Bayesian Learning Without Recall
We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which inclu...
Main Authors: | Jadbabaie, Ali (Author), Rahimian, Mohammad Amin (Contributor), Jadbabaie-Moghadam, Ali (Contributor) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering (Contributor), Massachusetts Institute of Technology. Institute for Data, Systems, and Society (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor) |
Format: | Article |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2018-09-17T14:55:33Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Complexity of Bayesian Belief Exchange over a Network
by: Mossel, Elchanan, et al.
Published: (2018) -
Distributed estimation and learning over heterogeneous networks
by: Rahimian, Mohammad Amin, et al.
Published: (2017) -
A Theory of Non-Bayesian Social Learning
by: Molavi, Pooya, et al.
Published: (2022) -
Receding horizon control of nonlinear systems: a control Lyapunov function approach
by: Jadbabaie, Ali
Published: (2001) -
Equilibrium analysis for a leader-follower game with noisy observations: A pace forward in Witsenhausen's counterexample conjecture
by: Ajorlou, Amir, et al.
Published: (2018)