Automated agents for reward determination for human work in crowdsourcing applications

Amos Azaria, Yonatan Aumann, Sarit Kraus

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish
Pages (from-to)934-955
Number of pages22
JournalAutonomous Agents and Multi-Agent Systems
Issue number6
StatePublished - Nov 2014
Externally publishedYes


  • Crowdsourcing
  • Human-computer interaction
  • Negotiation


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