NSF award 2614933 · Stanford University

CAREER: Learning Theory for Large-scale Stochastic Games

Final report due

31 May 2030

in 1,321 days · The award ends on 31 January 2030.

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Decide about an extension3 Oct 2029in 1081 days
award ending 2030-01-31 · Research.gov
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Extension request17 Dec 2029in 1156 days
award ending 2030-01-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jan 2030in 1191 days
award ending 2030-01-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due31 May 2030in 1321 days
award ending 2030-01-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due31 May 2030in 1321 days
award ending 2030-01-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out31 May 2030in 1321 days
award ending 2030-01-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 January 2030.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 31 May 2030.

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Award card

Award number: 2614933
Period of support: 1 Feb 2026 – 31 Jan 2030
Amount: $409,625
Awardee: Stanford University, Stanford, CA
Program: APPLIED MATHEMATICS
Directorate: MPS

Abstract

In modern financial markets and economic systems with large populations, decision-making has evolved into a multifaceted process involving various aspects such as population heterogeneity, diverse information structures, and human-AI interactions. This project aims to develop new learning frameworks and mathematical foundations that strengthen our understanding of the stability, efficiency, and fairness of societal…

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