CAREER: Information Elicitation in Algorithmic Economics and Machine Learning

从自动驾驶汽车的设计到疾病传播的预测，各种各样的重要问题都依赖于预测和评估。这些问题反过来依赖于损失函数，损失函数是衡量预测(In)准确性的数学工具。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，计算机通过选择最符合历史数据的预测模型来进行预测，这是通过损失函数来判断的。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。这个项目将通过(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、数据和其他方面的市场，以解决这些缺点。随着我们的社会继续向信息经济迈进，这些结果将影响到一些关键的经济部门。在有监督的机器学习中，算法使用代理损失函数，该函数比目标损失更容易优化，但仍然解决了相同的问题。尽管它们的普遍性和重要性，文献缺乏一个系统的设计和分析代孕损失的一般框架。这种框架在结构化预测环境中尤其缺乏，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代换损失，有可能得到更尖锐和更普遍的结果。利用离散凸几何的技术，这个框架很容易适用于多面体(分段线性凸)损失，这是结构化预测中的一种流行类别。在算法经济学中，以信息换取金钱的信息诱导机制是信息经济的有前途的基础。该项目将研究各种新机制，并对现有机制进行新的分析，以提高效率和激励兼容性。这些设置包括机器学习竞赛、预测竞赛，以及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启发机制的几个核心问题，其中一些已经公开了十多年。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.