Skip to main content

Impact of Social Network Structure on Social Welfare and Inequality

  • Chapter
  • First Online:
Social Networks: A Framework of Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 526))

Abstract

In this chapter, how the structure of a network can affect the social welfare and inequality (measured by the Gini coefficient) are investigated based on a graphical game model which is referred to as the Networked Resource Game (NRG). For the network structure, the Erdos–Renyi model, the preferential attachment model, and several other network structure models are implemented and compared to study how these models can effect the game dynamics. We also propose an algorithm for finding the bilateral coalition-proof equilibria because Nash equilibria do not lead to reasonable outcomes in this case. In economics, increasing inequalities and poverty can be sometimes interpreted as a circular cumulative causations, such positive feedback is also considered by us and a modified version of the NRG by considering the positive feedback (p-NRG) is proposed. The influence of network structures in this new model is also discussed at the end of this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://en.wikipedia.org/wiki/Congestion_game

  2. 2.

    http://en.wikipedia.org/wiki/Gini_coefficient

References

  1. Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)

    Google Scholar 

  2. Axelrod, R., Hamilton, W.D.: The evolution of cooperation. Science 211, 1390–1396 (1981)

    Google Scholar 

  3. Bowling, M.: Convergence and no-regret in multiagent learning. In: Advances in Neural Information Processing Systems 17 (NIPS), pp. 209–216, 2005. A longer version is available as a University of Alberta Technical Report, TR04-11

    Google Scholar 

  4. Duong, Q., Vorobeychik, Y., Singh, S., Wellman, M.P.: Learning graphical game models. In: IJCAI (2011)

    Google Scholar 

  5. Elkind, E., Goldberg, L., Goldberg, P.: Nash equilibria in graphical games on trees revisited. In: Proceedings of the 7th ACM Conference on Electronic Commerce, pp. 100–109. ACM, New York (2006)

    Google Scholar 

  6. Erdo, P., Renyi, A.: On random graphs. Mathematicae 6, 290–297 (1959)

    Google Scholar 

  7. Gini, C.: On the measure of concentration with special reference to income and statistics. Colorado College Publication (1936)

    Google Scholar 

  8. Greenwald, A., Hall, K.: Correlated q-learning. In: 20th International Conference on Machine Learning, pp. 242–249 (2003)

    Google Scholar 

  9. Groh, G., Lehmann, A., Reimers, J., Friess, M., Schwarz, L.: Detecting social situations from interaction geometry. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 1–8 (2010)

    Google Scholar 

  10. Heckerman, D., Geiger, D., Chickering, D.M.: Learning bayesian networks: the combination of knowledge and statistical data. In: Machine Learning, pp. 197–243 (1995)

    Google Scholar 

  11. Hsieh, H.-P., Li, C.-T.: Mining temporal subgraph patterns in heterogeneous information networks. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 282–287 (2010)

    Google Scholar 

  12. Irving, R.W.: An efficient algorithm for the stable roommates problem. J. Algorithms. 6(4), 577–595 (1985)

    Google Scholar 

  13. Jackson, M.O.: Allocation rules for network games. Games Econ. Behav. 51(1), 128–154 (2005)

    Google Scholar 

  14. Jackson, M.O., Watts, A.: The evolution of social and economic networks. J. Econ. Theor. 106(2), 265–295 (2002)

    Google Scholar 

  15. Kakhbod, A., Teneketzis, D.: Games on social networks: on a problem posed by goyal. CoRR. abs/1001.3896 (2010)

    Google Scholar 

  16. Kearns, M., Littman, M., Singh, S.: Graphical models for game theory. In: Conference on Uncertainty in Artificial Intelligence, pp. 253–260 (2001)

    Google Scholar 

  17. E. Kim, L. Chi, R. Maheswaran, and Y.-H. Chang. Dynamics of behavior in a network game. In IEEE International Conference on Social Computation (2011)

    Google Scholar 

  18. Li, Z., Chang, Y.-H., Maheswaran, R.T.: Graph formation effects on social welfare and inequality in a networked resource game. In: SBP, pp. 221–230 (2013)

    Google Scholar 

  19. Luca, M.D., Cliff, D.: Human-agent auction interactions: adaptive-aggressive agents dominate. In: IJCAI (2011)

    Google Scholar 

  20. Luo, L., Chakraborty, N., Sycara, K.: Prisoner’s dilemma in graphs with heterogeneous agents. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 145–152 (2010)

    Google Scholar 

  21. Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V. (eds.): Algorithmic Game Theory. Cambridge University Press, New York (2007)

    Google Scholar 

  22. Ortiz, L., Kearns, M.: Nash propagation for loopy graphical games. In: Neural Information Processing Systems (2003)

    Google Scholar 

  23. Erdos, P., Renyi, A.: On random graphs. Publicationes Mathematicae 6, 290–297 (1959)

    Google Scholar 

  24. Qiu, B., Ivanova, K., Yen, J., Liu, P.: Behavior evolution and event-driven growth dynamics in social networks. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 217–224 (2010)

    Google Scholar 

  25. Shoham, Y.: Computer science and game theory. Commun. ACM 51(8), 74–79 (2008)

    Google Scholar 

  26. Vickrey, D., Koller, D.: Multi-agent algorithms for solving graphical games. In: National Conference on Artificial Intelligence (AAAI) (2002)

    Google Scholar 

  27. von Neumann J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)

    Google Scholar 

  28. Wani, M.A., Li, T., Kurgan, L.A., Ye, J., Liu, Y. (eds.): In: The Fifth International Conference on Machine Learning and Applications, ICMLA 2006, Orlando, Florida, USA, 14–16 December 2006. IEEE Computer Society (2006)

    Google Scholar 

  29. Yang, X.-S. (ed.): Artificial Intelligence, Evolutionary Computing and Metaheuristics—In the Footsteps of Alan Turing, Studies in Computational Intelligence. vol. 427, Springer, Berlin (2013)

    Google Scholar 

  30. Yitzhaki, S.: More than a dozen alternative ways of spelling gini economic inequality. Econ. Inequality 8, 13–30 (1998)

    Google Scholar 

Download references

Acknowledgments

This research is partly funded by the NCET Program, Ministry of Education, China, and the National Natural Science Foundation of China (NSFC) under Grant No. 61305047.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhuoshu Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Li, Z., Qin, Z. (2014). Impact of Social Network Structure on Social Welfare and Inequality. In: Pedrycz, W., Chen, SM. (eds) Social Networks: A Framework of Computational Intelligence. Studies in Computational Intelligence, vol 526. Springer, Cham. https://doi.org/10.1007/978-3-319-02993-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02993-1_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02992-4

  • Online ISBN: 978-3-319-02993-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics