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Incentive Compatible Active Learning

Echenique, Federico and Prasad, Siddharth (2019) Incentive Compatible Active Learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210304-085754936

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Abstract

We consider active learning under incentive compatibility constraints. The main application of our results is to economic experiments, in which a learner seeks to infer the parameters of a subject's preferences: for example their attitudes towards risk, or their beliefs over uncertain events. By cleverly adapting the experimental design, one can save on the time spent by subjects in the laboratory, or maximize the information obtained from each subject in a given laboratory session; but the resulting adaptive design raises complications due to incentive compatibility. A subject in the lab may answer questions strategically, and not truthfully, so as to steer subsequent questions in a profitable direction. We analyze two standard economic problems: inference of preferences over risk from multiple price lists, and belief elicitation in experiments on choice over uncertainty. In the first setting, we tune a simple and fast learning algorithm to retain certain incentive compatibility properties. In the second setting, we provide an incentive compatible learning algorithm based on scoring rules with query complexity that differs from obvious methods of achieving fast learning rates only by subpolynomial factors. Thus, for these areas of application, incentive compatibility may be achieved without paying a large sample complexity price.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1911.05171arXivDiscussion Paper
ORCID:
AuthorORCID
Echenique, Federico0000-0002-1567-6770
Record Number:CaltechAUTHORS:20210304-085754936
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210304-085754936
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108306
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Mar 2021 21:38
Last Modified:04 Mar 2021 21:38

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