Learning single-issue negotiation strategies using hierarchical clustering method

Jun Geol Baek, Chang Ouk Kim

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This research proposes an off-line learning method targeted for systematically constructing single-issue negotiation strategies in electronic commerce. Our research is motivated by the following fact: evidence from both theoretical analysis and observations of human interaction shows that if decision makers have a prior knowledge on the behaviors of opponents, the overall payoffs would increase. Given past negotiation data set, a competitive learning and a variant of hierarchical clustering model are applied to extract the negotiation strategies. A negotiation strategy is a chain of the pairs consisting of (buyer's offer, seller's counteroffer). An agent-based simulation convinced us that the proposed method is more effective than human negotiation in terms of the ratio of negotiation agreement and resulting payoffs.

Original languageEnglish
Pages (from-to)606-615
Number of pages10
JournalExpert Systems With Applications
Volume32
Issue number2
DOIs
Publication statusPublished - 2007 Feb
Externally publishedYes

Keywords

  • Agent-based simulation
  • Competitive learning
  • Hierarchical clustering method
  • Negotiation
  • Negotiation strategy

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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