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A Data-Driven Distributionally Robust Game Using Wasserstein Distance

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Decision and Game Theory for Security (GameSec 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12513))

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Abstract

This paper studies a special class of games, which enables the players to leverage the information from a dataset to play the game. However, in an adversarial scenario, the dataset may not be trustworthy. We propose a distributionally robust formulation to introduce robustness against the worst-case scenario and tackle the curse of the optimizer. By applying Wasserstein distance as the distribution metric, we show that the game considered in this work is a generalization of the robust game and data-driven empirical game. We also show that as the number of data points in the dataset goes to infinity, the game considered in this work boils down to a Nash game. Moreover, we present the proof of the existence of distributionally robust equilibria and a tractable mathematical programming approach to solve for such equilibria.

This research is partially supported by awards ECCS-1847056, CNS-1544782, CNS-2027884, and SES-1541164 from National Science of Foundation (NSF), and grant W911NF-19-1-0041 from Army Research Office (ARO).

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Guanze Peng, Tao Zhang & Quanyan Zhu

Authors
  1. Guanze Peng
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  2. Tao Zhang
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  3. Quanyan Zhu
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Corresponding author

Correspondence to Guanze Peng .

Editor information

Editors and Affiliations

  1. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Quanyan Zhu

  2. ISR, University of Maryland, College Park, MD, USA

    John S. Baras

  3. Electrical Engineering, University of Washington, Seattle, WA, USA

    Radha Poovendran

  4. New York University, New York, NY, USA

    Juntao Chen

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Peng, G., Zhang, T., Zhu, Q. (2020). A Data-Driven Distributionally Robust Game Using Wasserstein Distance. In: Zhu, Q., Baras, J.S., Poovendran, R., Chen, J. (eds) Decision and Game Theory for Security. GameSec 2020. Lecture Notes in Computer Science(), vol 12513. Springer, Cham. https://doi.org/10.1007/978-3-030-64793-3_22

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  • DOI: https://doi.org/10.1007/978-3-030-64793-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64792-6

  • Online ISBN: 978-3-030-64793-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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