Preference interdependencies in a multi-issue salary negotiation

James Hale, Peter Kim, J. Gratch
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Abstract

Negotiation is an important potential application domain for intelligent virtual agents but, unlike research on agent-agent negotiations, agents that negotiate with people often adopt unrealistic simplifying assumptions. These assumptions not only limit the generality of these agents, but call into question scientific findings about how people negotiate with agents. Here we relax two common assumptions: the use of assigned rather than elicited user preferences, and the use of linear utility functions. Using a simulated salary negotiation, we find that relaxing these assumptions helps reveal interesting individual differences in how people negotiate their salary and allows algorithms to find better win-win solutions.
多议题薪资谈判中的偏好相互依赖关系
协商是智能虚拟代理的一个重要潜在应用领域,但与agent-agent协商研究不同,与人协商的agent往往采用不切实际的简化假设。这些假设不仅限制了这些代理人的普遍性,而且对人们如何与代理人谈判的科学发现提出了质疑。在这里,我们放松了两个常见的假设:使用指定的而不是引出的用户偏好,以及使用线性效用函数。通过模拟薪资谈判,我们发现放宽这些假设有助于揭示人们在薪资谈判中有趣的个体差异,并允许算法找到更好的双赢解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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