This paper investigates learning in the Santa Fe (El Farol) bar problem (sfbp). It is argued that rationality together with belief-based learning (e.g., Bayesian updating) yields unstable behavior in this game. More specifically, two conditions normally sufficient for convergence to Nash equilibrium, namely rationality and predictivity, are shown to be incompatible. Low-rationality learning alg...