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Reducing Mistrust in Agent-Human Negotiations

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IEEE Intelligent Systems

Article on IEEE Intelligent Systems, issue No.02 – Mar-Apr (2014 vol.29), pp: 36-43

By Yinping Yang, Horacio Falcao, Nuno Delicado, Andrew Ortony
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Abstract: Face-to-face negotiations always benefit if the interacting individuals trust each other. But trust is also important in online interactions, even for humans interacting with a computational agent. In this paper we describe a behavioral experiment to determine whether, by volunteering information that it need not disclose, a software agent in a multi-issue negotiation can alleviate mistrust in human counterparts who differ in their propensities to mistrust others. Results indicated that when cynical, mistrusting humans negotiated with an agent that proactively communicated its issue priority and invited reciprocation, there were significantly more agreements and better utilities than when the agent did not volunteer such information. Furthermore, when the agent volunteered its issue priority, the outcomes for mistrusting individuals were as good as those for trusting individuals, for whom the volunteering of issue priority conferred no advantage. These findings provide insights for designing more effective, socially intelligent, agents in online negotiation settings.

 

 

Effects of Communicating Issue Priority for Preference Tradeoffs in Agent-Human Negotiations

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INSEAD Working Paper

INSEAD Working Paper, by Yinping Yang, Horacio Falcao, Nuno Delicado, Andrew Ortony

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Abstract: This study explores the consequences of a software agent volunteering one of its issue priorities and inviting its human counterpart to reciprocate in order to obtain preference tradeoffs in a multi-issue e-commerce negotiation scenario. Results indicated that while the agent followed the same negotiation strategy algorithm, agent-human dyads achieved better agreement rate and joint outcomes, and the human negotiators experienced more positive post-negotiation affect when the agent volunteered to communicate an issue priority than when it didn’t. The findings have implications for automated negotiation research and practice.