TY - GEN
T1 - A Data-Driven Distributionally Robust Game Using Wasserstein Distance
AU - Peng, Guanze
AU - Zhang, Tao
AU - Zhu, Quanyan
N1 - Funding Information:
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).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Data-driven optimization
KW - Distributionally robust game
KW - Mathematical programming
UR - http://www.scopus.com/inward/record.url?scp=85098249963&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-64793-3_22
DO - 10.1007/978-3-030-64793-3_22
M3 - Conference contribution
AN - SCOPUS:85098249963
SN - 9783030647926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 405
EP - 421
BT - Decision and Game Theory for Security - 11th International Conference, GameSec 2020, Proceedings
A2 - Zhu, Quanyan
A2 - Baras, John S.
A2 - Poovendran, Radha
A2 - Chen, Juntao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Conference on Decision and Game Theory for Security, GameSec 2020
Y2 - 28 October 2020 through 30 October 2020
ER -