Benefits of learning in negotiation

Dajun Zeng, Katia Sycara

Research output: Contribution to conferencePaper

80 Scopus citations

Abstract

Negotiation has been extensively discussed in gametheoretic, economic, and management science literatures for decades. Recent growing interest in electronic commerce has given increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payoff might increase. In this paper, we propose a sequential decision making model of negotiation, called Bazaar. Within the proposed negotiation framework, we model learning as a Bayesian belief update process. In this paper, we explore the hypothesis that learning is beneficial in sequential negotiation and present initial experimental results.

Original languageEnglish (US)
Pages36-41
Number of pages6
StatePublished - Dec 1 1997
Externally publishedYes
EventProceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 - Providence, RI, USA
Duration: Jul 27 1997Jul 31 1997

Other

OtherProceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97
CityProvidence, RI, USA
Period7/27/977/31/97

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Zeng, D., & Sycara, K. (1997). Benefits of learning in negotiation. 36-41. Paper presented at Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97, Providence, RI, USA, .