Game Theoretic

Game theory provides a mathematical framework for analyzing strategic interactions between multiple agents, aiming to understand and predict outcomes in various scenarios. Current research focuses on applying game-theoretic principles to machine learning, particularly in areas like adversarial training, multi-agent reinforcement learning, and the explainability of complex models, often employing algorithms such as Shapley values and Nash equilibrium solutions within model architectures like Generative Adversarial Networks. This approach is proving valuable for improving the robustness, fairness, and interpretability of machine learning systems, as well as for designing efficient mechanisms in areas such as online marketplaces and resource allocation.