Adaptive Learning vs. Equilibrium Refinements in an Entry Limit Pricing Game.

Signaling models are studied using experiments and adaptive learning models in an entry limit pricing game. Even though high cost monopolies never play dominated strategies, the easier it is for other players to recognize that these strategies are dominated, the more likely play is to converge to the undominated separating equilibrium and the more rapidly limit pricing develops. This is inconsistent with the equilibrium refinements literature, including I. Cho and D. Kreps's (1987) intuitive criterion, and pure (Bayesian) adaptive learning models. An augmented adaptive learning model in which some players recognize the existence of dominated strategies and their consequences predicts these outcomes. Copyright 1997 by Royal Economic Society.