报告题目: Business Negotiations: Agents, Models, Data
报告所属学科：管理科学与工程
报告人：Gregory E. Kersten
报告时间：2019年6月27日14:00
报告地点：将军路校区经管楼706室
报告摘要：
Beginning with Edgeworth in 1881, economists have been interested in the distribution of resources between firms. A modification of Edgeworth’s suggestion is a well-known Edgeworth box, which illustrates a contract curve on which every point is an efficient solution to the negotiation problem.
Zeuthen (1930) proposed a negotiation strategy based on monotonic concessions that yield an agreement providing that, for every negotiator, the utility of this agreement is greater than the utility of disagreement. Harsanyi (1962) studied negotiations in the context of game theory and showed that the Zeuthen’s agreement is equivalent to the well-known Nash bargaining solution (1950). Baarslag, et al. (2016) and Rosenfeld and Kraus (2019) wrote comprehensive reviews of hybrid and automated negotiations. The general purpose is that these systems reach an agreement, if possible, Pareto optimal. The corollary is that the agents seek a single solution and exchange information about the same set of issues. Obviously, many business negotiations are concerned with finding a beneficial contract requiring the participants to agree on the contract terms. While these negotiations are commonplace they are easier to model and represent than negotiations in which the participants are more interested in learning about each other than about the contract details (Kersten, 2019).
报告人简介：
Dr. Kersten（Gregory E. Kersten, 康思腾），加拿大康考迪亚大学（Concordia University）约翰·摩森商学院 (John Molson School of Business)资深教授，意大利巴里理工大学机械工程与管理学院兼职教授，INFORMS群决策与谈判分会主席， SSCI Q1区期刊《Group Decision and Negotiation》主编。曾任意大利巴里理工大学、美国海军研究生院、香港科技大学、台湾国立中山大学、奥地利国际应用系统分析研究所等访问教授。长期从事单人与群决策、谈判分析、谈判支持、交换机制、拍卖、基于网络的系统开发、行为经济学等领域的研究。群决策与谈判系统的国际知名学者。研究项目多次获得加拿大自然科学与工程基金、加拿大社会与人文科学基金、德国洪堡基金、澳大利亚研究基金、加拿大贝尔基金、意大利CINECA基金等的支持。出版编辑9部专著/论文集，在权威SCI/SSCI学术期刊如Management Science, Decision Support Systems, European Journal of Operational Research等发表80余篇论文（Google Scholar引用5456次，h指数40）。
智能决策与风险分析研究所
