Advancing Methods for Analyzing Coordination: New Developments in Global Game Theory.

该奖项资助经济理论研究，重点是协调博弈。这个理论可以用来分析各种各样的经济问题，包括金融危机和商业周期。虽然标准的协调博弈方法提供了对这些设置的洞察，但它允许多种均衡，因此在缺乏均衡选择理论的情况下不能提供完整的理论。因此，全球博弈方法在应用经济学中得到了广泛应用。通过假设有关收益的嘈杂信息，人们可以消除均衡的多样性，并开发出各种各样的预测，然后可以根据数据进行测试。然而，全球游戏方法存在许多重要的局限性。本研究计划的四个组成部分解决了四个不同的限制。这些组件与所有全球游戏应用相关，但它们是在宏观经济学、政治经济学和金融应用的背景下开发的。因此，该项目有助于开发新的理论和方法，帮助个人、企业和各级政府的决策者做出更好的决策。全局博弈分析做出了极端的信息假设：存在关于收益和多重均衡的共同知识，或者小噪声和全局唯一均衡。该项目有四个组成部分，每个部分旨在解决这些假设的一个方面。第一个组成部分检查了更丰富的信息结构类别，以开发关于全球游戏预测何时出现的新预测。全球游戏分析基本上是静态的。第二个组成部分通过学习来研究动态环境，以预测全局游戏预测何时发生。全球游戏分析主要集中在二元动作游戏上。第三部分分析了一个政治经济学应用，其中可行的行动集本身就是一个选择变量，并描述了行动集何时是二元的或不是二元的。全球游戏分析主要依赖于简单的分析解决方案来提供定性见解。第四部分开发了一个金融应用程序，其中包含一组丰富的异质参与者，以便开发可用于实证工作的定量框架。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。

This award funds research in economic theory, with a focus on coordination games. This theory can be used to analyze a wide variety of economic problems, including financial crises and business cycles. While the standard coordination game approach offers insight into these settings, it allows multiple equilibria and thus does not offer a complete theory in the absence of a theory of equilibrium selection. In response, a global games approach has been widely used in applied economics. By assuming noisy information about payoffs, one can remove the multiplicity of equilibria and develop a wide variety of predictions that can then be tested against data. However, there are a number of important limitations to the global games approach. The four components of this research proposal address four different limitations. The components are relevant for all global game applications, but are developed in the context of applications in macroeconomics, political economy and finance. The project therefore contributes to developing new theories and methods that can help individuals, businesses, and policymakers at all levels of government make better decisions. Global game analysis has made extreme informational assumptions: there is common knowledge of payoffs and multiple equilibria, or small noise and globally unique equilibria. The project has four components, each designed to address one aspect of these assumptions. The first component examines richer classes of information structures to develop novel predictions about when global game predictions arise. Global game analysis has been primarily static. The second component examines dynamic environments with learning to develop predictions about when global game predictions occur. Global game analysis has focused on binary action games. The third component analyzes a political economy application where feasible set of available actions is itself a choice variable, and characterizes when the action set is or is not binary. Global game analysis has mostly relied on simple analytic solutions to deliver qualitative insights. The fourth component develops a finance application with a rich set of heterogeneous players in order to develop quantitative framework that that can be used in empirical work.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.