Automated Multilateral Negotiation on Multiple Issues with Private Information

Ronghuo Zheng
Carnegie Mellon University – David A. Tepper School of Business

Nilanjan Chakraborty
Stony Brook University — College of engineering and Applied Sciences

Tinglong Dai
Johns Hopkins University – Carey Business School (JHU)

Katia Sycara
Carnegie Mellon University

October 19, 2015

INFORMS Journal on Computing, Forthcoming.


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.

Automated Multilateral Negotiation on Multiple Issues with Private Information

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