
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.


coremsg

Conjectural-Online-Learning-in-Asymmetric-Information-Stochastic-Games


Conjectural Online Learning in Asymmetric Information Stochastic Games


title_rewrite


COL은 베이지안 학습을 통해 수렴하는 합리적인 전략을 제공합니다.


conjectural-online-learning-with-first-order-beliefs-in-asymmetric-information-stochastic-games


Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
