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 investigators will 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 investigators will identify bounds on the statistical moments of the equilibrium distribution. Conversely, the statistical description of the equilibrium outcome allows the investigators 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 investigators to assess the implications of different policies and regulations regarding information disclosure, trading restrictions, and reserve requirements that are independent of the specific information held by the economic agents.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Communication Foundations (CCF)
Type
Standard Grant (Standard)
Application #
1215808
Program Officer
Tracy J. Kimbrel
Project Start
Project End
Budget Start
2012-09-01
Budget End
2016-02-29
Support Year
Fiscal Year
2012
Total Cost
$199,941
Indirect Cost
Name
Yale University
Department
Type
DUNS #
City
New Haven
State
CT
Country
United States
Zip Code
06520