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Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
COL은 베이지안 학습을 통해 수렴하는 합리적인 전략을 제공합니다.
Conjectural Online Learning in Asymmetric Information Stochastic Games
The author proposes Conjectural Online Learning (COL) as a learning scheme for generic AISGs, utilizing first-order beliefs and Bayesian learning to adapt strategies efficiently. The core argument is that COL converges to the Berk-Nash equilibrium, demonstrating rationality under subjectivity.