Advertisement

Least Susceptible Networks to Systemic Risk

  • Ryota ZamamiEmail author
  • Hiroshi Sato
  • Akira Namatame
Chapter
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 669)

Abstract

There is empirical evidence that as the connectivity of a network increases, there is an increase in the network performance, but at the same time, there is an increase in the chance of risk contagion which is extremely large. If external shocks or excess loads at some agents are propagated to the other connected agents due to failure, the domino effects often come with disastrous consequences. In this paper, we design the least susceptible network to systemic risk. We consider the threshold-based cascade model, proposed by Watts [13]. We propose the network design model in which the associated adjacency matrix has the largest maximum eigenvalue. The topology of such a network is characterized as a core-periphery structures that consists of a partial complete graph of hub nodes and stub nodes that are connected to one of the hub nodes. The introduced network can reduce the turbulence of shocks triggered and prevent the spread of systemic risk. By both mathematical analysis and agent-based simulations, we show that the slightly differences of the structure of network causes systemic risk.

Keywords

Total Asset Degree Distribution Systemic Risk Threshold Model Scale Free Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Albert R (2000) Errror and attack tolerance of complex networ. Nature 406(27):378–382CrossRefGoogle Scholar
  2. 2.
    Barabási A, Albert R, Jeong H (2000) Scale-free characterixtics of random networks: the topology of the World-Wide Web. Phys A 281:69–77CrossRefGoogle Scholar
  3. 3.
    Erdös P, Rény A (1959) On random graphs. Publ Math (6):290–297Google Scholar
  4. 4.
    Granovetter M (1978) Threshold models of collective behavior. Am J Soc 83(6):1420–1443CrossRefGoogle Scholar
  5. 5.
    Gutfaind A (2010) Optimizing topological cascade resilience based on the structre of terrorist networks. PLos ONE 5(11):e13488Google Scholar
  6. 6.
    Komatsu T, Namatame A (2011) Dynamic diffusion processes in evolutionary optimized networks. Int J Bio Inspir Comput 3:384–392CrossRefGoogle Scholar
  7. 7.
    Komtasu T, Namatame A (2011) An evolutionary optimal network desighn to mitigate risk contagion. In: Proceeding of IEEE international conference natural computation (ICNC), Shanghai, vol 4, pp 1980–1985Google Scholar
  8. 8.
    López-Pintado D (2006) Contagion and coordination in random networks. Int J Game Theory 34(3):371–381CrossRefGoogle Scholar
  9. 9.
    Lorenz J, Battistion S, Schweitzer F (2009) Systemic risk in a unifying framework for cacading processes on networks. Eur Phys J B Condens Matter Complex Syst 71:441–460CrossRefGoogle Scholar
  10. 10.
    Motter AE, Lai YC (2002) Cascade-based attacks on complex networks. Phys Rev E 66(6):065102CrossRefGoogle Scholar
  11. 11.
    Schelling TC (1973) Hockey helmets, concealed weapons, and daylight saving: a study of binary choices with externalities. J Confl Resolut 17(3):381–428CrossRefGoogle Scholar
  12. 12.
    Stauffer D, Aharony A (1991) Introduction to percolation theory. Taylor and Francis, LondonGoogle Scholar
  13. 13.
    Watts D (2002) A simple model of global cascades on random networks. PNAS 99(9): 5766–5771CrossRefGoogle Scholar
  14. 14.
    Watts D (2007) Influentials, networks, and public opinion formation. J Consum Res 34: 441–458CrossRefGoogle Scholar
  15. 15.
    Wolframs S (1983) Statistical mechanism of cellular automata. Rev Mod Phys 55:501–644Google Scholar
  16. 16.
    Xu J, Wang XF (2005) Cacading failures in scale-free coupled map lattices. Phys A 349: 685–692CrossRefGoogle Scholar
  17. 17.
    Young P (2009) Innovation diffusion in heterogeneous populations: contagion, social influence, and social learning. Am Econ Rev 99(5):1899–1924CrossRefGoogle Scholar
  18. 18.
    Young P (2010) The dynamics of social innovation. PNAS 108(9)21285–21291Google Scholar
  19. 19.
    Yuan H (1988) A bound on the spectral radius of graphs. Linear Algebra Appl, 108:135–139. http://dx.doi.org/10.1016/0024-3795(88)90183-8

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of computer scienceNational Defense Academy of JapanYokosukaJapan

Personalised recommendations