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.

个人之间的私密且广泛分散的信息是经济环境中普遍存在的一个方面。在金融市场中，交易者拥有有关资产价值的不同信息；在劳动力市场上，雇员和雇主对工作成本有不同的信息。在个体主体的互动中，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，交互的结构、网络的形式决定了总体结果。然而，从观察者的角度来看，无论是作为分析师、监管者还是立法者，代理人的私人信息和网络结构的确切性质很少为人所知。当前的研究开发了一种方法来分析在线和离线经济环境中的行为和福利，独立于私人信息结构和代理人的网络结构。同时，PI 分析了在不了解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。与早期方法相比，这些方法具有认知和计算优势。 PI 确定平衡分布统计矩的界限。相反，当私人信息的性质未知时，均衡结果的统计描述允许 PI 给出可以从数据中了解多少的精确界限。 这种带有私人信息的网络新方法通过提供独立于信息和网络结构知识的界限，提供了分析经济规则和制度对福利的影响的技术。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影响，这些政策和法规独立于经济主体持有的具体信息。