TY - JOUR
T1 - Learning under diverse world views
T2 - Model-based inference†
AU - Mailath, George J.
AU - Samuelson, Larry
N1 - Publisher Copyright:
© 2020 American Economic Association. All rights reserved.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085383410&partnerID=8YFLogxK
U2 - 10.1257/aer.20190080
DO - 10.1257/aer.20190080
M3 - Article
SN - 0002-8282
VL - 110
SP - 1461
EP - 1501
JO - American Economic Review
JF - American Economic Review
IS - 5
ER -