Automated Strategies for Determining Rewards for Human Work

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations


We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.

Original languageEnglish
Number of pages8
StatePublished - 2012
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012


Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012

Bibliographical note

Publisher Copyright:
Copyright © 2012, Association for the Advancement of Artificial Intelligence ( All rights reserved.


We thank Avi Rosenfeld and Shira Abuhatzera for their helpful comments. This work is supported in part by the following grants: ERC grant #267523, MURI grant #W911NF-08-1-0144, ARO grants W911NF0910206 and W911NF1110344, MOST #3-6797 and ISF grant #1401/09.

FundersFunder number
Army Research OfficeW911NF1110344, W911NF0910206
Multidisciplinary University Research Initiative911NF-08-1-0144
European Research Council267523
Ministry of Science, Technology and Space3-6797
Israel Science Foundation1401/09


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