A Data-Driven Distributionally Robust Game Using Wasserstein Distance

Guanze Peng, Tao Zhang, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationDecision and Game Theory for Security - 11th International Conference, GameSec 2020, Proceedings
EditorsQuanyan Zhu, John S. Baras, Radha Poovendran, Juntao Chen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages405-421
Number of pages17
ISBN (Print)9783030647926
DOIs
StatePublished - 2020
Event11th Conference on Decision and Game Theory for Security, GameSec 2020 - College Park, United States
Duration: Oct 28 2020Oct 30 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12513 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Conference on Decision and Game Theory for Security, GameSec 2020
CountryUnited States
CityCollege Park
Period10/28/2010/30/20

Keywords

  • Data-driven optimization
  • Distributionally robust game
  • Mathematical programming

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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