CAREER: Information Elicitation in Algorithmic Economics and Machine Learning

从设计自动驾驶汽车到预测疾病传播，各种各样的重要问题都依赖于做出和评估预测。这些问题反过来又依赖于损失函数，损失函数是测量预测精度的数学工具。设计良好的损失函数可以指导计算机和人类做出准确的预测。在人工智能的一个分支机器学习中，计算机通过选择最适合历史数据的预测模型来进行预测，并根据损失函数进行判断。然而，对于许多常见的机器学习任务，缺乏设计和分析损失函数的通用框架。在经济学中，高效的信息经济对于促进数据、预测和其他信息的交易至关重要。不幸的是，目前的经济机制不适合竞争和合作的环境，而竞争和合作对健康的经济至关重要。该项目将通过(a)开发一个通用框架来设计和分析机器学习中的损失函数，以及(b)设计协作和竞争机制来促进预测、数据等市场，来解决这些缺点。随着我们的社会继续向信息经济发展，这些结果将影响许多关键的经济部门。在监督机器学习中，算法使用代理损失函数，它比目标损失更容易优化，但仍然解决相同的问题。尽管它们的流行和重要性，文献缺乏一个总体框架来系统地设计和分析替代损失。这种框架在结构化预测设置中尤其缺乏，例如在计算机视觉、自然语言处理和生物信息学中，人们试图预测像树或序列这样的对象。利用经济学的思想，该项目将开发一个新的框架来研究和设计凸替代损失，有可能获得更清晰、更普遍的结果。利用离散凸几何的技术，这个框架很容易应用于多面体（分段线性凸）损失，这是结构化预测中很流行的一类损失。在算法经济学中，以信息交换货币的信息激发机制是信息经济的一个有希望的基础。该项目将研究各种新机制，并对现有机制进行新的分析，以提高效率和奖励相容性。这些设置包括机器学习竞赛、预测竞赛和为一般合作项目提供资金的新机制。这些设置解决了关于多智能体诱导机制的几个核心问题，其中一些问题已经开放了十多年。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A wide variety of important problems, from designing self-driving cars to forecasting the spread of a disease, rely on making and evaluating predictions. These problems in turn rely on loss functions, which are mathematical tools to measure prediction (in)accuracy. Well-designed loss functions can guide both computers and humans in making accurate predictions. In machine learning, a branch of artificial intelligence, computers make predictions by choosing the predictive model that best fits historical data, as judged by a loss function. Yet a general framework to design and analyze loss functions is lacking for many common machine learning tasks. In economics, an efficient information economy is crucial to facilitate the trade of data, predictions, and other information. Unfortunately, current economic mechanisms fall short for settings involving competition and collaboration, which are vitally important to a healthy economy. This project will to address these shortcomings, by (a) developing a general framework to design and analyze loss functions in machine learning, and (b) designing collaborative and competitive mechanisms to facilitate markets for predictions, data, and beyond. These results will impact a number of key economic sectors as our society continues to progress toward an information economy.In supervised machine learning, algorithms employ surrogate loss functions, which are easier to optimize than the target loss but still solve the same problem. Despite their prevalence and importance, the literature lacks a general framework to systematically design and analyze surrogate losses. Such a framework is especially lacking in structured prediction settings, as in computer vision, natural language processing, and bioinformatics, where one tries to predict an object like a tree or sequence. Using ideas from economics, the project will develop a new framework to study and design convex surrogate losses, with the potential for sharper and more general results. Using techniques from discrete convex geometry, this framework readily applies to polyhedral (piecewise linear convex) losses, a popular class in structured prediction. In algorithmic economics, information elicitation mechanisms, which exchange information for money, are a promising foundation for an information economy. The project will study a variety of new mechanisms, and develop new analyses of existing mechanisms, to increase efficiency and incentive-compatibility. Among these settings are machine learning competitions, forecasting competitions, and a new mechanism to fund general collaborative projects. These settings address several central questions about multi-agent elicitation mechanisms, some of which have been open for over a decade.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.