Award Abstract # 1847393
CAREER: Harnessing Prediction Engines and Non-Monetary Mechanisms for Real-Time Decision Making

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: CORNELL UNIVERSITY
Initial Amendment Date: February 12, 2019
Latest Amendment Date: June 15, 2021
Award Number: 1847393
Award Instrument: Continuing Grant
Program Manager: Yih-Fang Huang
yhuang@nsf.gov
 (703)292-8126
ECCS
 Division of Electrical, Communications and Cyber Systems
ENG
 Directorate for Engineering
Start Date: March 1, 2019
End Date: February 28, 2026 (Estimated)
Total Intended Award Amount: $500,549.00
Total Awarded Amount to Date: $500,549.00
Funds Obligated to Date: FY 2019 = $429,574.00
FY 2021 = $70,975.00
History of Investigator:
  • Siddhartha Banerjee (Principal Investigator)
    sbanerjee@cornell.edu
Recipient Sponsored Research Office: Cornell University
341 PINE TREE RD
ITHACA
NY  US  14850-2820
(607)255-5014
Sponsor Congressional District: 19
Primary Place of Performance: Cornell University
229 Rhodes Hall
Ithaca
NY  US  14853-3801
Primary Place of Performance
Congressional District:
19
Unique Entity Identifier (UEI): G56PUALJ3KT5
Parent UEI:
NSF Program(s): OE Operations Engineering,
EPCN-Energy-Power-Ctrl-Netwrks
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01001920DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 092E, 1045
Program Element Code(s): 006Y00, 760700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

CAREER: Harnessing Prediction Engines and Non-Monetary Mechanisms for Real-Time Decision Making

Smart societal systems - on-demand transportation, smart supply-chain and logistics networks, cloud platforms, the smart grid, financial-processing networks, etc - are revolutionizing all aspects of our economic and social lives. All these systems face similar decision-making challenges due to uncertainty, complex state-spaces, combinatorial constraints, and strategic agent behavior. The aim of this Faculty Early Career Development Program (CAREER) project is to develop a unified framework for real-time decision-making for smart systems, built around the use of data-driven prediction oracles and non-monetary market mechanisms. Such an approach leads to policies that are simple, easy to interpret and implement in practice; understanding their performance however requires new theoretical and methodological ideas. The research will be complemented by collaborations with partners in on-demand transportation, cloud computing, online payment processing and the local food-bank, which will provide a portfolio of examples for pedagogical purposes. These collaborations tie in with outreach plans, which center on the development of a public library of societal systems simulators. These will provide research projects for undergraduate students, experiential problems for courses, and public demonstrations for attracting K-12 students to STEM fields.

From a technical perspective, this research will develop rigorous frameworks for: (i) harnessing prediction oracles as inputs to real-time control policies, and (ii) designing non-monetary allocation policies based on emulating monetary mechanism. An exemplar of the paradigm is the idea of simulation-as-a-service (SaaS), wherein complex data-driven simulators will be used as inputs for control policies and mechanisms. Such an approach leverages historical and real-time data, incorporates the unique constraints of the underlying system, and results in simple and practical policies. The cost of this simplicity is that it is harder to prove rigorous guarantees. To overcome this, the research will couple advances in machine learning and mechanism design theory with the underlying philosophy of model-predictive control. In particular, new theoretical techniques, using ideas from stochastic coupling, convex optimization, martingale duality, and measure concentration, will be developed.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 27)
Périvier, Noémie and Hssaine, Chamsi and Samaranayake, Samitha and Banerjee, Siddhartha "Real-time Approximate Routing for Smart Transit Systems" Proceedings of the ACM on Measurement and Analysis of Computing Systems , v.5 , 2021 https://doi.org/10.1145/3460091 Citation Details
Périvier, Noémie and Hssaine, Chamsi and Samaranayake, Samitha and Banerjee, Siddhartha "Real-time Approximate Routing for Smart Transit Systems" SIGMETRICS '21: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems , 2021 https://doi.org/10.1145/3410220.3460096 Citation Details
Sinclair, Sean R. and Banerjee, Siddhartha and Lee Yu, Christina "Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces" ACM SIGMETRICS Performance Evaluation Review , v.48 , 2020 https://doi.org/10.1145/3410048.3410059 Citation Details
Sinclair, Sean R. and Banerjee, Siddhartha and Yu, Christina Lee "Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces" Proceedings of the ACM on Measurement and Analysis of Computing Systems , v.3 , 2019 https://doi.org/10.1145/3366703 Citation Details
Sinclair, Sean R. and Banerjee, Siddhartha and Yu, Christina Lee "Adaptive Discretization in Online Reinforcement Learning" Operations Research , 2022 https://doi.org/10.1287/opre.2022.2396 Citation Details
Sinclair, Sean R. and Banerjee, Siddhartha and Yu, Christina Lee "Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve" Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems , 2022 https://doi.org/10.1145/3489048.3526951 Citation Details
Sinclair, Sean R. and Jain, Gauri and Banerjee, Siddhartha and Yu, Christina Lee "Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Trade-off Curve" Operations Research , 2022 https://doi.org/10.1287/opre.2022.2397 Citation Details
Vera, Alberto and Banerjee, Siddhartha "The Bayesian Prophet: A Low-Regret Framework for Online Decision Making" ACM SIGMETRICS 2019 , 2019 https://doi.org/10.1145/3309697.3331518 Citation Details
Vera, Alberto and Banerjee, Siddhartha "The Bayesian Prophet: A Low-Regret Framework for Online Decision Making" Management Science , v.67 , 2021 https://doi.org/10.1287/mnsc.2020.3624 Citation Details
Alijani, Reza and Banerjee, Siddhartha and Gollapudi, Sreenivas and Munagala, Kamesh and Wang, Kangning "Predict and Match: Prophet Inequalities with Uncertain Supply" SIGMETRICS '20: Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems , 2020 https://doi.org/10.1145/3393691.3394212 Citation Details
Banerjee, S. and Munagala, K. and Shen, Y. and Wang, K. "Fair Price Discrimination" ACM-SIAM Symposium on Discrete Algorithms , 2024 https://doi.org/10.1137/1.9781611977912.96 Citation Details
(Showing: 1 - 10 of 27)

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