Advertisement

Compromise as a Way to Promote Convention Emergence and to Reduce Social Unfairness in Multi-Agent Systems

  • Shuyue Hu
  • Ho-fung Leung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

Recently, the study of social conventions has attracted much attention. We notice that different agents may tend to establish different conventions, even though they share common interests in convention emergence. We model such scenarios to be competitive-coordination games. We hypothesize that agents may fail to establish a convention under these scenarios and introducing the option of compromise may help solve this problem. Experimental study confirms this hypothesis. In particular, it is shown that besides convention emergence is promoted, the undesirable social unfairness is also significantly reduced. In addition, we discuss how the reward of coordination via compromise affects convention emergence, social efficiency and unfairness.

Keywords

Convention emergence Norm Fairness Compromise 

References

  1. 1.
    Ullmann-Margalit, E.: The Emergence of Norms, vol. 11. Oxford University Press, Oxford (2015)Google Scholar
  2. 2.
    Savarimuthu, B.T.R., Cranefield, S.: Norm creation, spreading and emergence: a survey of simulation models of norms in multi-agent systems. Multiagent Grid Syst. 7(1), 21–54 (2011)CrossRefGoogle Scholar
  3. 3.
    Neumann, M.: A classification of normative architectures. In: Takadama, K., Cioffi-Revilla, C., Deffuant, G. (eds.) Simulating Interacting Agents and Social Phenomena. Agent-Based Social Systems, vol. 7, pp. 3–18. Springer, Tokyo (2010).  https://doi.org/10.1007/978-4-431-99781-8_1CrossRefGoogle Scholar
  4. 4.
    Sen, S., Airiau, S.: Emergence of norms through social learning. In: Proceedings of IJCAI (2007)Google Scholar
  5. 5.
    Hu, S., Leung, H.-F.: Achieving coordination in multi-agent systems by stable local conventions under community networks. In: Proceedings of IJCAI (2017)Google Scholar
  6. 6.
    Delgado, J.: Emergence of social conventions in complex networks. Artif. Intell. 141, 171–185 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Young, H.P.: The Economics of Convention. J. Econ. Perspect. 10(2), 105–122 (1996)CrossRefGoogle Scholar
  8. 8.
    Shoham, Y., Tennenholtz, M.: On the emergence of social conventions: modeling, analysis, and simulations. Artif. Intell. 94(1), 139–166 (1997)CrossRefGoogle Scholar
  9. 9.
    Yu, C., Zhang, M., Ren, F., Luo, X.: Emergence of social norms through collective learning in networked agent societies. In: Proceedings of AAMAS (2013)Google Scholar
  10. 10.
    Villatoro, D., Sabater-Mir, J., Sen, S.: Social instruments for robust convention emergence. In: Proceedings of IJCAI (2011)Google Scholar
  11. 11.
    Marchant, J., Griffiths, N., Leeke, M.: Convention emergence and influence in dynamic topologies. In: Proceedings of AAMAS (2015)Google Scholar
  12. 12.
    Pujol, J.M., Delgado, J., Sangüesa, R., Flache, A.: The role of clustering on the emergence of efficient social conventions. In: Proceedings of IJCAI (2005)Google Scholar
  13. 13.
    Hao, J., Leung, H.-F.: The dynamics of reinforcement social learning in cooperative multiagent systems. In: Proceedings of IJCAI (2013)Google Scholar
  14. 14.
    Salazar, N., Rodriguez-Aguilar, J.A., Arcos, J.L.: Robust coordination in large convention spaces. AI Commun. 23(4), 357–372 (2010)MathSciNetGoogle Scholar
  15. 15.
    Hasan, M.R., Raja, A., Bazzan, A.L.: Fast convention formation in dynamic networks using topological knowledge. In: Proceedings of AAAI (2015)Google Scholar
  16. 16.
    Wang, Y., Lu, W., Hao, J., Wei, J., Leung, H.-F.: Efficient convention emergence through decoupled reinforcement social learning with teacher-student mechanism. In: Proceedings of AAMAS (2018)Google Scholar
  17. 17.
    Kittock, J.E.: Emergent conventions and the structure of multi-agent systems. In: Proceedings of the 1993 Santa Fe Institute Complex Systems Summer School, vol. 6 (1993)Google Scholar
  18. 18.
    Sugawara, T.: Emergence and stability of social conventions in conflict situations. In: Proceedings of IJCAI (2011)Google Scholar
  19. 19.
    Yu, C., Zhang, M., Ren, F.: Emotional multiagent reinforcement learning in social Dilemmas. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS (LNAI), vol. 8291, pp. 372–387. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-44927-7_25CrossRefGoogle Scholar
  20. 20.
    Hu, S., Leung, H.-F.: Do social norms emerge? The evolution of agents’ decisions with the awareness of social values under iterated prisoner’s dilemma. In: Proceedings of SASO (2018)Google Scholar
  21. 21.
    Endriss, U., Maudet, N.: Welfare engineering in multiagent systems. In: Omicini, A., Petta, P., Pitt, J. (eds.) ESAW 2003. LNCS (LNAI), vol. 3071, pp. 93–106. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25946-6_6CrossRefGoogle Scholar
  22. 22.
    Gini, C.: Italian: Variabilità e mutabilità (variability and mutability). Cuppini, Bologna (1912)Google Scholar
  23. 23.
    Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)zbMATHGoogle Scholar
  24. 24.
    Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multiagent systems. In: Proceedings of AAAI/IAAI (1998)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongSha TinHong Kong

Personalised recommendations