用户名: 密码: 验证码:
复杂系统中集体行为和临界现象的动力学研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
与人类活动息息相关的生态环境、社会组织、经济体系、信息传播、工程技术等,甚至作为生命体的人类本身,都属于复杂系统范畴。在人类文明史上,诸类现象的研究形成了多种知识体系和方法论。近年海量数据的积累,正孕育着对经典理论的全面挑战。当知识从直觉转换为数据时,人类对环境的认知是否会产生质的飞跃?观念的飞跃和数学化是否导致深层次普遍性规律的发现?类似物理理论的建立又能否从根本上提升人类掌控自然的能力?这都是复杂系统研究所面临的挑战。
     复杂系统研究的核心问题:构成系统的大量的子系统是如何通过相互作用而自组织成为一个具有涌现行为结构的新的开放的非平衡系统?复杂系统研究的科学目标是产生新的技术(包括新的软件),对于复杂系统科学中基本问题的更深层次的理解。
     统计力学作为研究多体系统微观与宏观之间相互联系的数学理论,在系统地整理各类测量数据、建立相应的动力学模型进而分析其特性、寻找系统运动的规律等方面可发挥主导作用。近年来复杂网络的研究显示,不同范畴的系统在组织结构上可有惊人的相似,暗示复杂系统深层次的共性。统计力学研究是揭示普适性规律的重要途径.本文利用统计力学的多种手段对复杂系统中若干集体行为和临界现象的动力学进行研究,并发现了一些有趣的结果。
     文中通过引进截断参数K和排队等待时间提出了两种建立在最近邻信息的局域路由策略,发现通过调节优先传递指数α和K,被序参量刻画的无标度网络处理能力显著提高,文中讨论了接近和远离临界产生率Rc的交通动力学,发现负载流的时间序列不仅依靠产生率R还与参数K和α有关,最后研究了临界产生率和连接密度m的函数关系。考虑到获得最近邻信息及显著改善网络处理能力的低花费,文中提出的策略将对现代通讯网络的协议设计很有意义。
     在原始的BML模型上我们改变网络的拓扑结构,试图探讨道路网络中反映网络拓扑结构的参量——平均的边和节点数目比值λ=E/N的变化对于交通拥堵出现的影响。考察了这上面车辆交通的动力学过程,发现道路结构对于交通拥堵的出现有着显著的关系,采取恰当的拓扑结构可以增加道路网络的吞吐量,为改善拥堵提出可能的理论依据。
     另外,讨论了复杂网络上的博弈。我们提出了基于收益的策略学习机制的囚徒博弈模型并研究了该模型在不同网络结构上(规则网络、小世界网络和无标度网络)的动力学性质,发现了在规则网络上的合作者倾向于分散,我们详细的讨论了无标度网络上的博弈行为,发现无标度网络上比起小度节点,度大的(?)节点不倾向于合作易于获得最大的收益,而度小的节点更频繁的变化自己的博弈策略。
     对于舆论动力学我们提出了一个引入移动机制的模型,并对其更新策略的机制进行了研究,找到了一个最佳的更新策略,并研究了系统由混合意见达到意见一致的相变过程的动力学问题。
     在三维Vicsek模型中引入粒子的视野角的概念,试图寻找系统是否存在着一个最佳的视野角使系统最快的达到同步,我们发现了这个最佳视野角,并研究了在热力学极限下,最佳视野角与系统粒子密度和初始速度之间的关系,给出了定量的分析。并利用序参量对系统从集体无序到集体有序这一集体行为进行刻画,对相关的临界现象进行了分析。
     最后,我们对Eden模型也作了进一步推广,提出来具有屏蔽效应的广义Eden模型,给出了相关的解析,这个结果可能对限制集聚扩散的生长机制给出解释。
Complex system is a very broad domain, including social organizations, economic systems, information diffusion, industrial technology, human beings, etc, on which various knowledge systems and methodologies have been established. However, classical theories are facing a great challenge due to the emergence of huge amount of data. We are confronting many unknown questions when knowledge is transferred from intuition to data:whether our cognition of the environment can achieve a qualitative leap? Whether this leap and can lead to discovery of deep universal law? Whether the foundation of new theory improves our ability to control the nature?
     The core issues of complex system:how the emergence appears in a new non-equilibrium open-structure system self-organized by the large number of subsystems? The objectives of complex system science:to generate new technologies (including new software) and the deeper understanding about the basic problems of complex system.
     Statistical mechanics is a theory bridge micro and macro many-body system. It can play a important role in organizing all kinds of measurement data systematically, establishing appropriate kinetic model to obtain its properties and finding the law of movement and other aspects of the system. The studies of complex networks have shown that the organizational structure in different areas can have surprisingly similarity in recent years, which suggests the common underlying in complex system. Statistical mechanics is an important way to reveal the universal law. In this paper, we study a number of critical phenomena and collective behaviors in complex systems by various means of statistical mechanics and find some interesting results.
     We propose a effective routing strategy on the basis of the so-called nearest neighbor search strategy by introducing a preferential delivering exponent a and a waiting time exponentβ. We assume that the handling capacity of one vertex is proportional to its degree when the degree is smaller than a cut-off value K, and is infinite otherwise. Traffic dynamics both near and far away from the critical generating rate Rc are discussed. Simulation results demonstrate that the optimal a,βand K. Our strategy may be useful and reasonable for the protocol designing of modern communication networks.
     We study the traffic dynamics in two-dimensional Biham-Middleton-Levine(BML) traffic flow model based on the irregular lattice. It is found that a phase separation phenomenon, in which the system separates into coexistence of free flow and jam, could be observed in intermediate vehicle density range.
     We investigate the prisoner's dilemma game based on a new rule:players will change their current strategies to opposite strategies with some probability if their neighbors'average payoffs are higher than theirs. Compared with the cases on regular lattices (RL) and Newman-Watts small world network (NW), cooperation can be best enhanced on the scale-free Barabasi-Albert network (BA). It is found that cooperators are dispersive on RL network, which is different from previously reported results that cooperators will form large clusters to resist the invasion of defectors. Cooperative behaviors on the BA network are discussed in detail.
     We generalize a majority-rule model on the opinion dynamics, where all mobile agents update their status based on Vicsek model. We find the existence of an optimal preferential rule for which the convergence to global consensus can be achieved much more rapidly than under other rules. Qualitative insights are obtained by examining the spatiotemporal evolution of the opinion clusters.
     The three-dimensional Vicsek model is introduced to investigate the collective motion of self-propelled particles. In this model each particle has a limited view angle. We find that there exists an optimal value of view angle, leading to the quickest direction consensus. Our work shows that the optimal view angle is just a function of the absolute velocity and the particles density in the limit of large boundary size. We present a series of special results demonstrating more fundamental and practical rules in the daily-life pragmatic collective phenomena, which is of significance to the low-cost communication.
     We generalize the Eden model to take into account of the screening effect, i.e., the end point grows much faster than the interior points do. Highly anisotropic clusters are obtained in our generalized Eden model. It is found that the length in the long direction scales differently than that in the short direction does as the number of site increases.
引文
1. Anderson P. W. Science, New Series177,4047 pp.393(1972).
    2.汪秉宏,周涛,韩筱璞复杂科学与复杂网络上海系统科学出版社P72(2010).
    3. Feynman, R.P. The Feynman Lecture on Physics 1, Chapter 56:Probability (Addision-Wesley, Redwood City, California) (1989).
    4. Barber, M.N. and Ninham, B.W. Random walks and restricted walks (Gordon and Breach, New York)(1970).
    5. Witten, T.A. and Sander, L.M. Diffusion-limited aggregation, a kinetic critical phenomenon, Phys. Rev. Lett.47,1400(1981).
    6. Milgram S., The small world problem, Psychol. Today 1,60(1967).
    7. Watts D.J. and Strogaz S.H., Collective dynamics of small-world network, Nature393,440(1998).
    8. Albert R., and Barabasi A. L., Emergence of Scaling in Random Networks. Science 286,509 (1999); Statistical mechanics of complex networks. Reviews of Modern Physics 74,47 (2002)
    9. Newman M. E. J. and Watts D. J., Scaling and percolation in the small-world network model. Phys. Rev. E 60,7332(1999).
    10. Tadic B., Thurner S., and Rodgers G. J., Phys. Rev. E 69,036102 (2004).
    11. M. A. de Menezes, Barabasi A.-L.. Phys. Rev. Lett.92,28701 (2004).
    12. Echenique P., Gomez-Gardenes J., Moreno Y., Improved routing strategies for Internet traffic delivery. Phys. Rev. E 70,056105 (2004).
    13. Zhao L., Park K., Lai Y.C., Local versus global thermal states:Correlations and the existence of local temperatures. Phys. Rev. E 70,035101(R)(2004).
    14. Wang W. X., Yin C. Y., Yan G., and Wang B. H., Integrating local static and dynamic information for routing traffic. Phys. Rev. E 74,016101 (2006).
    15. Chen Z. Y. and Wang X. F., Effects of network structure and routing strategy on network capacit. Phys. Rev. E73,036107(2006).
    16. Zonghua Liu, Xiaoyan Wu, and P. M. Hui, "An alternative approach to characterize the topology of complex networks and its applicationin epidemic spreading", Front. Comput. Sci. China 3,324(2009);Ming Tang, Zonghua Liu, Xiaoming Liang, and P. M. Hui, Self-adjusting routing schemes for time-varying traffic in scale-free networks, PHYSICAL REVIEW E 80, 026114(2009
    17. Ling X., Hu M.-B., Wang R.-L., Cao X.-B. and Wu Q.-S., Pheromone routing protocol on a scale-free network. Phys. Rev. E 80,066110 (2009).
    18. Adrian Cho, Ourselves and Our Interactions:The Ultimate Physics Problem? Science 325, 406(2009).
    19.姜锐,交通流复杂动态特征的微观和宏观模式研究,Ph. D thesis,中国科学技术大学
    20.段进宇,缪立新,交通流概论,in:[19], pp.5-16.
    21. Wolfram S., Statistical mechanics of cellular automata, Rev. Mod. Phys.55,601(1983).
    22. Wolfram S., Theory and application of cellular automata, World Scientific Singapore, (1986).
    23.司马迁,史记,中华书局(1959)
    24. Von Neumann J. and Morgenstern O., Theory of Games and Economic Behaviour Princeton University Press, Princeton(1944).
    25. Sugden R., The Economics of Rights, Co-operation and Welfare Blackwell, Oxford, U.K.(1986).
    26. Genetic and Cultural Evolution of Cooperation, edited by Peter Hammerstein MIT, Cambridge, MA(2003).
    27. J. Maynard Smith, Evolution and the Theory of Games Cambridge University Press, Cambridge, England(1982).
    28. Hofbauer J. and Sigmund K., Evolutionary Games and Population Dynamics Cambridge University Press, Cambridge, U.K.(1998).
    29. Faloutsos M, Faloutsos P, Faloutsos C. Computer Communications Review 29,251, (1999).
    30. M. Nowak and K. Sigmund, Tit for tat in heterogeneous populations. Nature (London) 355, 250(1992).
    31. M. Nowak, and K. Sigmund, A strategy of win-stay, lose-shift that outperforms tit-for-tat in Prisoner's Dilemma. Nature (London) 364,56 (1993).
    32. M. Nowak and R. M. May, Nature (London) 359,826 (1992); Int. J. Bifurcation Chaos Appl. Sci. Eng.3,35 (1993).
    33. Hauert C. and Doebeli M., Spatial structure often inhibits the evolution of cooperation in the snow drift game, Nature 428,643 (2004).
    34. Abramson G. and Kuperman M., Social games in a social network, Phys. Rev. E 63,030901(R) (2001).
    35. Santos F. C. and Pacheco J. M., Scale-free networks provide a unifying framework for the emergence of cooperation, Phys. Rev. Lett.95,098104 (2005).
    36. Gomez-Gardenes J., Campillo M., Floria L.M., and Moreno Y., Dynamical Organization of Cooperation in Complex Topologies, Phys. Rev. Lett.98,108103 (2007).
    37. Wang W.-X., Ren J., Chen G., and Wang B.-H., Memory-based snowdrift game on networks, Phys. Rev. E 74,056113 (2006).
    38. Ren J., Wang W.-X., and Qi F., Randomness enhances cooperation:A resonance-type phenomenon in evolutionary games, Phys. Rev. E 75,045101 (2007).
    39. Reynolds C, Flocks, herds, and schools:A distributed behavioral model, Comput. Graph21, 25(1987).
    40. Gazi V. and Passino K. M., Stability analysis of swarms, IEEE Trans. Automatic Control48, 692(2003). Liljeros F et al. Nature411,907(2001).
    41. Vicsek T., Czirok A., Ben-Jacob E., Cohen I. and Shochet O., Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett.75,1226(1995).
    42. Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules, IEEE Trans. Automatic Control48,988(2003).
    43. Couzin I. D., Krause J., James R., Ruxton G.D., and Franks N.R., Collective memory and spatial sorting in animal groups, J. Theor. Biol.218,1(2002).
    44. Olfati-Saber R. and Murray R., Consensus problems in networks of agents with switching topology and time-delays, IEEE Transactions on Automatic Control, vol.49,1520(2004).
    45. Couzin I. D., Krause J., Franks N. R. and Levin S. A., Effective leadership and decision-making in animal groups on the move, Nature433,513 (2005)
    46. Bellerini M., et al., Interaction ruling animal collective behavior depends on topological rather than metric distance:Evidence from a field study, Proc. Natl. Acad. Sci. USA105, 1232(2009).
    47.刘志新、郭雷,Vicsek模型的连通与同步,中国科学-E辑37,979(2007).
    48. Tian B M. et al., Optimal view angle in collective dynamics of self-propelled agents, Phys. Rev. E 79,052102(2009).
    49.汪小帆、李翔、陈关荣,复杂网络—流量与应用,北京:清华大学出版社,(2006).
    50. Li X, Wang X.F. and Chen G, Pinning a complex dynamical network to its equilibrium, IEEE Transactions on Circuits and Systems 151,2074(2004).
    51. Chen T., Liu X., and Lu W, Pinning complex network by a single controller, IEEE Trans. Circuits Syst. I; Regul. Papers 54,1317(2007).
    52. Duan Z.. Wang J. Chen G. and Huang L., Stability analysis and decentralized control of a class of complex dynamical networks, Automatica44,1028(2008).
    53. Xiao F. and Wang L., Consensus protocols for discrete-time multi-agent systems with time-varying delays, Automatica vol 44,2577(2008).
    54.史济民计算机网络公共基础,清华大学出版社,(2002).
    55. Broido, A., and Claffy, K. Internet topology:connectivity of IP graphs, in Proceedings of SPIE ITCom(2001).
    56. Oregon route-views project, http://www.antc.uoregon.edu/route-views/
    57. Qian C., Chang H., Govindan R., Jamin S., Shenker S., and Willinger W., in Proceedings of IEEE INFOCOM,. New York, (2002).
    58. W. Willinger, M.S. Taqqu and A. Erramilli, A bibliographical guide to self-similar traffic and performance modeling for modern high-speed networks,339--366, Clarendon Press, Oxford(1996).
    59. http://www.caida.org
    60. Bela Bollobas and Oliver Riordan. Source:Internet Math.1,1(2003).
    61. Cohen R. and Havlin S., Probabilistic Prediction in Scale-Free Networks:Diameter Changes. Phys. Rev. Lett.90,058701. (2003)
    62. Wang W.-X., Wang B.-H, Hu B., Yan G., Ou Q., General Dynamics of Topology and Traffic on Weighted Technological Networks. Phys.Rev. Lett.94,188702 (2005)
    63. Singh B. K., and Gupte N., Congestion and decongestion in a communication network. Phys. Rev. E 71,055103(2005).
    64. Kinzer J. P., Application of the theory of probability to problem of highway traffic, B. C. E thesis, Politech. Inst. Brooklyn (1933).
    65. Greenshields B. D., Shapiro D. and Ericksen E. L., Traffic performance at urban street intersections, Tech. Rep. No.l, Yale Bureau of Highway traffic, New Haven Corn (1947).
    66. Lighthill M. J., Whitham G. B., On kinematic waves:II. a theory of traffic flow on long crowded roads, Proceedings of the Royal Society, Ser. A 229317 (1955).
    67. Richards P. I., Shock waves on the highway, Oper. Res.442 (1956).
    68. Payne H. J. Models of freeway traffic and control, In:Bekey, G.A. (Ed.), Mathematical Models of Public Systems 1, Simulation Council, La Jolla,51(1971).
    69. Payne H. J., Freflo:a macroscopic simulation model for freeway traffic. Transpn. Res. Rec. 72268(1979).
    70. Aw A., Rascle M., Resurrection of "second order" models of traffic flow, SIAM Journal on Appl. Math.60,916(2000).
    71. Jiang R., Wu Q. S., Zhu Z. J., A new continuum model for traffic flow and numerical tests, Transp. Res. B 36,405 (2002).
    72. Siebel F., Mauser W., Synchronized flow and wide moving jams from balanced vehicular traffic, Phys. Rev. E 73,066108 (2006).
    73. Prigogine I., Herman R., Kinetic theory of vehicular traffic, American Elsevier, New York (1971).
    74. Paveri-Fontana S. L., On Boltzmann-like treatments for traffic flow. A critical review of the basic model and an alternative proposal for dilute traffic analysis. Tranpn. Res.9,225(1975).
    75. Helbing D. et al., MASTER:macroscopic traffic simulation based on a gas-kinetic, non-local traffic model, Transp. Res. B 351,83(2001).
    76. Reuschel A., Vehicle movements in a platoon. Oesterreichisches Ingenieur-Archir 4, 193(1950).
    77. Pipes L. A., An operational analysis of traffic dynamics. J. Appl. Phys.24,274 (1953).
    78. Newell G. F., Nonlinear effects in the dynamics of car following, Oper. Res.9,209(1961).
    79. Bando M. et al., Dynamical model of traffic congestion and numerical simulation, Phys. Rev. E51,1035 (1995).
    80. Sasoh A., Impact of unsteady disturbance on multi-lane traffic flow, J. Phys. Soc. Japn.71, 989(2002).
    81. Neumann V., Cellular automata, Academic Press, New York (1968).
    82. Cremer M., Ludwig J., A fast simulation model for traffic flow on the basis of Boolean operations, J. Math. Comp. Simul.28,297(1986).
    83. Biham O., MiddletonA. A., Levine D. A., Self-organization and a dynamical transition in traffic flow models, Phys. Rev. A 46, R6124(1992).
    84. Raissa M. D'Souza, Coexisting phases and lattice dependence of a cellular automaton model for traffic flow, Phys. Rev. E 71,066112(2005).
    85. M. Barthelemy, A. Flammini, Modeling urban street patterns, Phys. Rev. Lett.100,138702 (2008).
    86. Nowak M A., Five rules for the evolution of cooperation, Science314,1560(2006).
    87. Vukov J and Szabo G, Evolutionary prisoner's dilemma game on hierarchical lattices, Phys. Rev. E 71,036133(2005).
    88. Galam S., Minority opinion spreading in random geometry, Eur. Phys. J. B 25,403 (2002).
    89. Sznajd-Weron K. and Sznajd J., Opinion evolution in closed community, Int. J. Mod. Phys. C 11,1157(2000).
    90. Czirok A., Ben-Jacob E., Cohen I. and Vicsek T., Formation of complex bacterial colonies via self-generated vortices, Phys. Rev. E.54,1791(1996).
    91. Li Y.J., Wang S., Han Z.L., Xi Z.D. and Wang B.H., Optimal view angle in the self-propelled particle models in three dimensions, (2011), Eur. Phys. Lett.93 68003 (2011).
    92. Peng L.Q., Zhao Y., Tian B.M., Zhang J., Wang B.H., Zhang H.T. and Zhou T., Consensus of self-driven agents with avoidance of conclusions, Phy. Rev. E.79,026113 (2009).
    93. Monetti R., Rozenfeld A. and Albano E., Study of Interacting Particle Systems:The Transition to the Oscillatory Behavior of a Prey—Predator Model, Physica A 283,52(2000).
    94. Renlds C.W., Flocks, herds and schools:A distributed behavior model, Comput.Graph.21, 25(1987).
    95. Sumpter D.J.T., The priiciples of collective animal behavior, Phil.Trans. R. Soc.B,361, 5(2006).
    96. Anders E., Martin N.J., Johan N. and Kolbjorn T., Determining interaction rules in animal swarms, Behav. Ecol.21,1106 (2010).
    97. Gazi V. and Passino K.M., Stability analysis of social foraging swarms:Combined effects ofattractant/repellent profiles,American Control Conf.Anchorage,Alaska, May (2002).
    98. Bescco C, Vandewalle N., Delcourt J. and Poncin P., Experimental evidences of a structural and dynamical transition in fish school, Physica A,367,487 (2006).
    99. Nagy M., Akos Z., Biro D. and Vicsek T., Hierarchical group dynamics in pigeon flocks, Nature,464,890(2010).
    100. Jihad R. T., Amer Sh. and Leonid I.K., Self-organization in two-dimentional swarms, Phys. Rev. E.81,066106(2010).
    101. Jadbabaie A., Lin J. and Morse S., Coordination of groups of mobile autonomous agents using nearest neighbour rules, IEEE Trans, on Autom. Control 48,988 (2003).
    102. Czirok A., Stanley H.E. and Vicsek T., Spontaneously ordered motion of self-propelled particles, Journal of Physics A-Mathematical and General 30,1375 (1997).
    103. Zhang H.T., Chen M.Z.Q. and Zhou T., Predictive protocol of flocks with small-world connection pattern, Physical Review E,79,016113 (2009).
    104. Zhang J., Zhao Y., Tian B.M., Peng L.Q. et al. Accelerating consensus of self-driven swarm via adaptive speed, Physica A,388,1228 (2009).
    105. Li W. and Wang X.F., Adaptive velocity strategy for swarm aggregation, Phys. Rev. E75, 021917(2007).
    106. Pitcher T.J. and Parrish J.K., Functions JK of shoaling behavior in teleosts, In Behaviour of Teleost Fishes, TJ Pitcher, ed., pp 363-439, Chapman and Hall, London, UK (1993).
    107. Krause J. and Ruxton G.D., Living in Groups, Oxford University Press, Oxford (2002).
    108.http://www.telegraph.co.uk/news/picturegalleries/picturesoftheday/8428982/Pictures-of-the-day-5-April-2011.html
    109. Goss S., Aron S., Deneubourg J.L. and Pasteels J.M., Self-organized shortcuts in the Argentine ant, Naturwiss.76,579 (1989).
    110. Andrea C., Alessio C., Irene G., Giorgio P., S. Raffaele, Fabio S. and Massimiliano V., Scale-free correlations in starling flocks, PNAS,107(26),11865(2010).
    111. Ballerini M., Cabibbo N., Candelier R., Cavagna A., Cisbani E., Giardina I., Lecomte V., Orlandi A., Parisi G., A. Procaccini, M. Viale, and V. Zdravkovic, Interaction ruling animal collective behavior depends on topological rather than metric distance:evidence from a field study, Proc. Natl. Acad. Sci. U.S.A.105,1232(2008).
    112. Krause J., Differential fitness returns in relation to spatial position in groups, Biol. Rev.69. 187(1994).
    113. Hammer W.M. and Parrish J.K., Is the sum of the parts equal to the whole:the conflict between individuality and group membership, In Animal Groups in Three Dimensions, J Parrish and WM Hammer, eds., pp 163-173, Cambridge University Press, Cambridge, UK (1997).
    114. LU J.H., Liu J., Couzin I.D. and Levin S.A., Emerging collective behaviors of animal groups, Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China (June 25-27,2008).
    115.Witten T. A., and Sander L.M., Phys. Rev. Lett.47 1400(1981), Phys. Rev. B 275686(1983).
    116. Meakin P., Phys. Rev. A 27,604,1945(1983).
    117. Meakin P., Phys. Rev. A 27,2316(1983).
    118. Deutch J.M. And Meakin P., J. Chem. Phys.78,2093(1983).
    119. Tokyama M. and Kawasaki K., Phys. Letts. A 100,337(1984).
    120. Mandelbrot B., Kol B. and Ahaony A., Angular gaps in radial diffusion limited aggregation: two fractal dimensions and non-transient deviations from linear self-similarity, Phys.Rev. Lett. 88,055501(2002).
    121. Barabasi A. L. and Stanley H. E., Fractal Concepts in Surface Growth (Cambridge University Press,1995).
    122. Landau D. P. And Binder K., Nature Monte Carlo Simulations in Statistical Physics (Cambridge University Press,2005).
    123. Tolman S. and Meakin P., Phys. Rev. A 40,428(1989), and references therein.
    124. Eden M., as cited in Ref.115 and in Vannimenus J., Nikel B., and Hakim V., Phys. Rev. B 30,391(1984), and in Peters H. P., Stauffer D., Holters, andLoewenich K., Z. Phys. B 34,399(1979).
    125. Parisi G. and Zhang Y. C., Eden Model in Many Dimensions, Phys. Rev. Lett.53,1791(1984).
    126. Barabasi A. L., The origin of bursts and heavy tails in humans dynamics. Nature 435, 207(2005).
    127. C-G Gu, T. Zhou, Y-Q Qu, Y-M Jiang and D-R He, Continuous phase transition at the onset of cooperation between layered networks, Preprint.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700