Learning under Diverse World Views: Model-Based Inference
- (pp. 1464-1501)
AbstractPeople 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.
CitationMailath, George J., and Larry Samuelson. 2020. "Learning under Diverse World Views: Model-Based Inference." American Economic Review, 110 (5): 1464-1501. DOI: 10.1257/aer.20190080
- D82 Asymmetric and Private Information; Mechanism Design
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness