ADAPTIVE LEARNING vs. EQUILIBRIUM REFINEMENTS IN AN ENTRY LIMIT PRICING GAME*
通过实验和自适应学习模型研究进入限制定价博弈，发现即使高成本垄断者从不使用占优策略，其他玩家越容易识别这些策略，博弈越可能收敛到无占优分离均衡，且限制定价发展更快。这挑战了均衡精炼文献和纯贝叶斯自适应学习模型，而一个增强的自适应学习模型能预测这些结果。
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.