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Research Spending & Results

Award Detail

Awardee:REGENTS OF THE UNIVERSITY OF MINNESOTA
Doing Business As Name:University of Minnesota-Twin Cities
PD/PI:
  • Krishnamurthy Iyer
  • (612) 624-2488
  • kriyer@umn.edu
Award Date:11/22/2019
Estimated Total Award Amount: $ 9,314
Funds Obligated to Date: $ 9,314
  • FY 2016=$9,314
Start Date:08/15/2019
End Date:02/28/2021
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Repeated Auctions in Incomplete Information Settings with Learning Bidders
Federal Award ID Number:2002156
DUNS ID:555917996
Parent DUNS ID:117178941
Program:OE Operations Engineering
Program Officer:
  • Georgia-Ann Klutke
  • (703) 292-2443
  • gaklutke@nsf.gov

Awardee Location

Street:200 OAK ST SE
City:Minneapolis
State:MN
ZIP:55455-2070
County:Minneapolis
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:University of Minnesota-Twin Cities
Street:200 OAK ST SE
City:Minneapolis
State:MN
ZIP:55455-2070
County:Minneapolis
Country:US
Cong. District:05

Abstract at Time of Award

Recently, there has been a tremendous growth in the number of electronics markets that cater to a diverse set of participants such as advertisers, retailers, publishers, traders, and freelance workers. A unifying feature of such markets is that the participants do not possess complete information about payoff-relevant market features, which they must learn during their multiple interactions with the market. The goal of this research activity is to study repeated auction markets where the bidders have incomplete information about the items being auctioned, and analyze the resulting dynamic bidding behavior. Such auctions arise in online advertising markets and play a significant role in sustaining a wide range of services available on the Internet. The insights obtained from this research will be used to evaluate and improve the design and operation of such markets. The PI is committed to involving students from underrepresented groups into this research program, and will engage in outreach efforts through various outreach programs at Cornell University. The learning incentives inherent in repeated auction markets require each bidder to evaluate the effects of his or her actions and those of his or her competitors on the future evolution of the market state. In order to understand how a bidder behaves in such settings, this research will first use the tools of dynamic programming to obtain insights about the structure of the bidder's optimal strategy for given fixed strategies of her competitors. Using these structural insights, the researcher will then characterize the resulting equilibrium in the market where each bidder reacts optimally to others. We will develop algorithms based on iterative schemes to approximately compute such equilibria and numerically analyze how the design of the market affects its equilibrium properties. We will use the numerical investigations to identify practical guidelines for improving the design and operation of repeated auction markets, in terms of maximizing its revenue and overall efficiency.

Publications Produced as a Result of this Research

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Yang, Pu and Iyer, Krishnamurthy and Frazier, Peter I. "Information Design in Spatial Resource Competition" Lecture notes in computer science, v., 2019, p.. Citation details  

Anunrojwong, Jerry and Iyer, Krishnamurthy and Lingenbrink, David. "Persuading Risk-Conscious Agents: A Geometric Approach" Lecture notes in computer science, v., 2019, p.. doi:10.1007/978-3-030-35389-6 Citation details  

Lingenbrink, David and Iyer, Krishnamurthy "Optimal Signaling Mechanisms in Unobservable Queues" Operations Research, v.67, 2019, p.. doi:10.1287/opre.2018.1819 Citation details  

Lingenbrink, David Alan "Information Design in Service Systems and Online Markets" ProQuest Dissertations & Theses A&I, v., 2019, p.. Citation details  

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