Final Report: Data-Driven Game Theory
Abstract
We make multiple contributions in this research. Though classical game theory assumes that payoff matrices are given for the players involved in the game, we show how these can be learned automatically from a body of data. We further show new definitions of quasi-polynomially computable approximate equilibria and show how to efficiently and approximately compute them. We apply these methods to the behavior of real world terrorist groups. Continuing with our work on adversarial models, we study methods to disclose information publicly in order to shape the adversary's actions to our advantage. We further develop methods to quantify and reduce lethality of networks. Our work develops and further studies the use of game-theoretic frameworks to the study of security games, online games, as well as games involving diverse terrorist groups including Lashkar-e-Taiba and the Indian Mujahideen.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 15, 2016
- Accession Number
- AD1055617
Entities
People
- V. S. Subrahmanian
Organizations
- University of Maryland