Learning under Diverse World Views: Model-Based Inference
George J. Mailath
- American Economic Review (Forthcoming)
People reason about uncertainty with deliberately incomplete models.
How do people hampered by different, incomplete views of the
world learn from each other? We introduce a model of "model-based
inference." Model-based reasoners partition an otherwise hopelessly
complex state space into a manageable model. Unless the differences
in agents' models are trivial, interactions will often not lead agents
to have common beliefs or beliefs near the correct-model belief. If
the agents' models have enough in common, then interacting will lead
agents to similar beliefs, even if their models also exhibit some bizarre
idiosyncrasies and their information is widely dispersed.
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