ADAPTIVE LEARNING vs. EQUILIBRIUM REFINEMENTS IN AN ENTRY LIMIT PRICING GAME

Authors


  • This research has been supported by a grant from the National Science Foundation. Earlier versions of this paper were presented at the Experimental Game Theory Conference at SUNY Stony Brook, the ESA meetings in Tucson and in seminars at a number of universities. We have benefited from comments and discussions resulting from these presentations. Alexis Miller provided valuable research assistance. Thanks to charles Plott for providing us with speedy access to the data from Miller and Plott. The usual caveat applies.

Abstract

Signalling models are studied using experiments and adaptive learning models in an entry limit pricing game. Even though high cost monopolists never play dominated strategies, the easier it is for other players to recognise 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 Cho-Kreps’ intuitive criterion) and pure (Bayesian) adaptive learning models. An augmented adaptive learning model in which some players recognise the existence of dominated strategies and their consequences predicts these outcomes.

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