Rationalizable conjectural equilibrium: A framework for robust predictions
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.
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