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Stable Configurations with (Meta)Punishing Agents

  • Nathaniel BeckemeyerEmail author
  • William Macke
  • Sandip Sen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10798)

Abstract

We consider an adaptation of Axelrod’s metanorm model, where a population of agents choose between cooperating and defecting in bilateral interactions. Because punishing incurs an enforcement cost, Axelrod proposes using metanorms, to facilitate the stability of a norm of punishing defectors, where those who do not punish defectors can themselves be punished. We present two approaches to study the social effects of such metanorms when agents can choose their interaction partners: (a) a theoretical study, when agent behaviors are static, showing stable social configurations, under all possible relationships between system parameters representing agent payoffs with or without defection, punishment, and meta-punishment, and (b) an experimental evaluation of emergent social configurations when agents choose behaviors to maximize expected utility. We highlight emergent social configurations, including anarchy, a “police” state with cooperating agents who enforce, and a unique “corrupt police” state where one enforcer penalizes all defectors but defects on others!

Keywords

MABS workshop Multi-agent systems Cooperation Norm emergence Network topologies Metanorm Metapunishment Punishment 

Notes

Acknowledgments

We would like to thank the University of Tulsa and in particular the Tulsa Undergraduate Research Challenge (TURC) for financial support of this project.

References

  1. 1.
    Airiau, S., Sen, S., Villatoro, D.: Emergence of conventions through social learning. Auton. Agents Multi-agent Syst. 28(5), 779–804 (2014)CrossRefGoogle Scholar
  2. 2.
    Axelrod, R.: An evolutionary approach to norms. Am. Polit. Sci. Rev. 80, 1095–1111 (1986)CrossRefGoogle Scholar
  3. 3.
    Baetz, O.: Social activity and network formation. Theor. Econ. 10(2), 315–340 (2015)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Barabasi, A.: Network Science. Cambridge University Press, Cambridge (2016)zbMATHGoogle Scholar
  5. 5.
    Belardinelli, F., Grossi, D.: On the formal verification of diffusion phenomena in open dynamic agent networks. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 237–245. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2015)Google Scholar
  6. 6.
    Berninghaus, S., Vogt, B.: Network formation and coordination games, March 2003Google Scholar
  7. 7.
    Borge-Holthoefer, J., Baos, R.A., Gonzlez-Bailn, S., Moreno, Y.: Cascading behaviour in complex socio-technical networks. J. Complex Netw. 1(1), 3–24 (2013)CrossRefGoogle Scholar
  8. 8.
    Brooks, L., Iba, W., Sen, S.: Modeling the emergence and convergence of norms. In: IJCAI, pp. 97–102 (2011)Google Scholar
  9. 9.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of International AAAI Conference on Weblogs and Social in ICWSM 2010 (2010)Google Scholar
  10. 10.
    David, E., Jon, K.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York (2010)zbMATHGoogle Scholar
  11. 11.
    Delgado, J.: Emergence of social conventions in complex networks. Artif. Intell. 141(1–2), 171–185 (2002)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Epstein, J.M.: Learning to be thoughtless: social norms and individual computation. Comput. Econ. 18(1), 9–24 (2001)CrossRefGoogle Scholar
  13. 13.
    Galán, J.M., Łatek, M.M., Rizi, S.M.M.: Axelrod’s metanorm games on networks. PLOS ONE 6(5), 1–11 (2011)CrossRefGoogle Scholar
  14. 14.
    Mahmoud, S., Miles, S., Luck, M.: Cooperation emergence under resource-constrained peer punishment. In: Proceedings of the 2016 International Conference on Autonomous Agents & #38; Multiagent Systems, AAMAS 2016, pp. 900–908. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2016)Google Scholar
  15. 15.
    Peleteiro, A., Burguillo, J.C., Chong, S.Y.: Exploring indirect reciprocity in complex networks using coalitions and rewiring. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2014, pp. 669–676. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2014)Google Scholar
  16. 16.
    Ranjbar-Sahraei, B., Bou Ammar, H., Bloembergen, D., Tuyls, K., Weiss, G.: Evolution of cooperation in arbitrary complex networks. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2014, pp. 677–684. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2014)Google Scholar
  17. 17.
    Savarimuthu, B.T.R., Cranefield, S., Purvis, M., Purvis, M.: Norm emergence in agent societies formed by dynamically changing networks. In: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2007, pp. 464–470. IEEE Computer Society, Washington (2007)Google Scholar
  18. 18.
    Sina, S., Hazon, N., Hassidim, A., Kraus, S.: Adapting the social network to affect elections. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 705–713. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2015)Google Scholar
  19. 19.
    Tsang, A., Larson, K.: Opinion dynamics of skeptical agents. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2014, pp. 277–284. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2014)Google Scholar
  20. 20.
    Villatoro, D., Andrighetto, G., Sabater-Mir, J., Conte, R.: Dynamic sanctioning for robust and cost-efficient norm compliance. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume One, IJCAI 2011, pp. 414–419. AAAI Press (2011)Google Scholar
  21. 21.
    Villatoro, D., Sen, S., Sabater-Mir, J.: Topology and memory effect on convention emergence. In: IAT (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Tandy School of Computer ScienceThe University of TulsaTulsaUSA

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