This paper discusses the implications of learning theory for the analysis of games
with a move by Nature. One goal is to illuminate the issues that arise when modeling
situations where players are learning about the distribution of Nature's move as
well as learning about the opponents' strategies. A second goal is to argue that
quite restrictive assumptions are necessary to justify the concept of Nash equilibrium
without a common prior as a steady state of a learning process.