This paper illustrates that least squares learning may lead to suboptimal outcomes even when the estimated function perfectly fits the observations used in the regression. We consider the Salop model with three firms and two types of consumers that face different transportation costs. Firms do not know the demand structure and they apply least squares learning to learn the demand function. In each period, firms estimate a linear perceived demand function and they play the perceived best response to the previous-period price of the other firms. This learning rule can lead to three different outcomes: a self-sustaining equilibrium, the Nash equilibrium or an asymmetric learning-equilibrium. In this last equilibrium one firm underestimates the demand for low prices and it attracts consumers with high transportation costs only. This type of equilibrium has not been found in the literature on least squares learning before. Both the Nash equilibrium and the asymmetric learning-equilibrium are locally stable therefore the model has coexisting stable equilibria.
Salop model least squares learning heterogeneous consumers