Efficiency improvement and variance reduction
WSC '94 Proceedings of the 26th conference on Winter simulation
Monte Carlo simulation approach to stochastic programming
Proceedings of the 33nd conference on Winter simulation
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs
SIAM Journal on Optimization
Playing large games using simple strategies
Proceedings of the 4th ACM conference on Electronic commerce
Combinatorial Auctions
The complexity of computing a Nash equilibrium
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Empirical mechanism design: methods, with application to a supply-chain scenario
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Generating trading agent strategies: analytic and empirical methods for infinite and large games
Generating trading agent strategies: analytic and empirical methods for infinite and large games
Recent advances in ranking and selection
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Stochastic search methods for nash equilibrium approximation in simulation-based games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Searching for approximate equilibria in empirical games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Mechanism design and analysis using simulation-based game models
Mechanism design and analysis using simulation-based game models
Stochastic Nash equilibrium problems: sample average approximation and applications
Computational Optimization and Applications
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The field of game theory has proved to be of great importance in modeling interactions between self-interested parties in a variety of settings. Traditionally, game-theoretic analysis relied on highly stylized models to provide interesting insights about problems at hand. The shortcoming of such models is that they often do not capture vital detail. On the other hand, many real strategic settings, such as sponsored search auctions and supply-chains, can be modeled in high resolution using simulations. Recently, a number of approaches have been introduced to perform analysis of game-theoretic scenarios via simulation-based models. The first contribution of this work is the asymptotic analysis of Nash equilibria obtained from simulation-based models. The second contribution is to derive expressions for probabilistic bounds on the quality of Nash equilibrium solutions obtained using simulation data. In this vein, we derive very general distribution-free bounds, as well as bounds which rely on the standard normality assumptions, and extend the bounds to infinite games via Lipschitz continuity. Finally, we introduce a new maximum-a-posteriori estimator of Nash equilibria based on game-theoretic simulation data and show that it is consistent and almost surely unique.