Simultaneous Move Game

Simultaneous move games, where players choose actions concurrently without knowing their opponents' choices, are a crucial area of game theory research focusing on understanding optimal strategies and equilibrium outcomes in various competitive and cooperative scenarios. Current research emphasizes developing efficient algorithms, such as those based on tree search and reinforcement learning (including adaptations of AlphaZero), to solve these games, particularly addressing challenges posed by partial information and the need to model opponent behavior. These advancements have implications for improving multi-agent systems in diverse fields, from economics (analyzing algorithmic collusion) to artificial intelligence (developing more robust and effective AI agents for complex games and real-world applications).