Robustness of critical points in a complex adaptive system: Effects of hedge behavior

Abstract

In our recent papers, we have identified a class of phase transitions in the market-directed resourceallocation game, and found that there exists a critical point at which the phase transitions occur. The critical point is given by a certain resource ratio. Here, by performing computer simulations and theoretical analysis, we report that the critical point is robust against various kinds of human hedge behavior where the numbers of herds and contrarians can be varied widely. This means that the critical point can be independent of the total number of participants composed of normal agents, herds and contrarians, under some conditions. This finding means that the critical points we identified in this complex adaptive system (with adaptive agents) may also be an intensive quantity, similar to those revealed in traditional physical systems (with non-adaptive units).

Keywords

complex adaptive system / phase transition / resource allocation / hedge behavior / agentbased simulation

References

[1] W. Wang, Y. Chen, and J. P. Huang, Heterogeneous preferences, decision-Making capacity and phase transitions in a complex adaptive system, Proc. Natl. Acad. Sci. USA, 2009, 106(21): 8423

[2] L. Zhao, G. Yang, W. Wang, Y. Chen, J. P. Huang, H. Ohashi, and H. E. Stanley, Herd behavior in a complex adaptive system, Proc. Natl. Acad. Sci. USA, 2011, 108(37): 15058

[3] Y. Liang, K. N. An, G. Yang, and J. P. Huang, Contrarian behavior in a complex adaptive system, Phys. Rev. E, 2013, 87(1): 012809

[4] L. Guo, Y. F. Chang, and X. Cai, The evolution of opinions on scalefree networks, Front. Phys. China, 2006, 1(4): 506

[5] J. Zhou and Z. H. Liu, Epidemic spreading in complex networks, Front. Phys. China, 2008, 3(3): 331

[6] J. L. Ma and F. T. Ma, Solitary wave solutions of nonlinear financial markets: Data-modeling-concept-practicing, Front. Phys. China, 2007, 2(3): 368

[7] J. Q. Fang, Q. Bi, and Y. Li, Advances in theoretical models of network science, Front. Phys. China, 2007, 2(1): 109

[8] J. H. Holland, Adaptation in Natural and Artificial Systems, Cambridge, MA: MIT Press, 1992

[9] W. B. Arthur, Inductive reasoning and bounded rationality (The El Farol problem), Am. Econ. Assoc. Papers Proc., 1994, 84: 406

[10] D. Challet and Y. C. Zhang, Emergence of cooperation and organization in an evolutionary game, Physica A, 1997, 246(3–4): 407

[11] N. F. Johnson, P. Jefferies, and P. M. Hui, Financial Market Complexity, Oxford: Oxford University Press, 2003

[12] L. Tesfatsion, Agent-based computational economics: Modeling economies as complex adaptive systems, Inform. Sci., 2003, 149(4): 263

[13] J. D. Farmer and D. Foley, The economy needs agent-based modeling, Nature, 2009, 460(7256): 685

[14] L. X. Zhong, D. F. Zheng, B. Zheng, and P. M. Hui, Effects of contrarians in the minority game, Phys. Rev. E, 2005, 72(2): 026134

[15] Q. Li, L. A. Braunstein, S. Havlin, and H. E. Stanley, Strategy of competition between two groups based on an inflexible contrarian opinion model, Phys. Rev. E, 2011, 84(6): 066101

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Higher Education Press and Springer-Verlag Berlin Heidelberg