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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Doing Business As Name:Massachusetts Institute of Technology
PD/PI:
  • Stephen E Morris
  • (617) 253-5193
  • semorris@mit.edu
Award Date:07/27/2021
Estimated Total Award Amount: $ 245,721
Funds Obligated to Date: $ 245,721
  • FY 2021=$245,721
Start Date:08/01/2021
End Date:07/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Information and Markets
Federal Award ID Number:2049744
DUNS ID:001425594
Parent DUNS ID:001425594
Program:Economics
Program Officer:
  • Nancy Lutz
  • (703) 292-7280
  • nlutz@nsf.gov

Awardee Location

Street:77 MASSACHUSETTS AVE
City:Cambridge
State:MA
ZIP:02139-4301
County:Cambridge
Country:US
Awardee Cong. District:07

Primary Place of Performance

Organization Name:Massachusetts Institute of Technology
Street:
City:
State:MA
ZIP:02139-4301
County:Cambridge
Country:US
Cong. District:07

Abstract at Time of Award

This project analyzes the impact of information for the structure of markets. The use of information in internet and other markets has been revolutionized in the twenty by developments in technology. Economic institutions are adapting to these changes. At the same time, there have been fundamental improvements in our theoretical understanding of the role of information in the economy, but these new insights are somewhat abstract. Our project brings recent theoretical developments in information economics to bear on key economic questions in the economy. One component of our project addresses the new multi-billion dollar market for internet advertising. Advertisers purchase the right to display advertisements to particular internet users (the display of one advertisement to a particular internet user is known as an "impression". Because internet platforms have detailed information about users, it is possible to target users precisely, i.e., show particular advertisements to internet users with particular characteristics. The market for impressions has become very sophisticated. The research team seeks to understand the gains and losses associated with targeting impressions. This work will be an input into the important policy question of how internet platforms should be regulated. The team's work on the market for impressions will analyze new questions in auction theory. In particular, in the market for impressions, the publisher can use his private information about users to control advertisers' information about the value of particular viewers. Providing more information will increase the efficiency of the allocation. But more information will also reduce competition and give more information rents to the buyer. The team plans to understand the optimal information structure for the publishers and see how the optimal policy translates into selling methods used in practice. A second component of the project will study "Price Discrimination in Competitive Markets". Price discrimination when firms exploit information about consumers to target them with different prices. Price discrimination has been most successfully studied in the context of a monopoly seller. When firms both compete with each other but also price discriminate, the analysis can become intractable. This is a good example of a setting where more insight and tractability can be gained by allowing richer information. The team plans to provide tight bounds on welfare outcomes and price distributions for a given distribution of heterogeneous values of consumers. Finally, they want to improve our understanding of the relation between information and "higher-order beliefs". Higher-order beliefs encode a population's beliefs about the world, their beliefs about others' beliefs and so on. What is the relation between representing information via higher-order beliefs and less structured representations? They will develop the connection, understanding how a many player formalization of "more information" translates into higher-order beliefs and identifying settings where higher-order beliefs can be used to approximate any information structure. 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.

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