Mining trading partner's preferences for efficient multi-issue bargaining in E-business

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Scopus Citations
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Original languageEnglish
Pages (from-to)79-103
Journal / PublicationJournal of Management Information Systems
Issue number1
Publication statusPublished - Jun 2008


Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e-marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e-marketplaces. © 2008 M.E. Sharpe, Inc.

Research Area(s)

  • Bayesian learning, E-business, Knowledge discovery, Multi-issue bargaining, Negotiation