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.
ADAPTIVE LEARNING vs. EQUILIBRIUM REFINEMENTS IN AN ENTRY LIMIT PRICING GAME*
Article first published online: 30 JAN 2012
Royal Economic Society 1997
The Economic Journal
Volume 107, Issue 442, pages 553–575, May 1997
How to Cite
Cooper, D. J., Garvin, S. and Kagel, J. H. (1997), ADAPTIVE LEARNING vs. EQUILIBRIUM REFINEMENTS IN AN ENTRY LIMIT PRICING GAME. The Economic Journal, 107: 553–575. doi: 10.1111/j.1468-0297.1997.tb00027.x
- Issue published online: 30 JAN 2012
- Article first published online: 30 JAN 2012
- Date of receipt of final typescript: July 1996
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.