CAREER: Machine Learning through the Lens of Economics (And Vice Versa)

机器学习（ML）是利用数据和计算资源来获得预测和决策算法的研究，这些算法在存在不确定性的情况下运行良好。用于设计和研究ML算法的技术通常涉及概率、统计和优化的概念和工具;另一方面，经济学的语言明显缺乏。在ML研究文献中，很少遇到边际价格、效用、均衡、风险规避等术语。这一差距很大，掩盖了这样一个现实，即对机器学习的广泛兴趣及其作为一个研究领域的突然增长，可以归因于它在社会许多领域产生经济价值的潜力。这个NSF CAREER项目推进了机器学习与微观经济理论和金融领域之间已经出现的关系。这将开始与数学工具的发展，使学习理论对象和经济抽象之间的语义对应。例如，该项目表明，许多算法可以被视为实现市场经济，其中学习参数与价格相关联，参数更新被视为交易，并且在某些条件下，学习的假设可以被提取为市场出清价格均衡。除了开发这种联系之外，该项目的研究还提出了一些有趣的问题，并探索了几个令人惊讶和新颖的应用，这些应用对计算机科学有更广泛的好处。在几个这样的应用程序源于新的理论联系是：1。为学习和估计任务开发分布式计算的新模型：经济透镜为数据集中任务的分散化提供了一个强大而有效的模型。通过涉及金融支付计划的协作机制，设计用于众包和劳动力分散的新技术：这建立在亚马逊的Mechanical Turk以及Netflix Prize和预测挑战公司Kaggle等平台的成功基础上。开发面向市场的数据经纪和经济高效学习模型：随着信息在市场环境中的交易越来越多，我们的目标是回答诸如“一个数据单元的边际价值是多少？该项目还将开发密歇根预测团队，这是一个以数据科学为重点的项目，用于制定和解决来自密歇根大学内外的预测和学习挑战。该小组主要针对有研究生导师的本科生，团队具有很强的跨学科重点。

Machine Learning (ML) is the study of leveraging data and computational resources to obtain prediction and decision-making algorithms that function well in the presence of uncertainty. The techniques employed to design and study ML algorithms typically involve concepts and tools from probability, statistics, and optimization; the language of economics, on the other hand, is conspicuously absent. It is rare to encounter terms such as marginal price, utility, equilibrium, risk aversion, and such, in the ML research literature. This gap is significant and belies the reality that the broad interest in Machine Learning, and its sudden growth spurt as a research field, can be ascribed to its potential for generating economic value across many segments of society. This NSF CAREER projectadvances an already-emerging relationship between Machine Learning and the fields of microeconomic theory and finance. This will begin with the development of mathematical tools that enable a semantic correspondence between learning-theoretic objects and economic abstractions. For example, the project shows that many algorithms can be viewed as implementing a market economy, where learning parameters are associated with prices, parameter updates are viewed as transactions, and under certain conditions learned hypotheses can be extracted as market-clearing price equilibria. In addition to developing this link, the project research raises a number of intriguing questions and explores several surprising and novel applications with benefits to computer science more broadly. Among several such applications stemming from the new theoretical connections are:1. Developing new models for distributed computing for learning and estimation tasks: The economic lens gives new insights into a robust and effective model for decentralization of data-focused tasks.2. Designing new techniques for crowdsourcing and labor decentralization via collaborative mechanisms involving financial payment schemes: This builds off of the success of platforms like Amazon's Mechanical Turk as well as the Netflix Prize and the prediction challenge company Kaggle.3. Developing a market-oriented model for data brokerage and financially-efficient learning: As information is increasingly traded in market environments, we aim to answer questions such as "what is the marginal value of a unit of data?"The project will also develop the Michigan Prediction Team, a data-science focused program for formulating and solving prediction and learning challenges that develop from across the University of Michigan as well as externally. The group primarily targets undergraduates with graduate student mentors, and Team has a strong interdisciplinary focus.