，数学）练习：一旦一个人致力于世界的博弈论模型，就几乎没有余地对现实世界的观察作出反应。相比之下，计算机科学目前正在经历一场数据科学革命：我们现在认为计算机系统不是静态的人工制品，而是记住用户交互并适应它们的不断发展的服务。一个系统（自动驾驶汽车、语音识别系统、搜索引擎）与用户交互的数据越多，它就应该工作得越好，这已经成为一个不言而喻的事实。 拟议的研究将有助于市场设计成为这种范式转变的一部分，使市场能够共同利用与用户的实际互动和博弈论分析。更具体地说，它将开发数据敏感技术，用于对市场中的人类行为进行建模，构建启发式清算算法，并分析自适应机制。其结果将是市场设计，可以优化到不同的设置，并可以适应部署后，就像其他现代计算机系统。

Markets are institutions that facilitate the exchange of goods and services via binding contracts; they thus run according to rules. Sometimes these rules arise organically. This can work well when buyers and sellers have little trouble finding each other, when it's not very important who does the buying and selling, and when the market produces simple contracts (e.g., “I'll sell you this can of Coke for $2”). Otherwise, it can be harder for markets to determine effective rules (e.g., to produce contracts like “I'll rent you my spare room tomorrow for $60, but you have to be quiet after 11 PM”). When effective markets do not arise organically, rules can instead be crafted explicitly, an idea championed by the fields of market design and mechanism design (recognized by Nobel prizes in 2012 and 2007 respectively). The goal is to prove that a market achieves desirable outcomes (e.g., matching up buyers and sellers who gain the most by trading) under the constraints imposed by a problem and under reasonable assumptions about the behaviour of participants. These assumptions are typically game theoretic: roughly, that participants are fully informed about how a market works and act “rationally” within it to best serve their interests. This is a powerful approach; it has yielded both elegant, general theory and deeply impactful applications as varied as search-engine keyword auctions and kidney exchanges. It has also had a profound impact on artificial intelligence, providing practical, theoretically grounded techniques for addressing longstanding challenges like information fusion and task allocation in multiagent systems. This approach has a critical flaw, which is more egregious in 2016 than it was when the field's foundations were being laid in the mid-1900s. This flaw is that market design is almost entirely an analytic (i.e., mathematical) exercise: once one has committed to a game theoretic model of the world, there is little room left for responsiveness to real-world observations. In contrast, computer science is currently undergoing a data science revolution: we now think of computer systems not as static artefacts, but as evolving services that remember user interactions and adapt to them. It is becoming a truism that the more data one has about user interactions with a system (a self-driving car; a speech recognition system; a search engine) the better it should work. The proposed research will help market design to become part of this paradigm shift, enabling markets to draw jointly on actual interactions with users and on game theoretic analysis. More specifically, it will develop data-sensitive techniques for modeling human behavior in markets, building heuristic clearing algorithms, and analyzing adaptive mechanisms. The result will be market designs that can be optimized to different settings and that can adapt after being deployed, just like other modern computer systems.