中外谈判的艺术

<正> 谈判的方法是谈判者的有效武器和工具,采用不同的方法可以得到不同的结果。而方法是一门科学,一门艺术,讲究谈判方法就是要讲究谈判艺术。在工程涉外谈判中,我深切感到以下一些方法是重要的。 1.紧紧抓住谈判的主要目标 谈判时,谈判者必须牢牢把握谈判方向,抓住谈判的主要目标。要围绕谈判目标来进行各项谈判活     

基于Agent的谈判模型研究

提出了一种基于Agent技术的谈判模型,应用模糊数学理论建立了谈判论据及谈判解接受度数学模型。采用约束放松的方法对谈判模型进行求解,得到了谈判Agent满意的谈判解。阐述了双边多属性谈判过程,设计并开发了Agent谈判系统原型,实例计算分析表明,该谈判模型是一种双赢的谈判模型。     

基于案例推理的谈判支持系统的研究

通过对谈判案例的表达、检索、复用、评价、适配和学习的研究,基于案例推理的谈判支持系统解决了谈判者谈判相关知识缺乏的问题;在谈判案例表达方法中提出了属性分类方法;采用改进的最近相邻法进行谈判案例适配,以获得更相近的谈判历史案例;通过对基于案例推理的谈判机制分析,构建了基于案例推理谈判支持系统的体系结构,并详细设计了谈判系统的功能.最后,通过一个采购谈判方案验证了基于案例推理谈判支持系统的实用性.     

基于案例推理的谈判支持系统的研究

通过对谈判案例的表达,检索,复用,评价,适配和学习的研究,基于案例推理的谈判支持系统解决了谈判者谈判相关知识缺乏的问题,在谈判案例表达方法中提出了属性分类方法;采用改进的最近相邻法进行谈判案例适配,以获得更相近的谈判历史案例;通过对基于案例推理的谈判机制分析,构建了基于案例推理谈判支持系统的体系结构,并详细设计了谈判系统的功能.最后,通过一个采购谈判方案验证了基于案例推理谈判支持系统的实用性.

     

基于Agent的多问题并行遗传谈判模型

Agent谈判增加了电子商务系统的主动性,一个有效的谈判模型是系统实现的关键。分析了已有Agent谈判模型的特点,设计的基于Agent的多问题并行谈判模型解决了已有谈判模型中存在的谈判问题单一、非并行、不考虑对手收益和固定权重等问题。模型中产生谈判方案的算法的自适应性体现在问题权重、遗传参数和收益偏差的动态调整上,给出了问题实数编码和权重调整公式。最后,设计了一个面向三个问题的电子谈判实例,验证了谈判模型的可行性和有效性。     

一种基于多Agent的多策略谈判框架

多Agent之间的谈判是DAI领域的重要研究课题.目前的谈判框架大多是采用单因素依赖的谈判策略,没有考虑复杂环境下多因素的综合影响,为解决这一问题,提出了一个多策略加权整合的多议题的谈判框架.本文从时间、资源、对方策略等多方面的因素来考虑整个谈判策略,从而提高了谈判框架的效率.     

基于辩论的自动谈判模型

自动谈判是商务活动中最富智能性和创造性的环节,是智能商务模式中亟需解决的关键技术.主要讨论了基于辩论的自动谈判问题,提出了相应的谈判模型和谈判Agent的体系结构模型,并详述了辩论式谈判机制,最后设计了一个辩论式谈判实例.     

考虑铁损时异步电机的仿真

推导出在转子磁场定向控制中两相同步旋转坐标系下不考虑铁损和考虑铁损的三相异步电机动态数学模型,进而建立基于Simulink的不考虑铁损和考虑铁损的三相异步电机仿真模型.用仿真实例比对两个模型,验证了考虑铁损的异步电机模型在磁场定向时能更真实地反映电机实际物理过程,为异步电机的控制问题提供了一个更加接近电机行为特性的高效仿真模型.     

基于涌现视角的MAS信任模型仿真分析方法

目前MAS信任模型研究关注的焦点是信任管理的微观机制设计,而从这些微观机制到系统的宏观特性,并不是显而易见的.因此,这些研究都采用了仿真分析.但是这些仿真实验的设计大多缺乏理论指导,往往不能正确反映系统的宏观特性.引入系统科学中的涌现视角来审视MAS,探讨基于涌现视角的MAS信任模型仿真分析方法.这一方法从相对清晰的微观层面入手,建立仿真运行模型;在充分考虑的相关宏观约束下,研究系统宏观层面的问题以及微观和宏观之间的联系.总结分析了信任模型微观机制的设计图式,在此基础上探讨了仿真Agent的设计框架;讨论了信任模型仿真分析中应考虑的宏观问题,包括宏观约束、威胁模型、评价指标以及仿真执行中的问题,最后实例验证了此方法的有效性.     

掌控最后的关键

谈判败局并不能归罪于谈判者说错了什么或做错了什么,而更多地是由于在定局阶段我们没有做一些至关紧要的事情——有效促进。换句话说,大多数人在谈判中遵循规则行事并不难,难的是对机会的判断和细节的把握。     

基于模糊理论的一对多自动谈判协调策略

针对一对多自动谈判参与各方在谈判过程中因为相互等待而效率低下的问题,提出一种能使谈判过程连续化的基于模糊的协调策略,以达到在尽可能少的时间内得到最满意协议的目的。该策略通过协调Agent灵活地创建和撤离新的谈判线程并在谈判过程中不断更新谈判的信念值进而支持连续谈判,满足开放和动态的谈判环境,提高谈判效率。模拟实验表明,该策略能够对谈判效用和时间进行优化,具有有效性和实用性。     

基于模糊多准则决策的谈判模型

针对目前谈判支持系统中谈判模型存在的难以准确获知谈判人的偏好，以及利用偏好构造其效用函数的问题，应用模糊数学的方法，将谈判人的偏好和对目标值的评价用梯形模糊数表示，由此提出一种基于模糊多准则决策方法求解谈判问题的模型。通过示例分析计算，表明该模型能帮助谈判各方达成一个满意解。     

多Agent自动协商的公平性研究

为了能够快速、高效地进行Agent协商,构建一个优化的多Agent协商模型。在这个模型的基础上,提出了一个基于协商各方公平性的协商学习算法。算法采用基于满意度的思想评估协商对手的提议,根据对方Agent协商历史及本次协商交互信息,通过在线学习机制预测对方Agent协商策略,动态得出协商妥协度并向对方提出还价提议。最后,通过买卖协商仿真实验验证了该算法的收敛性,表明基于该算法的模型工作的高效性、公平性。     

Risk assessment for build-operate-transfer projects: a dynamic multi-objective programming approach

This study uses a dynamic multi-objective programming approach to establish a risk assessment model, and develops an iterative algorithm for the model solution. The results obtained show that the sum of the interactive utility value could determine whether or not the interactive relationship is characterized by independence among negotiators. In addition, our numerical example shows that the risk measurement model developed can reflect risk assessment made by the negotiation group for certain events, and can analyze interaction characteristics among negotiators.     

基于神经网络的协商学习机制

多agent协商研究中,如何通过学习提高协商效率是一个重要的课题,目前的研究多采用简单的学习算法,学习效果不好。论文首先提出了一个两方多回合交互协商框架,然后依据协商历史结果、协商双方初次出价等信知,对协商结果信息进行预测,从而确定协商交互中的推理策略,并利用BP神经网络的自适应、自学习能力对协商结果预测机制进行学习。随后的验证系统表明,该机制通过对协商结果的有效预测,提高了协商交互的效率和协商个体的效用。     

The Influences of Deception and Computer-Mediation on Dyadic Negotiations

This article reports on an experiment investigating the differences between computer-mediated and face-to-face negotiations and between negotiators being deceptive about hidden agendas and negotiators without hidden agendas. Our results supported the hypotheses that individuals negotiating via instant messaging are more likely to use forcing negotiating, experience more tension, and have lower deception detection accuracy than individuals negotiating face-to-face. Unexpectedly, it was found that individuals negotiating via instant messaging were more satisfied with the negotiation process than were face-to-face negotiators. Finally, results supported the hypothesis that those being deceptive about hidden agendas experienced higher tension than those without hidden agendas. These findings have several implications for organizations: higher levels of tension from computer-mediated negotiations and from deception can affect the long-term effectiveness of employees, undetected deception in computer-mediated negotiations can have a negative impact on negotiations, and computer-mediation can lead to the use of a forcing negotiation style, which may improve the effectiveness of negotiators with individualistic goals.     

基于Agent辩论谈判的奖励模型研究

电子商务谈判过程中,由于能使接受辩论的Agent根据它所接受到的辩论改变其偏好或目标,从而使谈判双方更快更好地达成一致,因而基于辩论的Agent谈判方式受到了广泛关注。目前已经有文献对此作了一定程度的研究,然而较少有对其中客观存在的威胁、奖励和申辩进行有效地形式化建模和描述。目标主要在于研究其中的奖励模型和其评价模型,然后在此基础上对奖励的评价模型进行模拟和分析,以评价其辩论力度的强弱,使被奖励对象最终做出正确选择。     

The importance of language familiarity in global business e-negotiation

This study explores the influence of language familiarity on online persuasion behavior based on subjective measurements and objective actual negotiation behavior. It was designed to test whether negotiating in a non-native language decreases the negotiation self-efficacy, given that the increasing use of global e-marketplaces and popularity of international business trades make negotiation in a non-native language inevitable. An online experiment was conducted using a text-based asynchronous e-negotiation system, with two groups of subjects negotiating in native and non-native languages separately in purchasing negotiations. The analysis results show that language familiarity plays a critical role in inducing persuasion behavior in e-negotiations, with a higher language familiarity leading to higher language self-efficacy and negotiation self-efficacy. However, only negotiation self-efficacy affects e-negotiation communication efficiency and effectiveness, both of which increase online persuasion behavior. Based on actual negotiation behavior, the results show that non-native language negotiators are less active than native language negotiators in negotiations. However, the negotiation outcome did not differ significantly between the two groups, suggesting that the final outcome is also influenced by other factors. The results also showed that language familiarity has a greater effect on the buyer than on the seller.     

Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation

Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated approaches have proven particularly promising for complex negotiations and previous research indicates evolutionary computation could be useful for such complex systems. To improve the efficiency of realistic multi-lateral multi-issue negotiations and avoid the requirement of complete information about negotiators, a novel negotiation model based on an improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotiation efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution (DE), in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive parameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multiagent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.