Abstract:We introduce Berk--Nash rationalizability, a set-valued counterpart to Berk--Nash equilibrium for environments in which agents may hold misspecified models. A set of behaviors is self-justified if every behavior in the set is optimal under beliefs fitted to data generated within the set. Berk--Nash rationalizability is the largest self-justified set and reduces to standard rationalizability under correct specification and identification. We provide a learning foundation allowing Bayesian agents to learn from different, selectively sampled histories: almost surely, the limit set of play is self-justified. Conversely, under identification, any compact self-justified set can arise as the limit set with arbitrarily high probability. We illustrate how the concept yields informative predictions that are robust across learning procedures, without committing to a specific dynamic or assuming convergence.
From: Demian Pouzo [view email]
[v1]
Tue, 27 May 2025 04:35:44 UTC (32 KB)
[v2]
Mon, 23 Jun 2025 21:28:38 UTC (34 KB)
[v3]
Fri, 24 Oct 2025 21:19:50 UTC (60 KB)
[v4]
Wed, 16 Sep 2026 18:31:53 UTC (136 KB)
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