Price Stabilization in Networks — What Is an Appropriate Model ?

  • Jun Kiniwa
  • Kensaku Kikuta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6976)


We consider a simple network model for economic agents where each can buy commodities in the neighborhood. Their prices may be initially distinct in any node. However, by assuming some rules on new prices, we show that the distinct prices will converge to unique by iterating buy and sell operations. First, we present a protocol model in which each agent always bids an arbitrary price in the difference between his own price and the lowest price in the neighborhood, called max price difference. Next, we derive the condition that price stabilization occurs in our model. Furthermore, we consider game (auction) theoretic price determination by assuming that each agent’s value is uniformly distributed over the max price difference. Finally, we perform a simulation experiment. Our model is suitable for investigating the effects of network topologies on price stabilization.


multiagent model price determination game (auction) theory Bayesian-Nash equilibrium 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beauquier, J., Herault, T., Schiller, E.: Easy Stabilization with an Agent. In: Datta, A.K., Herman, T. (eds.) WSS 2001. LNCS, vol. 2194, pp. 35–50. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Blume, L.E., Easley, D., Kleinberg, J., Tardos, E.: Trading Networks with Price-Setting Agents. Games and Economic Behavior 67, 36–50 (2009)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Challet, D., Marsili, M., Zhang, Y.-C.: Minority Games — Interacting Agents in Financial Markets. Oxford University Press, New York (2005)MATHGoogle Scholar
  4. 4.
    Dolev, S., Kat, R.I., Schiller, E.M.: When Consensus Meets Self-stabilization. Journal of Computer and System Sciences 76(8), 884–900 (2010)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Dolev, S., Schiller, E.M., Spirakis, P.G., Tsigas, P.: Strategies for Repeated Games with Subsystem Takeovers Implementable by Deterministic and Self-stabilizing Automata. In: Proceedings of the 2nd International Conference on Autonomic Computing and Communication Systems (Autonomics 2008), pp. 23–25. ICST, Brussels (2008)Google Scholar
  6. 6.
    Dolev, S., Schiller, E.M., Welch, J.L.: Random Walk for Self-stabilizing Group Communication in Ad Hoc Networks. IEEE Transactions on Mobile Computing 5(7), 893–905 (2006)CrossRefGoogle Scholar
  7. 7.
    Dasgupta, A., Ghosh, S., Tixeuil, S.: Selfish Stabilization. In: Datta, A.K., Gradinariu, M. (eds.) SSS 2006. LNCS, vol. 4280, pp. 231–243. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Dolev, S.: Self-stabilization. The MIT Press, Cambridge (2000)MATHGoogle Scholar
  9. 9.
    Dolev, S., Israeli, A., Moran, S.: Analyzing Expected Time by Scheduler-Luck Games. IEEE Transactions on Software Engineering 21(5), 429–439 (1995)CrossRefGoogle Scholar
  10. 10.
    Even-Dar, E., Kearns, M., Suri, S.: A Network Formation Game for Bipartite Exchange Economies. In: Proceedings of the 18th ACM-SIAM Simposium on Discrete Algorithms (SODA 2007), pp. 697–706. ACM, SIAM, New York, Philadelphia (2007)Google Scholar
  11. 11.
    Ghosh, S.: Agents, Distributed Algorithms, and Stabilization. In: Du, D.-Z., Eades, P., Castro, V.E., Lin, X., Sharma, A. (eds.) COCOON 2000. LNCS, vol. 1858, pp. 242–251. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Gouda, M.G., Acharya, H.B.: Nash Equilibria in Stabilizing Systems. In: Guerraoui, R., Petit, F. (eds.) SSS 2009. LNCS, vol. 5873, pp. 311–324. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Herman, T., Masuzawa, T.: Self-stabilizing Agent Traversal. In: Datta, A.K., Herman, T. (eds.) WSS 2001. LNCS, vol. 2194, pp. 152–166. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Kakade, S.M., Kearns, M., Ortiz, L.E., Pemantle, R., Suri, S.: Economic Properties of Social Networks. In: Proceedings of the Neural Information Processing Systems (NIPS 2004), pp. 633–640. The MIT Press, Cambridge (2004)Google Scholar
  15. 15.
    Kiniwa, J., Kikuta, K.: Analysis of an Intentional Fault Which Is Undetectable by Local Checks under an Unfair Scheduler. In: Guerraoui, R., Petit, F. (eds.) SSS 2009. LNCS, vol. 5873, pp. 443–457. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Kiniwa, J., Kikuta, K.: A Network Model for Price Stabilization. In: Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011), pp. 394–397. SciTePress, Portugal (2011)Google Scholar
  17. 17.
    Krishna, V.: Auction Theory. Academic Press, Orlando (2002)Google Scholar
  18. 18.
    Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Publishers, San Francisco (1996)MATHGoogle Scholar
  19. 19.
    Myerson, R.B.: Game Theory: Analysis of Conflict. Harvard University Press, Cambridge (1991)MATHGoogle Scholar
  20. 20.
    Raberto, M., Cincotti, S., Dose, C., Focardi, S.M., Marchesi, M.: Price Formation in an Artificial Market: Limit Order Book Versus Matching of Supply and Demand. In: Thomas, L., Stefan, R., Eleni, S. (eds.) Nonlinear Dynamics and Heterogeneous Interacting Agents. LNEMS, vol. 550, pp. 305–315. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  21. 21.
    Stiglitz, J.E.: Principles of Micro-economics. W.W.Norton & Company, New York (1993)Google Scholar
  22. 22.
    Varian, H.R.: Microeconomic Analysis. W.W.Norton & Company, New York (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jun Kiniwa
    • 1
  • Kensaku Kikuta
    • 2
  1. 1.Department of Applied EconomicsUniversity of HyogoNishi-kuJapan
  2. 2.Department of Strategic ManagementUniversity of HyogoJapan

Personalised recommendations