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Learning under Diverse World Views: Model-Based Inference
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DOI：10.1257/aer.20190080.png)
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Abstract
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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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