ABSTRACT Human–computer negotiation has the potential to play an important role in today’s highly dynamic online environment, especially in business-to-consumer e-commerce transactions. However, the lack of research on effective automated negotiation algorithms to respond to human buyers’ strategic and/or tactic offers has limited the development of automated human–computer negotiation systems for real-world applications. Intelligent software agents that are capable of dynamically adjusting their negotiation strategy in response to human buyers’ offers can greatly improve the negotiation experience of human buyers. In this study, guided by design science principles, we design a portfolio strategy model, which implements four negotiation strategies (i.e., time-dependent, behavior-dependent, dynamic time-dependent, and impasse resolution) as the core of our software agent for negotiating with human buyers. To evaluate this novel model, we implement a prototype of the system and compare it with three benchmark single-strategy models (i.e., competitive, collaborative, and selection) in human–computer negotiation experiments. The results show that our model not only enables the software agent to outperform its human counterpart but also significantly increases the settlement ratio and the joint outcome of both parties.

中文翻译：

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电子零售中人机谈判的投资组合策略设计

摘要 人机协商有可能在当今高度动态的在线环境中发挥重要作用，尤其是在企业对消费者的电子商务交易中。然而，缺乏对响应人类买家的战略和/或战术提议的有效自动谈判算法的研究，限制了用于现实世界应用的自动人机谈判系统的发展。能够根据人类买家的报价动态调整其谈判策略的智能软件代理可以极大地改善人类买家的谈判体验。在本研究中，以设计科学原理为指导，我们设计了一个投资组合策略模型，该模型实现了四种谈判策略（即时间相关、行为相关、动态时间相关、和僵局解决）作为我们与人类买家谈判的软件代理的核心。为了评估这个新模型，我们实现了系统原型，并将其与人机谈判实验中的三个基准单策略模型（即竞争、协作和选择）进行比较。结果表明，我们的模型不仅使软件代理的表现优于人类代理，而且显着提高了双方的结算比率和联合结果。