Automated Multilateral Negotiation on Multiple Issues with Private Information

INFORMS Journal on Computing, Vol. 28, No. 4, pp. 612–628, Fall 2016

30 Pages Posted: 22 Oct 2015 Last revised: 20 Jan 2019

See all articles by Ronghuo Zheng

Ronghuo Zheng

Carnegie Mellon University - David A. Tepper School of Business

Tinglong Dai

Johns Hopkins University - Carey Business School

Katia Sycara

Carnegie Mellon University - School of Computer Science

Nilanjan Chakraborty

Stony Brook University -- College of engineering and Applied Sciences

Date Written: June 3, 2016

Abstract

In this paper, we propose and analyze a distributed negotiation strategy for a multi-agent multi-attribute negotiation in which the agents have no information about the utility functions of other agents. We analytically prove that, if the zone of agreement is non-empty and the agents concede up to their reservation utilities, agents generating offers using our offer-generation strategy, namely the sequential projection strategy, will converge to an agreement acceptable to all the agents; the convergence property does not depend on the specific concession strategy. In considering agents’ incentive to concede during the negotiation, we propose and analyze a reactive concession strategy. We demonstrate through computational experiments that our distributed negotiation strategy yields performance sufficiently close to the Nash bargaining solution, and that our algorithms are robust to potential deviation strategies. Methodologically, our paper advances the state of the art of alternating projection algorithms, in that we establish the convergence for the case of multiple, moving sets (as opposed to two, static sets in the current literature). Our paper introduces a new analytical foundation for a broad class of computational group decision and negotiation problems.

Keywords: convergence of negotiation, multi-agent multi-attribute negotiation, alternating projection algorithms, distributed decision making

JEL Classification: C61, C63, C78, D74, D82

Suggested Citation

Zheng, Ronghuo and Dai, Tinglong and Sycara, Katia and Chakraborty, Nilanjan, Automated Multilateral Negotiation on Multiple Issues with Private Information (June 3, 2016). INFORMS Journal on Computing, Vol. 28, No. 4, pp. 612–628, Fall 2016. Available at SSRN: https://ssrn.com/abstract=2677729 or http://dx.doi.org/10.2139/ssrn.2677729

Ronghuo Zheng

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Tinglong Dai (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

HOME PAGE: http://carey.jhu.edu/faculty/tinglong-dai-phd

Katia Sycara

Carnegie Mellon University - School of Computer Science ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213
United States

Nilanjan Chakraborty

Stony Brook University -- College of engineering and Applied Sciences ( email )

United States

HOME PAGE: http://www.stonybrook.edu/commcms/provost/faculty/new/new-ceas.html

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