報告題目: Business Negotiations: Agents, Models, Data
報告所屬學科：管理科學與工程
報告人：Gregory E. Kersten
報告時間：2019年6月27日14:00
報告地點：將軍路校區經管樓706室
報告摘要：
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).
報告人簡介：
Dr. Kersten（Gregory E. Kersten, 康思騰），加拿大康考迪亞大學（Concordia University）約翰·摩森商學院 (John Molson School of Business)資深教授，意大利巴里理工大學機械工程與管理學院兼職教授，INFORMS群決策與談判分會主席， SSCI Q1區期刊《Group Decision and Negotiation》主編。曾任意大利巴里理工大學、美國海軍研究生院、香港科技大學、臺灣國立中山大學、奧地利國際應用系統分析研究所等訪問教授。長期從事單人與群決策、談判分析、談判支持、交換機制、拍賣、基于網絡的系統開發、行為經濟學等領域的研究。群決策與談判系統的國際知名學者。研究項目多次獲得加拿大自然科學與工程基金、加拿大社會與人文科學基金、德國洪堡基金、澳大利亞研究基金、加拿大貝爾基金、意大利CINECA基金等的支持。出版編輯9部專著/論文集，在權威SCI/SSCI學術期刊如Management Science, Decision Support Systems, European Journal of Operational Research等發表80余篇論文（Google Scholar引用5456次，h指數40）。
智能決策與風險分析研究所
