Abstract: 

We propose novel generalizations of two prominent theories of learning - Bayesian updating and reinforcement learning - that provide differential, non-overlapping testable implications, based purely on which observables people learn from, and allow for separation and identification of learning types. We propose an experiment designed to generate maximal conflict between the theoretical predictions of these theories, to cleanly distinguish between them. This incentivized experiment will allow us to classify observed behavior in various contexts, across both laboratory and remote settings, according to our generalizations of the two theories. It will also allow us to identify the distribution of learning modalities in the subject population under different conditions. 

Joint work with Nate Neligh

Location:

ENS Paris-Saclay
4 avenue des Sciences, 91190, Gif-sur-Yvette