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 1
Externally publishedYes

Fingerprint

Electronic commerce

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Learning single-issue negotiation strategies using hierarchical clustering method. / Baek, Jun-Geol; Kim, Chang Ouk.

In: Expert Systems with Applications, Vol. 32, No. 2, 01.02.2007, p. 606-615.

Research output: Contribution to journalArticle

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