Rationalizable conjectural equilibrium: A framework for robust predictions
提出一个不同于贝叶斯博弈的新框架，用于研究同时存在结构不确定性和策略不确定性的环境，通过理性化推测均衡整合反馈学习和内省学习，帮助研究者检验纳什预测的稳健性。
I introduce a new framework to study environments with both structural and strategic uncertainty, different from Harsanyi's (1967-8) `Bayesian games', that allows a researcher to test the robustness of Nash predictions while maintaining certain desirable restrictions on players' beliefs. The solution concept applied to this environment is rationalizable conjectural equilibrium (RCE), which integrates both learning from feedback (in the spirit of self-confirming equilibrium) and from introspection (in the spirit of rationalizability). I provide an epistemic definition of RCE and obtain a characterization in terms of a procedure that generalizes iterated deletion of strategies that are not a best response.