Training with automated agents improves people's behavior in negotiation and coordination tasks

Raz Lin, Yakov Gal, Sarit Kraus, Yaniv Mazliah

Research output: Contribution to journalArticlepeer-review

Abstract

There is inconclusive evidence whether practicing tasks with computer agents improves people's performance on these tasks. This paper studies this question empirically using extensive experiments involving bilateral negotiation and three-player coordination tasks played by hundreds of human subjects. We used different training methods for subjects, including practice interactions with other human participants, interacting with agents from the literature, and asking participants to design an automated agent to serve as their proxy in the task. Following training, we compared the performance of subjects when playing state-of-the-art agents from the literature. The results revealed that in the negotiation settings, in most cases, training with computer agents increased people's performance as compared to interacting with people. In the three player coordination game, training with computer agents increased people's performance when matched with the state-of-the-art agent. These results demonstrate the efficacy of using computer agents as tools for improving people's skills when interacting in strategic settings, saving considerable effort and providing better performance than when interacting with human counterparts.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalDecision Support Systems
Volume60
Early online date5 Jun 2013
DOIs
Publication statusPublished - Apr 2014

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