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

关于战略代理互动的经济分析的经典工作是从对结果（如他们可能在拍卖中赢得的物品或物品集）进行评估的玩家开始，并分析最终游戏的均衡，在此玩家优化他们的策略以改善他们的结果。为了从经验上检验这一理论的预测，人们需要恢复参与者的估值。大多数用于恢复估值的计量经济学方法都依赖于博弈处于稳定均衡（即纳什均衡）的假设。这种框架不能很好地适应不断变化的市场或新市场中的数据，这并不奇怪。与此同时，在算法博弈论中，有越来越多的理论文献允许人们研究博弈不处于稳定平衡的博弈。pi的项目侧重于开发一种推理方法，而不依赖于动态变化环境（如在线拍卖）中结果稳定性的标准概念。该项目的目标是发展一种理论，使研究人员能够利用互联网上可获得的电子市场的新动态数据集，并利用数据中的发现来进一步深化基础理论。该项目的成果旨在促进数据科学工具的应用和发展，以便在不稳定和新的市场环境中进行分析和预测。这将影响市场分析师等经验研究人员的广泛群体，使他们能够研究以前被认为很难或不可能分析的经济市场。当玩家使用无后悔学习规则时，该研究计划基于使用算法博弈论的理论结果，并将这些结果与计量经济学技术相结合，使人们能够使用一组非参数估计技术从数据中估计玩家的最佳反应。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结合起来，开发一套分析工具，用于对非均衡环境下的博弈进行实证分析。算法博弈论帮助人们描述游戏结果的属性（游戏邦注：例如在各种情况下近似收益和福利的因素），当游戏不是处于稳定的均衡状态时，假设玩家使用的策略是保证某种不后悔的属性，而不是更强的均衡最佳反应假设。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。这个项目的智力价值是双重的：(i)在玩家使用一般学习策略的情况下，为游戏中的推理提供了一种方法（例如，估计玩家的收益函数和玩家类型的分布）；（ii）为分析非均衡环境下的结果提供工具，包括分析使用推断偏好和类型构建的结果的统计特性。

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.