|
EconStor >
The Johns Hopkins University, Baltimore, Md. >
Department of Economics, The Johns Hopkins University >
Working Papers, Department of Economics, The Johns Hopkins University >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/49877
|
| | |
| Title: | | Nonparametric learning rules from bandit experiments: The eyes have it!  |
| Authors: | | Hu, Yingyao Kayaba, Yutaka Shum, Matt |
| Issue Date: | | 2010 |
| Series/Report no.: | | Working papers // the Johns Hopkins University, Department of Economics 560 |
| Abstract: | | How do people learn? We assess, in a distribution-free manner, subjects' learning and choice rules in dynamic two-armed bandit (probabilistic reversal learning) experiments. To aid in identification and estimation, we use auxiliary measures of subjects' beliefs, in the form of their eye-movements during the experiment. Our estimated choice probabilities and learning rules have some distinctive features; notably that subjects tend to update in a non-smooth manner following choices made in accordance with current beliefs. Moreover, the beliefs implied by our nonparametric learning rules are closer to those from a (non-Bayesian) reinforcement learning model, than a Bayesian learning model. |
| Subjects: | | Learning experiments eye-tracking Bayesian vs. non-Bayesian learning nonparametric estimation |
| JEL: | | D83 C91 C14 |
| Document Type: | | Working Paper |
| Appears in Collections: | | Working Papers, Department of Economics, The Johns Hopkins University
|
| |
| | |
Download bibliographical data as:
BibTeX
|
| |
Share on:http://hdl.handle.net/10419/49877
|
Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.
|