From the paper:
Specifically, nonzero-sum large-scale strategic games with high-dimensional continuous decision spaces and random projection methods are investigated as a starting point. Our investigation centers around the reduction of large-scale strategic games using transformations such as random projections and their effect on Nash Equilibrium solutions. Analytically tractable results are presented for quadratic games and in an adversarial machine learning setting.
Large-Scale Strategic Games and Adversarial Machine Learning by Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie
Decision making in modern large-scale and complex systems such as communication networks, smart electricity grids, and cyber-physical systems motivate novel game-theoretic approaches. This paper investigates big strategic (non-cooperative) games where a finite number of individual players each have a large number of continuous decision variables and input data points. Such high-dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non-linear optimization scaling up to large systems of variables. In addition to these computational challenges, real-world players often have limited information about their preference parameters due to the prohibitive cost of identifying them or due to operating in dynamic online settings. The challenge of limited information is exacerbated in high dimensions and big data sets. Motivated by both computational and information limitations that constrain the direct solution of big strategic games, our investigation centers around reductions using linear transformations such as random projection methods and their effect on Nash equilibrium solutions. Specific analytical results are presented for quadratic games and approximations. In addition, an adversarial learning game is presented where random projection and sampling schemes are investigated.
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