Can We Reach Pareto Optimal Outcomes Using Bottom-Up Approaches?

  • Victor Sanchez-Anguix
  • Reyhan Aydoğan
  • Tim Baarslag
  • Catholijn M. Jonker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10238)

Abstract

Classically, disciplines like negotiation and decision making have focused on reaching Pareto optimal solutions due to its stability and efficiency properties. Despite the fact that many practical and theoretical algorithms have successfully attempted to provide Pareto optimal solutions, they have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outcomes in a group by using a bottom-up approach: discovering Pareto optimal outcomes by interacting in subgroups. We analytically show that the set of Pareto optimal outcomes in a group covers the Pareto optimal outcomes within its subgroups. This theoretical finding can be applied in a variety of scenarios such as negotiation teams, multi-party negotiation, and team formation to social recommendation. Additionally, we empirically test the validity and practicality of this proof in a variety of decision making domains and analyze the usability of this proof in practical situations.

Keywords

Pareto optimality Agreement technologies Group decision making Multi-agent systems Artificial intelligence 

References

  1. 1.
    Amador, S., Okamoto, S., Zivan, R.: Dynamic multi-agent task allocation with spatial and temporal constraints. In: Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems, pp. 1495–1496. International Foundation for Autonomous Agents and Multiagent Systems (2014)Google Scholar
  2. 2.
    Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 839–848. ACM (2012)Google Scholar
  3. 3.
    Argente, E., Botti, V., Carrascosa, C., Giret, A., Julian, V., Rebollo, M.: An abstract architecture for virtual organizations: the thomas approach. Knowl. Inf. Syst. 29(2), 379–403 (2011)CrossRefGoogle Scholar
  4. 4.
    Aydoğan, R., Hindriks, K.V., Jonker, C.M.: Multilateral mediated negotiation protocols with feedback. 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. SCI, vol. 535, pp. 43–59. Springer, Tokyo (2014). doi:10.1007/978-4-431-54758-7_3 CrossRefGoogle Scholar
  5. 5.
    Bogomolnaia, A., Moulin, H.: Size versus fairness in the assignment problem. Games Econ. Behav. 90, 119–127 (2015)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Brandt, F., Conitzer, V., Endriss, U.: Computational social choice. In: Multiagent system, pp. 213–283 (2012)Google Scholar
  7. 7.
    Corne, D.W., Knowles, J.D.: Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 773–780. ACM, New York (2007)Google Scholar
  8. 8.
    Jonge, D., Sierra, C.: NB\(^{3}\): a multilateral negotiation algorithm for large, non-linear agreement spaces with limited time. Auton. Agents Multi-agent Syst. 29(5), 896–942 (2015)CrossRefGoogle Scholar
  9. 9.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_83 CrossRefGoogle Scholar
  10. 10.
    di Pierro, F.: Many-objective evolutionary algorithms and applications to water resources engineering. Ph.D. thesis, University of Exeter (2006)Google Scholar
  11. 11.
    Esparcia, S., Sanchez-Anguix, V., Aydoğan, R.: A negotiation approach for energy-aware room allocation systems. In: Corchado, J.M., et al. (eds.) Highlights on Practical Applications of Agents and Multi-Agent Systems, vol. 365, pp. 280–291. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    García-Segarra, J., Ginés-Vilar, M.: The impossibility of paretian monotonic solutions: a strengthening of Roths result. Oper. Res. Lett. 43(5), 476–478 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hara, K., Ito, T.: A mediation mechanism for automated negotiating agents whose utility changes over time. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)Google Scholar
  14. 14.
    Heiskanen, P., Ehtamo, H., Hämäläinen, R.P.: Constraint proposal method for computing pareto solutions in multi-party negotiations. Eur. J. Oper. Res. 133(1), 44–61 (2001)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Hewitt, C.: Open information systems semantics for distributed artificial intelligence. Artif. Intell. 47(1–3), 79–106 (1991)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, pp. 82–87. IEEE (1994)Google Scholar
  17. 17.
    Xiao-Bing, H., Wang, M., Di Paolo, E.: Calculating complete and exact pareto front for multiobjective optimization: a new deterministic approach for discrete problems. IEEE Trans. Cybern. 43(3), 1088–1101 (2013)CrossRefGoogle Scholar
  18. 18.
    Kamishima, T.: Nantonac collaborative filtering: recommendation based on order responses. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 583–588. ACM (2003)Google Scholar
  19. 19.
    Kash, I., Procaccia, A.D., Shah, N.: No agent left behind: dynamic fair division of multiple resources. J. Artif. Intell. Res. 51, 579–603 (2014)MathSciNetMATHGoogle Scholar
  20. 20.
    Lai, G., Li, C., Sycara, K.: Efficient multi-attribute negotiation with incomplete information. Group Decis. Negot. 15(5), 511–528 (2006)CrossRefGoogle Scholar
  21. 21.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    Marsa-Maestre, I., Klein, M., Jonker, C.M., Aydoğan, R.: From problems to protocols: towards a negotiation handbook. Decis. Support Syst. 60, 39–54 (2014)CrossRefGoogle Scholar
  24. 24.
    Masthoff, J.: Group recommender systems: combining individual models. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 677–702. Springer, USA (2011)CrossRefGoogle Scholar
  25. 25.
    Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: Movielens unplugged: experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 263–266. ACM (2003)Google Scholar
  26. 26.
    O’Neill, B.: The number of outcomes in the pareto-optimal set of discrete bargaining games. Math. Oper. Res. 6(4), 571–578 (1981)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Rahwan, I., Larson, K.: Pareto optimality in abstract argumentation. In: AAAI, pp. 150–155 (2008)Google Scholar
  28. 28.
    Sanchez-Anguix, V., Dai, T., Semnani-Azad, Z., Sycara, K., Botti, V.: Modeling power distance and individualism/collectivism in negotiation team dynamics. In: 45 Hawaii International Conference on System Sciences (HICSS-45), pp. 628–637 (2012)Google Scholar
  29. 29.
    Sanchez-Anguix, V., Julian, V., Botti, V., Garcia-Fornes, A.: Reaching unanimous agreements within agent-based negotiation teams with linear and monotonic utility functions. IEEE Trans. Syst. Man Cybern. Part B 42(3), 778–792 (2012)CrossRefGoogle Scholar
  30. 30.
    Sanchez-Anguix, V., Julian, V., Botti, V., Garcia-Fornes, A.: Studying the impact of negotiation environments on negotiation teams’ performance. Inf. Sci. 219, 17–40 (2013)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Sanchez-Anguix, V., Aydoğan, R., Julian, V., Jonker, C.: Unanimously acceptable agreements for negotiation teams in unpredictable domains. Electr. Commer. Res. Appl. 13(4), 243–265 (2014)CrossRefGoogle Scholar
  32. 32.
    Sanchez-Anguix, V., Espinosa, A., Hernandez, L., Garcia-Fornes, A.: MAMSY: a management tool for multi-agent systems. In: Demazeau, Y., Pavón, J., Corchado, J.M., Bajo, J. (eds.) 7th International Conference on Practical Applications of Agents and Multi-agent Systems (PAAMS), vol. 55, pp. 130–139. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  33. 33.
    Sanchez-Anguix, V., Julian, V., Botti, V., García-Fornes, A.: Tasks for agent-based negotiation teams: analysis, review, and challenges. Eng. Appl. Artif. Intell. 26(10), 2480–2494 (2013)CrossRefMATHGoogle Scholar
  34. 34.
    Sánchez-Anguix, V., Valero, S., Julián, V., Botti, V., García-Fornes, A.: Evolutionary-aided negotiation model for bilateral bargaining in ambient intelligence domains with complex utility functions. Inf. Sci. 222, 25–46 (2013)CrossRefGoogle Scholar
  35. 35.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: Proceedings of the fifth International Conference on Computer and Information Technology, vol. 1. Citeseer (2002)Google Scholar
  36. 36.
    Skowron, P., Faliszewski, P., Slinko, A.: Achieving fully proportional representation is easy in practice. In: Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems, pp. 399–406. International Foundation for Autonomous Agents and Multiagent Systems (2013)Google Scholar
  37. 37.
    Zhenh, R., Chakraborty, N., Dai, T., Sycara, K.: Automated multilateral negotiation on multiple issues with private information. INFORMS J. Comput. 28(4), 612–628 (2015)MathSciNetMATHGoogle Scholar
  38. 38.
    Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32. ACM (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Coventry UniversityCoventryUK
  2. 2.Özyeğin UniversityIstanbulTurkey
  3. 3.Centrum Wiskunde and InformaticaAmsterdamNetherlands
  4. 4.Technical University of DelftDelftNetherlands

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