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

战略代理人相互作用的经济学分析的经典工作从对结果有估值的参与者开始，比如他们可能在拍卖中赢得的物品或成套物品，并分析结果博弈的均衡，其中参与者优化他们的策略以改善他们的结果。要对这一理论的预测进行实证检验，需要恢复球员的估值。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(称为纳什均衡)的假设。不足为奇的是，这样的框架与不断变化的或新市场中的数据不太匹配。与此同时，算法博弈论中有越来越多的理论文献，它允许人们研究博弈不处于稳定均衡的博弈。PIS的计划专注于开发一种推理方法，而不依赖于动态变化的环境(如在线拍卖)中结果稳定性的标准概念。这个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可用的电子市场的新的动态数据集，并使用数据的发现来进一步推进基本理论。该项目的成果旨在促进数据科学工具的应用和开发，以便在不稳定和新的市场环境下进行分析和预测。这将影响广泛的实证研究人员，如市场分析师，允许他们研究以前被认为很难或不可能分析的经济市场。该研究计划基于当玩家使用无遗憾学习规则时，使用算法博弈论关于游戏结果的理论结果，并将这些结果与计量经济学技术相结合，允许人们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PIS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。算法博弈论有助于描述博弈结果的性质(例如在各种情况下近似收入和福利的因素)，其中博弈不处于稳定均衡，假设玩家使用保证某种无遗憾性质的策略，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，以实现对动态市场的分析。该项目的智力价值有两重：(I)在玩家使用一般类别的学习策略的情况下，提供一种在游戏中进行推理的方法(即，估计玩家的收益函数和玩家类型的分布)；(Ii)提供工具，用于分析非平衡环境中的结果，包括分析使用推断的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.