Article

Bargaining with deadlines

Authors:

Tuomas Sandholm,

Nir VulkanAuthors Info & Claims

AAAI '99/IAAI '99: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence

Pages 44 - 51

Published: 18 July 1999 Publication History

Abstract

This paper analyzes automated distributive negotiation where agents have firm deadlines that are private information. The agents are allowed to make and accept offers in any order in continuous time. We show that the only sequential equilibrium outcome is one where the agents wait until the first deadline, at which point that agent concedes everything to the other. This holds for pure and mixed strategies. So, interestingly, rational agents can never agree to a nontrivial split because offers signal enough weakness of bargaining power (early deadline) so that the recipient should never accept. Similarly, the offerer knows that it offered too much if the offer gets accepted: the offerer could have done better by out-waiting the opponent. In most cases, the deadline effect completely overrides time discounting and risk aversion: an agent's payoff does not change with its discount factor or risk attitude. Several implications for the design of negotiating agents are discussed. We also present an effective protocol that implements the equilibrium outcome in dominant strategies.

References

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Cited By

Haim GGal YAn BKraus S(2017)Humancomputer negotiation in a three player market settingArtificial Intelligence10.1016/j.artint.2017.01.003246:C(34-52)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.artint.2017.01.003
Vo QTran TNguyen T(2016)On the Use of Surplus Division to Facilitate Efficient Negotiation in the Presence of Incomplete InformationProcedia Computer Science10.1016/j.procs.2016.08.14296:C(295-304)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.08.142
Kolomvatsos KPanagidi KNeokosmidis IVaroutas DHadjiefthymiades S(2016)Automated concurrent negotiationsElectronic Commerce Research and Applications10.1016/j.elerap.2016.09.00219:C(56-69)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.elerap.2016.09.002
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cover image Guide Proceedings

AAAI '99/IAAI '99: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence

July 1999

998 pages

ISBN:0262511061

Chairmen:
Jim Hendler
Univ. of Maryland, College Park; and DARP/ISO
,
Devika Subramanian
Rice Univ., Houston, TX
,
Ramasamy Uthurusamy
General Motors Research
,
Barbara Hayes-Roth
Extempo Systems, Inc.

Sponsors

AAAI: Am Assoc for Artifical Intelligence

Publisher

American Association for Artificial Intelligence

United States

Publication History

Published: 18 July 1999

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Cited By

Haim GGal YAn BKraus S(2017)Humancomputer negotiation in a three player market settingArtificial Intelligence10.1016/j.artint.2017.01.003246:C(34-52)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.artint.2017.01.003
Vo QTran TNguyen T(2016)On the Use of Surplus Division to Facilitate Efficient Negotiation in the Presence of Incomplete InformationProcedia Computer Science10.1016/j.procs.2016.08.14296:C(295-304)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.08.142
Kolomvatsos KPanagidi KNeokosmidis IVaroutas DHadjiefthymiades S(2016)Automated concurrent negotiationsElectronic Commerce Research and Applications10.1016/j.elerap.2016.09.00219:C(56-69)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.elerap.2016.09.002
Baarslag THendrikx MHindriks KJonker C(2016)Learning about the opponent in automated bilateral negotiationAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9309-130:5(849-898)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1007/s10458-015-9309-1
Sofer ISarne DHassidim A(2016)Negotiation in exploration-based environmentAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9303-730:4(724-764)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s10458-015-9303-7
Ji SLeung HSim KLiang YChiu D(2015)An adaptive prediction-regret driven strategy for one-shot bilateral bargaining software agentsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.07.02242:1(411-425)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.07.022
Vo Q(2013)A Mechanism to Improve Efficiency for Negotiations with Incomplete InformationProceedings of the 26th Australasian Joint Conference on AI 2013: Advances in Artificial Intelligence - Volume 827210.1007/978-3-319-03680-9_3(26-31)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.1007/978-3-319-03680-9_3
Kolomvatsos KHadjiefthymiades S(2012)Buyer behavior adaptation based on a fuzzy logic controller and prediction techniquesFuzzy Sets and Systems10.1016/j.fss.2011.07.017189:1(30-52)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.fss.2011.07.017
Sim KGuo YShi B(2009)BLGANIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics10.1109/TSMCB.2008.200450139:1(198-211)Online publication date: 1-Feb-2009
https://dl.acm.org/doi/10.1109/TSMCB.2008.2004501
Gatti N(2009)Extending the alternating-offers protocol in the presence of competitionAnnals of Mathematics and Artificial Intelligence10.1007/s10472-009-9158-155:3-4(189-236)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.1007/s10472-009-9158-1
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