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Optimal Non-adaptive Concession Strategies with Incomplete Information

  • Tim Baarslag
  • Rafik Hadfi
  • Koen Hindriks
  • Takayuki Ito
  • Catholijn Jonker
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 638)

Abstract

When two parties conduct a negotiation, they must be willing to make concessions to achieve a mutually acceptable deal, or face the consequences of no agreement. Therefore, negotiators normally make larger concessions as the deadline is closing in. Many time-based concession strategies have already been proposed, but they are typically heuristic in nature, and therefore, it is still unclear what is the right way to concede toward the opponent. Our aim is to construct optimal concession strategies against specific classes of acceptance strategies. We apply sequential decision techniques to find analytical solutions that optimize the expected utility of the bidder, given certain strategy sets of the opponent. Our solutions turn out to significantly outperform current state of the art approaches in terms of obtained utility. Our results open the way for a new and general concession strategy that can be combined with various existing learning and accepting techniques to yield a fully-fledged negotiation strategy for the alternating offers setting.

Keywords

Ultimatum Game Acceptance Strategy Negotiation Strategy Acceptance Threshold Optimal Bidder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    An, B., Gatti, N., Lesser, V.: Bilateral bargaining with one-sided uncertain reserve prices. Auton. Agents Multi-Agent Syst. 26(3), 420–455 (2013)CrossRefGoogle Scholar
  2. 2.
    An, B., Sim, K.M., Tang, L.G., Miao, C.Y., Shen, Z.Q., Cheng, D.J.: Negotiation agents’ decision making using markov chains. In: Ito, T., Hattori, H., Zhang, M., Matsuo, T. (eds.) Rational. Robust, and Secure Negotiations in Multi-Agent Systems, volume 89 of Studies in Computational Intelligence, pp. 3–23. Springer, Berlin (2008)Google Scholar
  3. 3.
    Baarslag, T., Fujita, K., Gerding, E.H., Hindriks, K., Ito, Takayuki, Jennings, Nicholas R., Jonker, Catholijn, Kraus, Sarit, Lin, Raz, Robu, Valentin, Williams, Colin R.: Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artif. Intell. 198, 73–103 (2013)CrossRefGoogle Scholar
  4. 4.
    Baarslag, T., Hindriks, K., Hendrikx, M., Dirkzwager, A., Jonker, Catholijn: Decoupling negotiating agents to explore the space of negotiation strategies. In: Marsa-Maestre, I., Lopez-Carmona, M.A., Ito, T., Zhang, M., Bai, Quan, Fujita, Katsuhide (eds.) Novel Insights in Agent-based Complex Automated Negotiation. Studies in Computational Intelligence, vol. 535, pp. 61–83. Springer, Japan (2014)CrossRefGoogle Scholar
  5. 5.
    Baarslag, T., Hindriks, K., Jonker, C.: A tit for tat negotiation strategy for real-time bilateral negotiations. In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations: Theories. Models, and Software Competitions, volume 435 of Studies in Computational Intelligence, pp. 229–233. Springer, Berlin (2013)CrossRefGoogle Scholar
  6. 6.
    Baarslag, T., Hindriks, K., Jonker, C.: Effective acceptance conditions in real-time automated negotiation. Decis. Support Syst. 60, 68–77 (2014)CrossRefGoogle Scholar
  7. 7.
    Baarslag, T., Hindriks, K.V.: Accepting optimally in automated negotiation with incomplete information. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS ’13, pp. 715–722, Richland, SC (2013). International Foundation for Autonomous Agents and Multiagent SystemsGoogle Scholar
  8. 8.
    Carnevale, P.J.D., Lawler, E.J.: Time pressure and the development of integrative agreements in bilateral negotiations. J. Confl. Resolut. 30(4), 636–659 (1986)CrossRefGoogle Scholar
  9. 9.
    Chen, S., Weiss, G.: OMAC: a discrete wavelet transformation based negotiation agent. In: Marsa-Maestre, I., Lopez-Carmona, M.A., Ito, T., Zhang, M., Bai, Q., Fujita, K. (eds.) Novel Insights in Agent-based Complex Automated Negotiation. Studies in Computational Intelligence, vol. 535, pp. 187–196. Springer, Japan (2014)CrossRefGoogle Scholar
  10. 10.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998). Multi-Agent RationalityGoogle Scholar
  11. 11.
    Fatima, S.S., Wooldridge, M., Jennings, N.R.: Optimal negotiation strategies for agents with incomplete information. In: Revised Papers from the 8th International Workshop on Intelligent Agents VIII, ATAL’01, pp. 377–392. Springer, London (2002)Google Scholar
  12. 12.
    Fatima, S., Wooldridge, M., Jennings, N.R.: Optimal negotiation of multiple issues in incomplete information settings. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3, AAMAS’04, pp. 1080–1087. IEEE Computer Society, Washington, DC, USA (2004)Google Scholar
  13. 13.
    Fatima, S.S., Wooldridge, M., Jennings, N.R.: Bargaining with incomplete information. Ann. Math. Artif. Intell. 44(3), 207–232 (2005)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Gode, D.K., Sunder, S.: Allocative efficiency in markets with zero intelligence (ZI) traders: market as a partial substitute for individual rationality. J. Polit. Econ. 101(1), 119–137 (1993)CrossRefGoogle Scholar
  15. 15.
    Hao, J., Leung, H.-F.: ABiNeS: an adaptive bilateral negotiating strategy over multiple items. In: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 02, WI-IAT’12, pp. 95–102. IEEE Computer Society, Washington, DC, USA (2012)Google Scholar
  16. 16.
    Kawaguchi, S., Fujita, K., Ito, T.: Compromising strategy based on estimated maximum utility for automated negotiating agents. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-based Complex Automated Negotiations, Series of Studies in Computational Intelligence, pp. 137–144. Springer, Berlin (2012)Google Scholar
  17. 17.
    Kersten, G.E., Vahidov, R., Gimon, D.: Concession-making in multi-attribute auctions and multi-bilateral negotiations: theory and experiments. Electr. Comm. Res. Appl. 12(3), 166–180 (2013). Negotiation and E-CommerceGoogle Scholar
  18. 18.
    Kolomvatsos, K., Anagnostopoulos, C., Hadjiefthymiades, S.: Determining the optimal stopping time for automated negotiations. IEEE Trans. Syst. Man Cybern.: Syst. 99, 1–1 (2013)Google Scholar
  19. 19.
    Leonardz, B.: To Stop or Not to Stop. Some Elementary Optimal Stopping Problems with Economic Interpretations. Almqvist & Wiksell, Stockholm (1973)Google Scholar
  20. 20.
    Li, C., Giampapa, J., Sycara, K.: Bilateral negotiation decisions with uncertain dynamic outside options. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 36(1), 31–44 (2006)Google Scholar
  21. 21.
    Lin, R., Kraus, S., Baarslag, T., Tykhonov, D., Hindriks, K., Jonker, C.M.: Genius: an integrated environment for supporting the design of generic automated negotiators. Comput. Intell. 30(1), 48–70 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Niemann, C., Lang, F.: Assess your opponent: a bayesian process for preference observation in multi-attribute negotiations. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) Advances in Agent-Based Complex Automated Negotiations. Studies in Computational Intelligence, vol. 233, pp. 119–137. Springer, Berlin (2009)CrossRefGoogle Scholar
  23. 23.
    Raiffa, H.: The Art and Science of Negotiation. Belknap Press of Harvard University Press (1982)Google Scholar
  24. 24.
    Rosenschein, J.S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation Among Computers. MIT Press, Cambridge, MA, USA (1994)Google Scholar
  25. 25.
    Rubin, J.Z., Brown, B.R.: The Social Psychology of Bargaining and Negotiation. Academic Press, New York (1975)Google Scholar
  26. 26.
    Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109 (1982)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Sanchez-Pages, S.: The use of conflict as a bargaining tool against unsophisticated opponents. ESE Discussion Papers 99, Edinburgh School of Economics, University of Edinburgh (2004)Google Scholar
  28. 28.
    Slembeck, T.: Reputations and fairness in bargaining - experimental evidence from a repeated ultimatum game with fixed opponents. Exp. EconWPA (1999)Google Scholar
  29. 29.
    Stahl, I.: Bargaining Theory. Economic Research Institute, Stockholm (1972)Google Scholar
  30. 30.
    van Krimpen, T., Looije, D., Hajizadeh, S.: Hardheaded. In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations: Theories. Models, and Software Competitions, volume 435 of Studies in Computational Intelligence, pp. 223–227. Springer, Berlin (2013)Google Scholar
  31. 31.
    Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: Iamhaggler: a negotiation agent for complex environments. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-based Complex Automated Negotiations, Series of Studies in Computational Intelligence, pp. 151–158. Springer, Berlin (2012)Google Scholar
  32. 32.
    Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: An overview of the results and insights from the third automated negotiating agents competition (ANAC 2012). In: Marsa-Maestre, I., Lopez-Carmona, M.A., Ito, T., Zhang, M., Bai, Q., Fujita, K. (eds.) Novel Insights in Agent-based Complex Automated Negotiation. Studies in Computational Intelligence, vol. 535, pp. 151–162. Springer, Japan (2014)CrossRefGoogle Scholar
  33. 33.
    Wu, M., Mathijs, W., Han, P.: Acceptance strategies for maximizing agent profits in online scheduling. In: Esther, D., Valentin, R., Onn, S., Sebastian, S., Andreas, S. (eds.) Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets, volume 119 of Lecture Notes in Business Information Processing, pp. 115–128. Springer, Berlin (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tim Baarslag
    • 1
  • Rafik Hadfi
    • 2
  • Koen Hindriks
    • 1
  • Takayuki Ito
    • 2
  • Catholijn Jonker
    • 1
  1. 1.Interactive Intelligence GroupDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Computer Science and EngineeringGraduate School of Engineering, Nagoya Institute of TechnologyNagoyaJapan

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