Autonomous Agents and Multi-Agent Systems

, Volume 28, Issue 6, pp 934–955

Automated agents for reward determination for human work in crowdsourcing applications


    • Department of Computer ScienceBar-Ilan University
  • Yonatan Aumann
    • Department of Computer ScienceBar-Ilan University
  • Sarit Kraus
    • Department of Computer ScienceBar-Ilan University
    • Institute for Advanced Computer StudiesUniversity of Maryland

DOI: 10.1007/s10458-013-9244-y

Cite this article as:
Azaria, A., Aumann, Y. & Kraus, S. Auton Agent Multi-Agent Syst (2014) 28: 934. doi:10.1007/s10458-013-9244-y


Crowdsourcing applications frequently employ many individual workers, each performing a small amount of work. In such settings, individually determining the reward for each assignment and worker may seem economically beneficial, but is inapplicable if manually performed. We thus consider the problem of designing automated agents for automatic reward determination and negotiation in such settings. We formally describe this problem and show that it is NP-hard. We therefore present two automated 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 RP, and the second, the No Bargaining Agent (NBA) which tries to avoid any negotiation. The performance of the agents is tested in extensive experiments with real human subjects, where both NBA and RPBA outperform strategies developed by human experts.


Human–computer interaction Crowdsourcing Negotiation

Copyright information

© The Author(s) 2013