AF: Medium: Collaborative Research: Econometric Inference and Algorithmic Learning in Games

AF：媒介：协作研究：游戏中的计量经济学推理和算法学习

• 批准号: 1563714
• 负责人: Eva Tardos
• 金额: $ 70.13万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563714&HistoricalAwards=false
• 关键词: AF; Medium; Collaborative; Research; Econometric

Classical work on economic analysis of the interactions of strategic agents starts with players that have valuations for outcomes, such as items or sets of items they may win in an auction, and analyzes equilibria of the resulting game, where players optimize their strategies to improve their outcomes. To empirically test the prediction of such a theory, one needs to recover valuations of the players. Most econometric methods used to recover valuations rely on the assumption that the game is at a stable equilibrium (known as a Nash equilibrium). It is not surprising that such a framework provides a poor fit to the data in changing or new markets. At the same time, there is a growing theoretical literature in algorithmic game theory that allows one to study games where the game is not at a stable equilibrium. The PIs' program focuses on developing a methodology for inference without relying on the standard notions of the stability of outcomes in dynamically changing environments, such as online auctions. The goal of this project is to develop a theory that allows the researchers to take advantage of new dynamic data sets from electronic markets available on the Internet, and using the findings from the data to further the underlying theory. The results of the project are intended to enable to application and development of Data Science tools for analysis and prediction in non-stable and new market settings. This will affect a broad community of empirical researchers such as market analysts, by allowing them to study economic markets that have previously been considered hard or impossible to analyze.The research program is based on using the theoretical results from algorithmic game theory on game outcomes when players use no-regret learning rules and combine these results with econometric techniques that allow one to estimate the best responses of players from the data using a set of non-parametric estimation techniques. The goal of the program, which PIs initiated in a paper in the ACM Conference on Economics and Computation in 2014, is to combine these approaches to develop a set of analytic tools for empirical analysis of games in non-equilibrium settings. Algorithmic game theory helps one to characterize the properties of outcomes in games (such as approximating factors for revenue and welfare in various cases), where the game is not at a stable equilibrium, assuming players use strategies that guarantee a certain no-regret property in place of the stronger equilibrium best response assumption. The project is aimed at combining the insights from algorithmic game theory with econometric methods to enable the analysis of dynamic markets. The intellectual merit of the project is twofold: (i) providing a methodology for inference in games (i.e., estimation of the payoff functions of players and the distribution of player types) in cases where the players use general classes of learning strategies; (ii) providing tools for the analysis of outcomes in non-equilibrium environments, including the analysis of statistical properties of the outcomes constructed using inferred preferences and types.

经典的经济学研究从对结果有估价的参与者开始，比如他们可能在拍卖中赢得的物品或物品集，并分析最终博弈的均衡，其中参与者优化他们的策略以改善他们的结果。为了实证检验这一理论的预测，我们需要恢复对参与者的估值。大多数用于恢复估值的计量经济学方法都依赖于游戏处于稳定均衡（称为纳什均衡）的假设。毫不奇怪，这样的框架在不断变化的或新的市场中提供了一个很差的数据。与此同时，算法博弈论的理论文献越来越多，它允许人们研究游戏不处于稳定均衡的游戏。PI的项目重点是开发一种推理方法，而不依赖于动态变化环境中结果稳定性的标准概念，例如在线拍卖。该项目的目标是开发一种理论，使研究人员能够利用互联网上电子市场的新的动态数据集，并利用数据中的发现来进一步发展基本理论。该项目的成果旨在使数据科学工具的应用和开发能够在不稳定和新市场环境中进行分析和预测。这将影响到市场分析师等广泛的实证研究人员，通过允许他们研究以前被认为很难或不可能分析的经济市场。该研究计划是基于使用算法博弈论的理论结果对游戏结果时，玩家使用无-后悔学习规则和联合收割机结合这些结果与计量经济学技术，允许一个估计的最佳对策的球员从数据使用一组非-参数估计技术该项目由PI在2014年ACM经济与计算会议上的一篇论文中发起，其目标是将这些方法联合收割机结合起来，开发一套分析工具，用于非均衡环境下的博弈实证分析。博弈论帮助人们描述博弈结果的性质（例如在各种情况下收入和福利的近似因子），其中博弈不是稳定的均衡，假设参与者使用保证一定的无遗憾属性的策略来代替更强的均衡最佳对策假设。该项目旨在将算法博弈论的见解与计量经济学方法相结合，以分析动态市场。该项目的智力价值是双重的：（i）提供一种游戏推理方法（即，估计玩家的收益函数和玩家类型的分布）的情况下，玩家使用一般类的学习策略;（ii）提供工具，用于分析结果在非均衡环境中，包括分析统计属性的结果构建使用推断的偏好和类型。