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| Author: | Cooper, D. J. Garvin, S. Kagel, J. H. |
| Title: | Adaptive learning vs. equilibrium refinements in an entry limit pricing game |
| Journal: | Economic Journal
1997 : MAY, VOL. 107:442, p. 553-575 |
| Index terms: | LEARNING EQUILIBRIUM ANALYSIS ENTRY CONDITIONS PRICING GAMES |
| Language: | eng |
| Abstract: | This paper studies signaling models using experiments and adaptive learning models in an entry limit pricing game. The easier it is for players to recognize that the strategies are dominated, the more likely play is to converge to the undominated separating equilibrium and the more rapidly limit pricing develops, even though high cost monopolists never play dominated strategies. With the equilibrium refinements literature and pure adaptive learning models this seems to be inconsistent. The augmented adaptive learning model best organises the experimental data as it reliably predicts which equilibrium play converges to and the speed with which limit pricing develops. |
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