Social Learning with Coarse Inference
Ends: Wednesday 7 March 2012 2:00 pm
We study social learning by boundedly rational agents. Agents take a decision in sequence, after observing their predecessors and a private signal. They are unable to make perfect inferences from their predecessors' decisions: they only understand the relation between the aggregate distribution of actions and the state of nature and make their inferences accordingly.
We show that, in a discrete action space, even if agents receive signals of unbounded precision, convergence to the truth does not occur. In a continuous action space, compared to the rational case, agents overweight early signals. Despite this behavioural bias, convergence to the truth eventually obtains.
E&F research seminar convener: Russ Moro (Russ.Moro@brunel.ac.uk)