ICES: Small: Collaborative Research: Interaction, Information and Identification

ICES：小型：协作研究：交互、信息和识别

• 批准号: 1215814
• 负责人: Stephen Morris
• 金额: $ 20万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1215814&HistoricalAwards=false
• 关键词: ICES; Small; Collaborative; Research; Interaction

Private and widely dispersed information across individuals is pervasive aspect of economic environments. In financial markets, traders possess different information about the value of an asset; In labor markets, employees and employers have different information about the cost of a job. In the interaction of the individual agents, the nature of the private information held by the individuals, influences the outcome of the entire economy. In particular, the structure of the interaction, the form of the network, determines the aggregate outcome. Yet, from the point of view of an observer, be it as an analyst or a regulator or a legislator, the precise nature of the private information and network structure of the agents is rarely known. The current research develops a method to analyze the behavior and the welfare in an economic environment, online as well as offline, independent of the structure of the private information and network structure of the agents. Concurrently, the PIs analyze the extent to which the structure of the economic environments (payoffs, preferences) can be identified from the empirical data without knowledge of the exact information and network structure. The methods have epistemic and computational advantages over earlier approaches. The PIs identify bounds on the statistical moments of the equilibrium distribution. Conversely, the statistical description of the equilibrium outcome allows the PIs to give precise bounds on how much can be learned from the data when the nature of the private information is unknown. This new approach to networks with private information offers techniques to analyze the welfare implications of economic rules and institutions by providing bounds that are independent of the knowledge of the information and network structure. The method allows the PIs to assess the implication of different policies and regulations regarding information disclosure, trading restrictions, reserve requirements, that are independent of the specific information held by the economic agents.

个人之间的私人和广泛分散的信息是经济环境的普遍方面。在金融市场上，交易者对一项资产的价值拥有不同的信息；在劳动力市场上，雇员和雇主对一份工作的成本有不同的了解。在个体行动者的互动中，个体所持有的私人信息的性质影响着整个经济的结果。特别是，互动的结构，网络的形式，决定了聚合的结果。然而，从一个观察者的角度来看，无论是作为一个分析师或监管者或立法者，私人信息和代理人的网络结构的确切性质是很少知道的。本研究发展了一种独立于个体私有信息结构和网络结构的经济环境下，在线和离线的行为和福利分析方法。同时，pi分析了在不知道确切信息和网络结构的情况下，从经验数据中识别经济环境结构（收益、偏好）的程度。与早期的方法相比，这些方法具有认知和计算方面的优势。pi确定了平衡分布的统计矩的界。相反，均衡结果的统计描述允许pi给出了当私有信息的性质未知时可以从数据中学到多少的精确界限。这种研究私人信息网络的新方法提供了分析经济规则和制度的福利含义的技术，方法是提供独立于信息和网络结构知识的边界。该方法允许pi评估有关信息披露、交易限制、准备金要求的不同政策和法规的含义，这些政策和法规独立于经济主体所持有的具体信息。