Learning single-issue negotiation strategies using hierarchical clustering method
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 (buyers offer, sellers 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.
negotiation negotiation strategy hierarchical clustering method competitive learning agent-based simulation