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A context-aware convention formation framework for large-scale networks

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Abstract

In this article, we present a decentralized convention formation framework for creating social conventions within large multiagent convention spaces. We study the role of the topological characteristics of the network in forming conventions with an emphasis on scale-free topologies. We hypothesize that contextual knowledge encapsulated in the topology can help improve both the quality of the emergent convention and the speed of forming such a convention. We also investigate the influence of network diversity. While recent research on diversity indicates that it improves organizational productivity, we observe that not all diversity is equally useful and identify the necessary conditions to maximize the benefit of diversity. We validate our convention formation framework using a language coordination problem in which agents in a multiagent system construct a common lexicon in a decentralized fashion. Agent interactions are modeled using a language game where every agent repeatedly plays with its neighbors. Each agent stochastically updates its lexicon based on the utility values of the lexicons received from its immediate neighbors. We introduce a novel context-aware utility computation mechanism and equip the agents with the ability to reorganize their neighborhood based on this utility estimate to expedite the convention formation process. A key idea behind our approach is the ability of socially influential high-utility-lexicon agents to bias their neighbors towards accepting their lexicons. Extensive experimentation results indicate that our proposed solution is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces.

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Notes

  1. Henceforth these two approaches are referred as SRA and FGJ respectively.

  2. In SRA as well as FGJ, the time-period for investigating the emergence of a lexicon convention is comprised of 100,000 time-steps of the experiment. We use this duration as a definition of a reasonable amount of time for convergence to occur.

  3. Throughout the article, we use agent and node interchangeably.

References

  1. Pujol, J. M., Delgado, J., Sangesa, R., & Flache, A. (2005). The role of clustering on the emergence of efficient social conventions. In L. P. Kaelbling & A. Saffiotti (Eds.), The international joint conference on artificial intelligence (IJCAI) (pp. 965–970).

  2. Sugawara, T. (2011). Emergence and stability of social conventions in conflict situations. In T. Walsh (Ed.), The international joint conference on artificial intelligence (IJCAI) (pp. 371–378). IJCAI/AAAI.

  3. Walker, A., & Wooldridge, M. (1995). Understanding the emergence of conventions in multi-agent systems. In V. R. Lesser & L. Gasser (Eds.), Proceedings of the first international conference on multiagent systems (ICMAS) (pp. 384–389). The MIT Press.

  4. Criado, N., Argente, E., Garrido, A., Gimeno, J. A., Igual, F., Botti, V. J., et al. (2011). Norm enforceability in electronic institutions? In M. De Vos, N. Fornara, J. V. Pitt, & G. Vouros (Eds.), Coordination, organizations, institutions, and norms in agent systems VI, lecture notes in computer science (Vol. 6541, pp. 250–267).

  5. Davidsson, P., & Jacobsson, A. (2007). Aligning models of normative systems and artificial societies: Towards norm-governed behavior in virtual enterprises. In G. Boella, L. W. N. van der Torre & H. Verhagen (Eds.), Normative multi-agent systems, ser. Dagstuhl seminar proceedings (Vol. 07122). Internationales Begegnungs- und Forschungszentrum fr Informatik (IBFI), Schloss Dagstuhl, Germany.

  6. Li, S., & Chen, Z. (2010). Social services computing: Concepts, research challenges, and directions. In Proceedings of the 2010 IEEE/ACM international conference on green computing and communications & international conference on cyber, physical and social computing, ser. GREENCOM-CPSCOM ’10 (pp. 840–845). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/GreenCom-CPSCom.2010.122.

  7. Artikis, A., Kamara, L., Pitt, J., & Sergot, M. J. (2004). A protocol for resource sharing in norm-governed ad hoc networks. In J. A. Leite, A. Omicini, P. Torroni & P. Yolum (Eds.), Declarative agent languages and technologies (DALT), ser. lecture notes in computer science (Vol. 3476, pp. 221–238). Springer.

  8. Salazar, N., Rodríguez-Aguilar, J. A., & Arcos, J. L. (2010). Self-configuring sensors for uncharted environments. In Fourth IEEE international conference on self-adaptive and self-organizing systems, SASO 2010, Budapest, Hungary, 27 September–1 October 2010 (pp. 134–143). https://doi.org/10.1109/SASO.2010.38.

  9. Mihaylov, M., Tuyls, K., & Now, A. (2014). A decentralized approach for convention emergence in multi-agent systems. Autonomous Agents and Multi-Agent Systems, 28(5), 749–778. https://doi.org/10.1007/s10458-013-9240-2.

    Article  Google Scholar 

  10. Nowak, M. A., Plotkin, J. B., & Krakauer, D. (1999). The evolutionary language game. Journal of Theoretical Biology, 200(2), 147–162. http://www.isrl.uiuc.edu/~amag/langev/paper/nowak99theEvolutionary.html.

  11. Castellano, C., Loreto, V., Barrat, A., Cecconi, F., & Parisi, D. (2005). Comparison of voter and glauber ordering dynamics on networks. Physical Review E, 71, 066107.

    Article  Google Scholar 

  12. Delgado, J. (2002). Emergence of social conventions in complex networks. Artificial Intelligence, 141(1–2), 171–185.

    Article  MathSciNet  Google Scholar 

  13. Villatoro, D., Sen, S., & Sabater-Mir, J. (2009). Topology and memory effect on convention emergence. In The proceedings of the intelligent agent technology (IAT) (pp. 233–240).

  14. Hasan, M. R., & Raja, A. (2013). Emergence of cooperation using commitments and complex network dynamics. In The proceedings of the intelligent agent technology (IAT) (pp. 345–352).

  15. Salazar, N., Rodriguez-Aguilar, J. A., & Arcos, J. L. (2010). Robust coordination in large convention spaces. AI Communications, 23(4), 357–372. http://dl.acm.org/citation.cfm?id=1898063.1898068.

  16. Franks, H., Griffiths, N., & Jhumka, A. (2013). Manipulating convention emergence using influencer agents. Autonomous Agents and Multi-Agent Systems, 26(3), 315–353. https://doi.org/10.1007/s10458-012-9193-x.

    Article  Google Scholar 

  17. Kittock, J. E. (1995). Emergent conventions and the structure of multi-agent systems. In L. Nadel & D. Stein (Eds.), Studies in the sciences of complexity. Reading, MA: Addison-Wesley.

    Google Scholar 

  18. Shoham, Y., & Tennenholtz, M. (1992). Emergent conventions in multi-agent systems: Initial experimental results and observations. In The 3rd international conference on principles of knowledge representation and reasoning (pp. 225–231). San Mateo, CA: Morgan Kauffmann.

  19. Steels, L. (1999). The talking heads experiment. Volume 1. Words and meanings. Antwerpen: Laboratorium.

    Google Scholar 

  20. Savarimuthu, B. T. R., Purvis, M., Purvis, M. K., & Cranefield, S. (2008). Social norm emergence in virtual agent societies. In M. Baldoni, T. C. Son, M. B. van Riemsdijk & M. Winikoff (Eds.), DALT, ser. Lecture notes in computer science (Vol. 5397, pp. 18–28). Springer.

  21. Lifton, J., Laibowitz, M., Harry, D., Gong, N.-W., Mittal, M., & Paradiso, J. A. (2009). Metaphor and manifestation—Cross-reality with ubiquitous sensor/actuator networks. IEEE Pervasive Computing, 8(3), 24–33.

    Article  Google Scholar 

  22. Dublon, G., & Paradiso, J. (2014). How a sensor-filled world will change human consciousness. Scientific American, 311(1), 36–41.

    Article  Google Scholar 

  23. Hasan, M. R. (2013). Emergence of privacy conventions in online social networks. In The proceedings of the international conference on autonomous agents and multiagent systems (AAMAS 2013) (pp. 1433–1434).

  24. Kumaraguru, P., & Cranor, L. F. (2005). Privacy indexes: A survey of westin’s studies. Institute for Software Research International (ISRI), Carnegie Mellon University, Tech. Rep.

  25. Catanese, S., Meo, P. D., Ferrara, E., Fiumara, G., & Provetti, A. (2011). Extraction and analysis of facebook friendship relations. In A. Abraham (Ed.), Computational social networks: Mining and visualization. London: Springer.

    Google Scholar 

  26. Villatoro, D. (2011). Social norms for policing multi-agent systems and virtual societies. PhD Dissertation, Universitat Autnoma de Barcelona.

  27. Nowak, M. A., & May, R. M. (1992). Evolutionary games and spatial chaos. Nature, 359, 826–829.

    Article  Google Scholar 

  28. Salazar, N., Juan, A., Aguilar, R., Arcos, J. L., Peleteiro, A., & Burguillo-Rial, J. (2011). Emergence of cooperation on complex networks. In The proceedings of the international conference on autonomous agents and multiagent systems (AAMAS 2011) (pp. 669–676).

  29. DeVylder, B. (2007). The evolution of conventions in multi-agent systems. PhD Dissertation, Artificial Intelligence Lab Vrije Universiteit Brussel.

  30. Dorogovtsev, S. N., & Mendes, J. F. F. (2003). Evolution of networks: From biological nets to the Internet and WWW. Oxford: Oxford University Press.

    Book  MATH  Google Scholar 

  31. Barrat, A., Barthlemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. New York: Cambridge University Press.

    Book  MATH  Google Scholar 

  32. Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.

    Book  MATH  Google Scholar 

  33. Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  34. Watts, D., & Strogatz, S. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.

    Article  MATH  Google Scholar 

  35. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826. http://www.pnas.org/content/99/12/7821.abstract.

  36. Caldarelli, G., & Vespignani, A. (2007). Large scale structure and dynamics of complex networks: From information technology to finance and natural science. River Edge, NJ: World Scientific Publishing Co., Inc.

    Book  MATH  Google Scholar 

  37. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosphical Society, 106(6), 467–482.

    Google Scholar 

  38. Seshadhri, C., Kolda, T. G., & Pinar, A. (2012). Community structure and scale-free collections of Erdős–Rényi graphs. Physical Review E, 85(5), 056109.

    Article  Google Scholar 

  39. Kolda, T. G., Pinar, A., Plantenga, T., & Seshadhri, C. (2013). A scalable generative graph model with community structure. February.

  40. Lancichinetti, A., Kivelä, M., Saramäki, J., & Fortunato, S. (2010). Characterizing the community structure of complex networks. CoRR. arXiv:1005.4376.

  41. Villatoro, D., Sabater-Mir, J., & Sen, S. (2011). Social instruments for robust convention emergence. In The international joint conference on artificial intelligence (IJCAI) (pp. 420–425).

  42. Abdallah, S. (2012). Using a hierarchy of coordinators to overcome the frontier effect in social learning. In The proceedings of the international conference on autonomous agents and multiagent systems (AAMAS 2012) (pp. 1381–1382).

  43. Marchant, J., Griffiths, N., & Leeke, M. (2015). Convention emergence and influence in dynamic topologies. In Proceedings of the 2015 international conference on autonomous agents and multiagent systems, ser. AAMAS ’15 (pp. 1785–1786). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems. http://dl.acm.org/citation.cfm?id=2772879.2773436.

  44. Hasan, M. R., Abdallah, S., & Raja, A. (2014). Topology aware convention emergence. In The proceedings of the international conference on autonomous agents and multiagent systems (AAMAS 2014) (pp. 1593–1594).

  45. Ohtsuki, H., Hauert, C., Lieberman, E., & Nowak, M. A. (2006). A simple rule for the evolution of cooperation on graphs and social networks. Nature, 441(7092), 502–505.

    Article  Google Scholar 

  46. Hasan, M. R., Raja, A., & Bazzan, A. L. C. (2015). Fast convention formation in dynamic networks using topological knowledge. In Proceedings of the twenty-ninth AAAI conference on artificial intelligence, January 25–30, Austin, Texas, USA (pp. 2067–2073).

  47. Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010). Measuring user influence in twitter: The million follower fallacy. In 4th International association for the advancement of artificial intelligence conference on weblogs and social media (ICWSM).

  48. Kooti, F., Mason, W. A., Gummadi, P. K., & Cha, M. (2012). Predicting emerging social conventions in online social networks. In X. Wen Chen, G. Lebanon, H. Wang & M. J. Zaki (Eds.), The proceedings of the international conference on information and knowledge management (CIKM) (pp. 445–454). ACM.

  49. Shoham, Y., & Tennenholtz, M. (1993). Co-learning and the evolution of social activity. Department of Computer Science, Stanford University, Tech. Rep.

  50. Sen, S., & Airiau, S. (2007). Emergence of norms through social learning. In The international joint conference on artificial intelligence (IJCAI) (pp. 1507–1512).

  51. Mukherjee, P., Sen, S., & Airiau, S. (2008). Norm emergence under constrained interactions in diverse societies. In The proceedings of the international conference on autonomous agents and multiagent systems (AAMAS 2008) (pp. 779–786).

  52. Savarimuthu, B. T. R., & Cranefield, S. (2011). Norm creation, spreading and emergence: A survey of simulation models of norms in multi-agent systems. Multiagent Grid Systems, 7(1), 21–54. http://dl.acm.org/citation.cfm?id=2019196.2019199.

  53. Savarimuthu, B. T. R., Cranefield, S., Purvis, M. K., & Purvis, M. A. (2009). Norm emergence in agent societies formed by dynamically changing networks. Web Intelligence and Agent Systems, 7(3), 223–232. https://doi.org/10.3233/WIA-2009-0164.

    Google Scholar 

  54. Gkantsidis, C., Goel, G.,  Mihail, M., & Saberi, A. (2007). Towards topology aware networks. In INFOCOM 2007. 26th IEEE international conference on computer communications, joint conference of the IEEE computer and communications societies, 6–12 May 2007, Anchorage, Alaska, USA (pp. 2591–2595). https://doi.org/10.1109/INFCOM.2007.327.

  55. Steels, L. (1995). A self-organizing spatial vocabulary. Artificial Life, 2(3), 319–332.

    Article  Google Scholar 

  56. Kim, B. J., Trusina, A., Holme, P., Minnhagen, P., Chung, J. S., & Choi, M. Y. (2002). Dynamic instabilities induced by asymmetric influence: Prisoners’ dilemma game in small-world networks. Physical Review E, 66, 021907.

    Article  Google Scholar 

  57. Page, S. E. (2007). The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton, NJ: Princeton University Press.

    Google Scholar 

  58. Perc, M., & Szolnoki, A. (2008). Social diversity and promotion of cooperation in the spatial prisoner’s dilemma game. Physical Review E, 77, 011904. https://doi.org/10.1103/PhysRevE.77.011904.

    Article  Google Scholar 

  59. Fu, F., Hauert, C., Nowak, M. A., & Wang, L. (2008). Reputation-based partner choice promotes cooperation in social networks. Physical Review E, 78, 026117. https://doi.org/10.1103/PhysRevE.78.026117.

    Article  Google Scholar 

  60. Pacheco, J., Traulsen, A., & Nowak, M. (2006). Coevolution of strategy and structure in complex networks with dynamical linking. Physical Review Letters, 97, 258103.

    Article  Google Scholar 

  61. Ugander, J., Karrer, B., Backstrom, L., & Marlow, C. (2011). The anatomy of the facebook social graph. Computing Research Repository (CoRR). arXiv:1111.4503.

  62. Antonakaki, D., Ioannidis, S., & Fragopoulou, P. (2018). Utilizing the average node degree to assess the temporal growth rate of twitter. Social Network Analysis and Mining, 8(1), 12. https://doi.org/10.1007/s13278-018-0490-5.

    Article  Google Scholar 

  63. Mitra, C., Kurths, J., & Donner, R. V. (2017). Rewiring hierarchical scale-free networks: Influence on synchronizability and topology. Computing Research Repository (CoRR). arXiv:1707.04057.

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Acknowledgements

We thank Professor Sherief Abdallah for his contributions towards our initial investigations of scale-free networks. We also thank the two anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. University of Nebraska-Lincoln, Lincoln, NE, 68588, USA

    Mohammad Rashedul Hasan

  2. The Cooper Union, New York, NY, 10003, USA

    Anita Raja

  3. PPGC/UFRGS, CP 15064, Porto Alegre, RS, 91501-970, Brazil

    Ana Bazzan

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Hasan, M.R., Raja, A. & Bazzan, A. A context-aware convention formation framework for large-scale networks. Auton Agent Multi-Agent Syst 33, 1–34 (2019). https://doi.org/10.1007/s10458-018-9397-9

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