In game theory, a Nash equilibrium, named after John Nash, is a situation where each player's strategy is optimal given the known strategies of other players, and no player benefits from changing their strategy unilaterally. In such an equilibrium, every player's strategy choice perfectly anticipates the choices of other players, ensuring mutual best responses. Nash demonstrated that every finite game has at least one such equilibrium, which may involve mixed strategies.
Stanford University
Autumn 2022-2023
Stanford's CS 221 course teaches foundational principles and practical implementation of AI systems. It covers machine learning, game playing, constraint satisfaction, graphical models, and logic. A rigorous course requiring solid foundational skills in programming, math, and probability.
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+ 88 more conceptsBrown University
Spring 2023
Offered by Brown University, this course intertwines game theory and computational considerations. With emphasis on strategic agent behavior, system design, and computational tractability, students delve into auction theory, bidding strategies, computational advertising, and automated negotiation. Knowledge in Java, Python, and certain mathematical areas is essential.
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