基于复杂适应系统理论的经济仿真研究
|
摘要
经济学作为一门单独的科学自从出现以来,已走过了数百年的发展道路,逐步形成了以概念、假设、数据以及数学模型为基础的比较完整的理论体系,并在经济学研究中占据了主流地位。然而,随着知识经济时代的到来,传统经济学遇到了空前的挑战。首先,由于现实经济中涉及的因素众多,相互关系复杂,给数学方法的运用带来了一定困难;其次,传统方法没有很好地解决经济系统的层次性结构问题,因此,无法对不同层次的特性差异给予一个整体性的解释,也就产生了宏观、微观相互脱节的理论体系上的缺陷;再次,传统经济理论对现实经济普遍存在的信息不完备与不确定性尚不能给予系统的解释;最后,传统经济中以观测数据为基础的经验主义的研究方法,无法深入了解经济的微观运行过程,从而降低了理论对现实的解释与预测能力。
从上世纪八十年代开始,世界各国的许多科学家对经济学作为一个演化的复杂系统展开了大量的研究工作,并在理论与方法上取得了一系列令人瞩目的研究成果。其中,基于复杂适应系统(CAS)理论的多主体经济仿真是经济系统复杂性研究中的一个重要分支。上世纪九十年代中期,美国Sandia国家实验室开发了基于CAS理论的经济模型ASPEN,该模型从仿真经济中微观个体的属性与行为出发,通过建立微观个体间的相互联系,间接地构造了美国经济的宏观构架,从而较好地解决了宏观与微观的联系问题。在对该模型的运行实验中,较好地再现了美国经济的波动现象,所进行的货币政策实验也取得了与经济理论相一致的结果。总之,该模型具有传统经济计量模型与投入产出模型所不具备的优势。
以ASPEN为代表的基于CAS理论的多主体经济仿真对我国的经济学研究同样具有重要的理论意义与应用价值。但由于美国未公布该模型的技术细节,国内一些单位,如中国人民大学信息学院、吉林大学数量经济研究中心等都针对该模型进行了大量的研究、开发工作,并取得了一定成果。
针对国内缺少ASPEN所需的大规模并行计算机的问题,作者开发了能够在微机局域网环境下使主体仿真程序并行工作的分布式多主体仿真平台DABS,并在该环境下成功地仿制了美国ASPEN模型。从目前所获得的国内研究资料分析,该模型在主体类型、主体数量、主体学习算法、主体问的相互联系以及模型的运行结果等方面,更加接近ASPEN原模型,在国内具有领先水平。在成功研制仿ASPEN模型的基础上,作者又开发了中国转型经济模型CASPEN。该模型重点仿真了计划经济与市场经济两种体制并存的中国转型期特有的经济特点,并对该市场环境下财政政策与货币政策的效果进行了对比实验。根据对实验结果的分析,提出了完善我国社会主义市场经济的一些政策建议。
本论文在复杂适应系统理论的框架下运用多主体仿真技术研究现实经济问题,其意义在于:首先,本课题是对经济学研究方法的一种探索。通过多主体仿真研究经济问题与传统经济学研究中广泛采用的演绎、归纳方法有所不同,它以复杂适应系统理论为依据,以多主体建模及模型实验为手段,通过对实验结果的分析,揭示经济系统的内在规律。这种方法与2002年诺贝尔经济学奖获得者Vernon Smith的实验经济学方法有共同之处。其次,在经济全球化与信息经济的时代背景下,传统经济学从理论到方法都遭遇到了空前的挑战。由于我国目前处于经济转型阶段,机制与环境随时都在发生着较大变化,传统经济学方法运用时会
As a branch of science, economics has gone through hundreds years and formed an overall theoretic structure on the basis of conceptions, hypotheses, data, and mathematic models. Therefore it has been the mainstream in the fields of economics researches. However, the traditional economics is confronted with a tremendous challenge with the advent of the knowledge economics era. Firstly, many factors involved in economic practices and their complex relationships make difficult to the application of mathematic methods. Secondly, there are no reasonable solutions with the traditional methods to hierarchical structure problems of the economic system, and so all-around interpretation cannot be given to the characteristic difference between hierarchies, as a result, disconnections between macro theoretic structures and micro theoretic structures come into being. Thirdly, incompleteness and uncertainty of information are ubiquitous in the world, and they cannot be explained systematically by traditional economical theories. Lastly, microcosmic process of economy cannot be found out thoroughly with experimentalism methods of traditional economics based on observational data, thereby capabilities of explaining and forecasting reality with theories have been reduced.Since 1980's, scientists in many countries have developed lots of researches on considering economics an evolutional complex system, and have born abundant fruits. Hereinto, the study of Multi-Agent simulation of economies based on the complex adaptive system theory is an important research branch of the complexity of economy. In the middle of 1990's, an economicsmodel based on the complex adaptive system theory-ASPEN was developed by SandiaNational laboratory in America. The model built the macro structure of American economy though simulating properties and actions of micro agents and simulating interaction of these agents. Accordingly, the relationship between macro and micro is solved preferably. The fluctuating phenomena of American economy can be imitated properly by experiments with the model. Anyway, the model has many advantages, which are absent in econometric models and input-output models of traditional economics.The Technique of Multi-Agent economic simulation based on the CAS theory is equally of significant theoretic implications and application value in China's economic studies. Because the detailed technique of ASPEN was not released publicly, some universities and institutes, such as the Information School of Renmin University of China, the Research Center of Quantitative Economics of Jilin University, have done lots of work involving the study of such model. Quite some progress has been made.Because of lack of parallel-computer needed by ASPEN on a large scale in our country, a distributed platform, named DABS, for Multi-Agent simulation was developed with microcomputer under the circumstance of local area network by the author. Based on this platform, an imitation of ASPEN was implemented, and the simulating results are much closer to the prototype of ASPEN considering agent types, quantities, learning algorithms and relationships among agents.
Furthermore, a model of China's transitional economy named CASPEN is developed successfully. Special characteristics of the transitional economy, with the planned economy and market economy concurrently existed, are simulated with the model, and then through comparing finance policy with monetary policy of market circumstance by experiments. By analyzing the experimental results, some suggestions about policy are presented to improve socialistic market economy of China.In this paper, the significance of research on some practical economic problems with Multi-Agent simulation based on the complex adaptive system theory is presented. First of all, it is an exploring of the methodology of economic studies. That is to say, study of Multi-Agent simulation of economies based on the complex adaptive system theory can uncover some intrinsic rules in economic system by analyzing experimental results. It displays the co
从上世纪八十年代开始,世界各国的许多科学家对经济学作为一个演化的复杂系统展开了大量的研究工作,并在理论与方法上取得了一系列令人瞩目的研究成果。其中,基于复杂适应系统(CAS)理论的多主体经济仿真是经济系统复杂性研究中的一个重要分支。上世纪九十年代中期,美国Sandia国家实验室开发了基于CAS理论的经济模型ASPEN,该模型从仿真经济中微观个体的属性与行为出发,通过建立微观个体间的相互联系,间接地构造了美国经济的宏观构架,从而较好地解决了宏观与微观的联系问题。在对该模型的运行实验中,较好地再现了美国经济的波动现象,所进行的货币政策实验也取得了与经济理论相一致的结果。总之,该模型具有传统经济计量模型与投入产出模型所不具备的优势。
以ASPEN为代表的基于CAS理论的多主体经济仿真对我国的经济学研究同样具有重要的理论意义与应用价值。但由于美国未公布该模型的技术细节,国内一些单位,如中国人民大学信息学院、吉林大学数量经济研究中心等都针对该模型进行了大量的研究、开发工作,并取得了一定成果。
针对国内缺少ASPEN所需的大规模并行计算机的问题,作者开发了能够在微机局域网环境下使主体仿真程序并行工作的分布式多主体仿真平台DABS,并在该环境下成功地仿制了美国ASPEN模型。从目前所获得的国内研究资料分析,该模型在主体类型、主体数量、主体学习算法、主体问的相互联系以及模型的运行结果等方面,更加接近ASPEN原模型,在国内具有领先水平。在成功研制仿ASPEN模型的基础上,作者又开发了中国转型经济模型CASPEN。该模型重点仿真了计划经济与市场经济两种体制并存的中国转型期特有的经济特点,并对该市场环境下财政政策与货币政策的效果进行了对比实验。根据对实验结果的分析,提出了完善我国社会主义市场经济的一些政策建议。
本论文在复杂适应系统理论的框架下运用多主体仿真技术研究现实经济问题,其意义在于:首先,本课题是对经济学研究方法的一种探索。通过多主体仿真研究经济问题与传统经济学研究中广泛采用的演绎、归纳方法有所不同,它以复杂适应系统理论为依据,以多主体建模及模型实验为手段,通过对实验结果的分析,揭示经济系统的内在规律。这种方法与2002年诺贝尔经济学奖获得者Vernon Smith的实验经济学方法有共同之处。其次,在经济全球化与信息经济的时代背景下,传统经济学从理论到方法都遭遇到了空前的挑战。由于我国目前处于经济转型阶段,机制与环境随时都在发生着较大变化,传统经济学方法运用时会
As a branch of science, economics has gone through hundreds years and formed an overall theoretic structure on the basis of conceptions, hypotheses, data, and mathematic models. Therefore it has been the mainstream in the fields of economics researches. However, the traditional economics is confronted with a tremendous challenge with the advent of the knowledge economics era. Firstly, many factors involved in economic practices and their complex relationships make difficult to the application of mathematic methods. Secondly, there are no reasonable solutions with the traditional methods to hierarchical structure problems of the economic system, and so all-around interpretation cannot be given to the characteristic difference between hierarchies, as a result, disconnections between macro theoretic structures and micro theoretic structures come into being. Thirdly, incompleteness and uncertainty of information are ubiquitous in the world, and they cannot be explained systematically by traditional economical theories. Lastly, microcosmic process of economy cannot be found out thoroughly with experimentalism methods of traditional economics based on observational data, thereby capabilities of explaining and forecasting reality with theories have been reduced.Since 1980's, scientists in many countries have developed lots of researches on considering economics an evolutional complex system, and have born abundant fruits. Hereinto, the study of Multi-Agent simulation of economies based on the complex adaptive system theory is an important research branch of the complexity of economy. In the middle of 1990's, an economicsmodel based on the complex adaptive system theory-ASPEN was developed by SandiaNational laboratory in America. The model built the macro structure of American economy though simulating properties and actions of micro agents and simulating interaction of these agents. Accordingly, the relationship between macro and micro is solved preferably. The fluctuating phenomena of American economy can be imitated properly by experiments with the model. Anyway, the model has many advantages, which are absent in econometric models and input-output models of traditional economics.The Technique of Multi-Agent economic simulation based on the CAS theory is equally of significant theoretic implications and application value in China's economic studies. Because the detailed technique of ASPEN was not released publicly, some universities and institutes, such as the Information School of Renmin University of China, the Research Center of Quantitative Economics of Jilin University, have done lots of work involving the study of such model. Quite some progress has been made.Because of lack of parallel-computer needed by ASPEN on a large scale in our country, a distributed platform, named DABS, for Multi-Agent simulation was developed with microcomputer under the circumstance of local area network by the author. Based on this platform, an imitation of ASPEN was implemented, and the simulating results are much closer to the prototype of ASPEN considering agent types, quantities, learning algorithms and relationships among agents.
Furthermore, a model of China's transitional economy named CASPEN is developed successfully. Special characteristics of the transitional economy, with the planned economy and market economy concurrently existed, are simulated with the model, and then through comparing finance policy with monetary policy of market circumstance by experiments. By analyzing the experimental results, some suggestions about policy are presented to improve socialistic market economy of China.In this paper, the significance of research on some practical economic problems with Multi-Agent simulation based on the complex adaptive system theory is presented. First of all, it is an exploring of the methodology of economic studies. That is to say, study of Multi-Agent simulation of economies based on the complex adaptive system theory can uncover some intrinsic rules in economic system by analyzing experimental results. It displays the co
引文
[1]许国志,顾基发,车宏安《系统科学》,上海科技教育出版社,2000.9
[2]许国志,顾基发,车宏安《系统科学与工程研究》,上海科技教育出版社,2000.10
[3]成思危,《复杂科学与管理》,南昌大学学报,2000.3
[4]方福康,《复杂经济系统的演化分析》,http://www.systemscience.org/zjjs/ffktxt1.htm
[5]方福康,《经济和生态环境的复杂性研究》,http://www.biox.cn/content/bioxall/200412173172.htm
[6]张世伟,《基于主体的宏观经济微观模拟模型》,财经科学,2004.1
[7]吴军,《紧缩与扩张》,清华大学出版社,2001.10
[8]成思危,《发展计划的制定与管理》,经济科学出版社,2004.3
[9]解三明,《中国经济增长潜力和经济周期研究》,中国计划出版社,2001.10
[10]汪红驹,《中国货币政策有效性研究》,中国人民大学出版社,2003,5
[11]武献华 庞明川,《中国经济转轨中的失业、通货膨胀与经济增长》,东北财经大学出版社,2003.4
[12]宋学锋,杨列勋,曹庆仁《复杂性科学研究进展》,科学出版社,2004.2
[13]高鸿桢,《实验经济学导论》,中国统计出版社,2003.4
[14]奧斯卡.兰格,《经济控制论导论》,中国社会科学出版社,1981
[15]曼内斯库,《经济控制论》,新时代出版社,1985
[16]波拉特,《信息经济》,中国展望出版社,1987
[17]理查德 H 戴,《混沌经济学》,上海译文出版社,1996
[18]贝纳西,《市场非均衡经济学》,上海译文出版社,1989
[19]沈华嵩,《经济系统的自组织理论》,中国社会科学出版社,1991
[20]陈禹,《CAS理论的发展与应用》,首都经济贸易大学多主体仿真理论与应用研讨会主题报告,2005.1
[21]钱小安,《中国货币政策的形成与发展》,上海人民出版社,2000
[22]贝塔朗菲,《一般系统论——基础,发展,应用》,北京,社会科学文献出版社,1987
[23]邹珊刚等,《系统科学》,上海人民出版社,1987
[24]夏绍玮,杨家本,杨振斌,《系统工程概论》,清华大学出版社,1995
[25]弗里德里希,克拉默,《混沌与秩序——生物系统的复杂结构》,上海科技教育出版社,2000
[26]刘洪,《经济系统预测的混沌理论原理与方法》,科学出版社,2003
[27]伍海华,李道叶,翟锋,《资本市场复杂性》,经济科学出版社,2003
[28]熊光楞,肖田元,张燕云,《连续系统仿真与离散事件系统仿真》,清华大学出版社,1991
[29]王红卫,《建模与仿真》,科学出版社,2002
[30]伍尔德里奇,《多Agent系统引论》,电子工业出版社,2003
[31] Leigh Testatsion, 2002, "Agent-Based Computational Economics: Growing Economies from the Bottom Up", Artificial Life, Vol. 8 No. 1,55-82
[32] Gintis, 2000, "Game theory evolving, Princeton", NJ: The Princeton University Press
[33] Dawid, 1999, "Adaptive learning by genetic algorithms: Analytical result and applications to economic models", Revised Second Edition, Berlin: Springer-Verlag
[34] Vriend, 2000, "An illustration of the essential difference between individual and social learning and its consequences for computational analyses", Journal of Economic Dynamics and Control 24,1-19
[35] Marks, 1998, "Evolved perception and behaviour in oligopolies", Journal of Economic Dynamics and Control 22, 1209-1233
[36] Axelrod, 1997, "The complexity of cooperation: Agent-based model of conflict and cooperation, Princeton", N.J.: The Princeton University Press
[37] Schelling, 1978, "Micromotives and macrobehavior", New York, NY: W. W. Norton Company
[38] Epstein and Axtell, 1996, "Growing artificial societies: Social science from the bootom up", Cambridge, MA: MIT Press
[39] Holland, 1995, "Hidden order: How adaptation builds complexity", Reading, MA: Addison-Wesley
[40] Riolo, 1997, "The effects and evolution of tag-mediated selection of partners in populations playing the interated prisoner's dilemma" In Thomas Baeck, Proceedings of the International Conference on Genetic Algorithms, San Francisco, CA: Morgan Kauffman
[41] Arifovic and Eaton, 1995, "Coordination via genetic learning", Computational Economics 8, 181-203
[42] Epstein, 2001, "Learning to be thoughtless: Social norms and individual computation", Computational Economics 18,9-24
[43] Tay and Linn, 2001, "Fuzzy inductive reasoning, expectation formation, and the behavior of security", Journal of Economic Dynamics and Control 25, 321-361
[44] LeBaron, 2001, "Empirical Regularities from interacting long and short horizon investors in an agent-based stock market", IEEE transactions on Evolutionary Computation 5, 442-455
[45] Izumi and Ueda, 2001, "Phase transition in a foreign exchange market: Analysis based on an artificial market approach", IEEE Transactions on Evolutionary Computation 5, 456-470
[46] Chen and Yeh, 2001, "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market", Journal of Economic Dynamics and Contral 25, 363-393
[47] Howitt and Clower, 2000, "The emergence of economic organization", Journal of Economic Behavior and Organization 41,55-84
[48] Bower and Bunn, 2001, "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and wales electricity market", Journal of Economic Dynamics and Control 25, 561-592
[49] Bunn and Oliveira, 2001,"Agent-based simulation: An application to the New Electricity Trading Arrangements of England and Wales", IEEE Transections on Evolutionary Computation 5,493-503
[50] Williamson and Masten, 1999,"The Economics of transaction costs, Northhampton", MA: Edward Elgar Publishing
[51] Albin and Foley, 1992, "Decentralized, dispersed exchange without an auctioneer: A simulation study", Journal of Economic Behavior and Organization 18, 27-51
[52] Kirman, 1997, "The economy as an interactive system", Proceedings Vol. XXVII, SFI Studies in the Sciences of Complexity, Reading, MA: Addison-Wesley
[53] Tesfatsion, 1997, "How Economists can get alife", Proceedings Vol. XXVII,SFI Studies in the Sciences of Complexity. Reading, MA: Addison-Wesley
[54] Tesfatsion, 1997, "A trade network game with endogenous partner selection", Dordrecht, The Netherlands: Kluwer Academic Publishers
[55] Vriend, 1995, "Self-organization of market: An example of a computational approach", Computational Economics 8
[56] Tesfatsion, 1998, "Preferential partner selection in evolutionary labor markets: A study in agent-based computational
economics", Proceedings of the Seventh Annual Conference on Evolutionary Programing. Burlin: Springer-Verlag
[57] Tesfatsion, 2001, "Structure, behavior, and market power in an evolutionary labor market with adaptive search", Journal of Economic Dynamics and Control 25, 419-457
[58] Tassier and Menczer, 2001, "Emerging small-world referral network in evolutionary labor markets", IEEE Transactions on Evolutionary Computation 5, 482-492
[59] Kirman and Vriend, 2001, "Evolving market structure: An ACE model of price dispersion and loyalty", Journal of Economic Dynamics and Control 25,459-502
[60] Holland, 1992, "Adaptation in natural and artificial system", Cambridge, MA: The MIT Press
[61] Rouchier, 2001, "A multiagent model for describing transhumance in North Cameroon", Journal of Economic Dynamics and Control 25, 527-559
[62] De Vany and Lee, 2001, "Quality signals in information cascades and the dedtribution of motion picture box office revenues", Jiurnal of Economic Dynamics and Control 25, 593-614
[63] Prietula, Carley and Gasser, 1998, "Simulating organizations: Computational models of institutions and groups", Cambridge, MA: The MIT Press
[64] Dawid, Reimann and Bullnheimer, 2001, "To innovate or not to innovate?, IEEE Transactions on Evolutionary Computation 5,471-481
[65] Miller and Andreoni, 1990, "A coevolutionary model of free riding behavior: Replicator dynamics as an explanation of the experimental results", Santa Fe Institute Working Paper
[66] Andreoni and Miller, 1995, "Auctions with adaptive artificial agents", Games and Economic Behavior 10, 39-64
[67] Arifovic, 1994, "Genetic algorithm learning and the cobweb model", Journal of Economic Dynamics and Control 18,3-28
[68] Arifovic, 1996, "The Behavior of the exchange rate in the genetic algorithm and experimental economics", Journal of Political Economy 104, 510-541
[69] Aathur, 1991, "On designing economic agents that behave like human agents: A behavioral approach to bounded rationality", American Economic Review 81, 353-359
[70] Chan, LeBaron and Poggio, 1999,"Agent-based models of financial markets: A comparison with experimental markets", MIT Artificial Markets Project, Paper No. 124
[71] Marimon, McGrattan and Sargent, 1990, "Money as a medium of exchange in an economy with artificially intelligent agents", Journal of Economic Dynamics and Control 14,329-373
[72] Duffy, 2001, "Learning to speculate: Experiments with artificial and real agents", Journal of Economic Dynamics and Control 25, 295-319
[73] Pingle and Tesfatsion, 2001, "Non-employment benefits and the evolution of worker-employer cooperation: Experiments with real and computational agents", Economic Report 55, Iowa State University
[74] Basu, Pryor, Quint and Arnold "ASPEN: A Microsimulation Model of the Economy", Sandia Report 1996
[75] Dianne C. Barton, "ASPEN-EE: An Agnet-Based Model of Infrastructure Interdependency", Sandia Report 2000
[76] Dianne C. Barton, "Simulation Economic Effects of Disfuptions in the Telecommunications",Sandia Report 2004
[77] David A. Schoenwand, Dianne C Barton, Mark A. Ehlen, "An Agent-based Simulation Laboratory for Economics and Infrastructure Interdependency", Sandia Report 2004
[78] Arthur, Holland, LeBaron, Palmer and Tayler, 1996, "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market", Working paper of Santa Fe Institute
[79] LeBaron, "Building the Santa Fe Artificial Stock Market", Working paper of Santa Fe Institute, 2002
[80] Johnson, "What I learned from the Artificial Stock Market", Working paper of Santa Fe Institute, 2001
[81] Ehrentreich, 2002,"A Corrected Version of the Santa Fe Institute Artificial Stock Market Model", Martin-Luther-Universitat Halle-Wittenberg
[82] Pingle and Tesfatsion, 2003,"l Evolution of Worker-Employer Nerworks and Behaviors Under Alternative Non-Employment Benefits: An Agent-Based Computational Study"
[83] Ormerod, Rosewell and Smith, 2001, "An Agent-based Model of the Evolution of Market Structure and Competition", Volterra Consulting Ltd.
[84] Impullitti and RebMann, "An Agent-based Model of Wealth Distribution", CEPA Working Paper 2002
[85] Bigus, Schlosnagle, Pilgrim, Mills and Diao, "ABLE: A toolkit for building nultiagent autonomic systems", http://www.research.ibm.com/journal/sj/413/bigus.html
[86] Miles Parker, "What is Ascape and Why Should you Care?", Journal of Artificial Societies and Social Simulation, Volume 4 No. 1, January 2001
[87] "D-OMAR: Distributed Operator Model Architecture", http://omar.bbn.com/
[88] "Gambit: Software Tools for Game Theory", http://econweb.tamu.edu/gambit/
[89] "JASA - Java Auction Simulator API", http://www.csc.liv.ac.uk/~sphelps/jasa/
[90] "The MadKit Project (a Mult-Agent Development Kit)", http://www.madkit.org/
[91] Corliss, Gulyas, Kozsik and Fazekas, "Model Design Interface - A CASE Tool for the Multi-Agent Modeling", SwarmFest 99
[92] D.J. Higham and N.J. Higham, "MATLAB Guide (Second Edition)", SIAM 2005
[93] Murat Yildizoglu's, "Evolutionary Modelling Tools", http://beagle.u-bordeaux4.fr/yildi/ evolmod.html
[94] David McFadzean, 1994, "SimBioSys: A Class Framework for Biological Simulations", http://www.lucifer.com/~david/thesis/
[95] Margarita and Sonnessa,"Sim2Web: An Open Source System for Web-Enabling Economic and Financial Simulations", http://wf.econ.unito.it/sim2web
[96] Jerferson, "Virtual time", ACM Transactions on Programming Languages and Systems 7,3(July 1985), P.405-425
[97] Jerferson, "Virtual time II: The Cancelback protocol for storage management in distributed simulation", Proceedings of the 9th Annual ACM Symposium on Principles of Distributed Computation (Aug. 1990), P.75-90
[98] James A. Sprigg, Jr., Craig R. Jorgensen, Richard J. Pryor, "Approach and Development Strategy for an Agent-Based Model of Economic Confidence", Sandia Report 2004
[99] James A. Sprigg, Jr., Mark A. Ehlen, "Full Employment and Competition in the ASPEN Economic Model: Implications for Modeling Acts of Terrorism", Sandia Report 2004
[100] James A. Sprigg, "Market Disruption, Cascading Effects and Economic Recovery - A Life-Cycle Hypothesis Model", Sandia Report 2004
[101] Leigh Tesfatsion, "Agent-Based Computational Economics", ISU Economics Working Paper No 1, 2003.8
[102] Marcus Daniels, "Integrating Simulation Technologies with Swarm", SDG Report
[103] John Foster, "Why is Economics not a Complex Systems Science?", Discussion Paper No. 336,School of Economics, The University of Queensland
[104] Casti, "Complexification", Harker Collins 1994
[105] Casti et al., "Boundaries and Barriers -on the limit to Scientific Knowledge", Addison-Wesley, 1996
[106] Bossomaier and Green "Pattern in the Sand, Computer, Complexity and Life", Allen & Unwin, 1998
[107] Bergmann and Bennett, "A Microsimulated Transactions Model of the United StatesEconomy", Baltimore: The Johns Hopkins University Press, 1986
[108] Stefansson, 1997,"Swarm: An Object Oriented Simulation Platform Applied to Markets and Organizations", Evolutionary Programming Ⅵ, New York: Springer-Verlag
[109] Page, 1999, "Computational Models from A to Z"
[110] Arthur, Durlauf, Lane, 1997, "The Economy as an Evolving Complex System", Addison-Wesley, Reading, Mass, 1997
[111] Basu and Pryor, 1997, "Growing a Market Economy", Sandia report. SAND-97-2093
[112] Minar, Burkhart, Lanton and Askenazi, 1996, "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations", SFI working paper, http://www.santafe.edu/projects/swarm
[113] Alvin and Foley, 1992, "Decentralized, Dispersed Exchange without an Auctioneer", Journal of Economic Behavior and Organization, 18, 27-51
[114] Axtell, Axelrod, Epstein and Cohen, 1996, "Aligning simulation models: a case study and results", Computational and Mathematical Organization Theory, 1, 123-141
[115] Cyert and March, 1963, "A behavioral theory of the firm", Englewood Cliffs, N. J., Prentice-Hall, 1963
[116] Epstein and Axtell, 1996, "Growing artificial societies: social science from the bottom up", Washington, DC: Brookings and Cambridge, MA: MIT Press
[117] Kauffman, Macready and Dickinson, 1994, "Divide to coordinate: coevolutionary problem solving", Santa Fe Institute Working Paper, 94-06-031
[118] Kauffman, 1995, "At home in the universe", Oxford and New York: Oxford University Press. See Especially 252-64
[119] March, 1978, "Bounded rationality, ambiguity and the engineering of choice", Bell Journal of Economics, 9, 587-608
[120] Poundstone, 1985, "The recursive universe", Chicago, IL: Contemporary Books
[121] Schelling, 1974, "On the Ecology of Micromotives", In The corporate society, Robert Morris (ed.), 19-64
[122] LeBaron, 2001, "A Builder's Guide to Agent Based Financial Markets",Quantitative Finance, Vol. 1, No.2, P.254
[123] Barnett, Greve and Park, "An Evolutionary model of Organizational Performance", Strategic Management Journal, 15: 11-28
[124] Chattoe, 2000, "Why is Building Multi-Agnet Models of Social System so Difficult? A Case Study of Innovation Diffusion", Department of Sociology University of Oxford
[125] Vanputte, Osbom and Hiles, "A Composite Agent Architecture for Multi-Agent Simulations", MOVES Institute NavalPostgraduate School University Circle
[126] McFadzean, Stewart and Tesfatsion, "A Computational Laboratory for Evolutionary Trade Networks", IEEE Transactions on Evolutionary Computation, Vol. 2001
[127] Anderlini and Fell, "Costly bargaining and renegotiation", Econometrica, 69(2001),377-411 Press, 1986
[128] Potts, "The New Evolutionary Microeconomics: Complexity, Competence and Adeptive Behavior", Cheltenham: Edward Elgar, 2000
[129] Mirowski, "More Heat then Light: Economics as Social Physics", New York, NY: Cambridge Universisty Press, 1989
[130] Byrne, "Complexity Theory and the Social Sciences: An Introduction", London: Routledge, 1998
[131] Depew and Weber, "Darwinism Evolving: System Dynamics and the Geneology of Natural Selection", Cambridge,
MA: MIT Press, 1995
[132] Penrose, "Biological analogies in the theory of the firm", American Economic Review, 42 (1952), 809-19
[133] Freeman and Louca, "As Time Goes By: From the Industrial Revolutions to the Information Revolution", Oxford: Oxford University Press, 2001
[134] Nelson and Winter, "An Evolutionary Theory of Economic Change", Cambridge, MA: Belknap Press of Harvard University Press, 1982
[135] Dopfer, Foster and Potts, "Micro-meso-Macro", Journal of Evolutionary Economics, 14(2004), 263-280
[136] Flieth and Foster, "Interactive expectations", Journal of Evolutionary Economics, 12(2002), 375-396
[137] Alchian, "Uncertainty, evolution and economic theory" ,Journal of Political Economy, 58(1950),221-22
[138] Kauffman, "The Origins of Order: Self-Organization and Selection in Evolution", New York, NY: Oxford University Press, 1993
[139] Foster, "Is there a role for transactions cost eoconomics if we view firm as complex adaptive systems?", Contemporary Economic Policy, 18 (2000b), 369-385
[140] Foster and Metcalfe, "Frontiers of Evolutionary Economics: Competition, Self-Organization and Innovation Policy", Cheltenham: Edward Elgar, 2001
[141] Metcalfe, Foster and Tamlogan, "Adaptive Economic Growth", Cambridge Journa of Economics, 29 (2004),
[142] Foster and Wild, "Econometric Modelling in the Presence of Evolutionary Change", Cambridge Journal of Economics, 23 (1999), 749-770
[143] Forster, "Competitive Selection, Self-Organization and Joseph A.Schumpeter", Journal of Evolutionary Economics, 10 (2000a), 311-328
[144] Foster, "Economics and the self-organization approach: Alfred Marshall revisited?", The Economic Journal, 103 (1993), 975-91
[145] Foster, "From simplistics to complex systems in economics", Cambridge Journal of Economics, 29, 2005-5-14
[146] Amblard, Ferrand and Hill,"How a Conceptual Framework can Help to Design Models Following Decreasing Abstraction", Proceedongs of 13~th SCS-European Simulation Symposium, Marseille (France), Oct. 2001
[147] Bowles and Gintis, "Walrasian economics in retrospect", Quarterly Journal of Economics, 115, 1411-39
[148] Friedman, "The Methodology of positive economics" in Essays in Positive Economics, M. Friedman,Chicago, IL: University of Chicago Press, 1953
[149] Rosser, "On the Complexities of Complex Economic Dynamics", Journal of Economic Perspectives, 13 (1999), 169-192
[150] Hayek, "The theory of complex phenomena", in Studies in Philosophy, Policys and Econoics, F. Hayek, Chicago, IL: University of Chicago Press, 1967
[151] Foley, "Unholy Trinity: Labour, Land and Capital", London: Routledge, 2003
[152] Brooks and Wiley, "Evolution as Entropy: Toward a Unified Theory of Biology", Chicago, IL: Chicago University
[153] McKelvey, "Complexity theory in organization science: seizing the promise or becoming a fad?", Emergence, 1 (1999), 3-32
[154] Popper, "The Poverty of Historicism", London: Routledge, 1944
[155] Entriken and wan, "Agent-Based Simulation of an Automatic Mitigation Procedure", Proceedongs of the 38~th Hawaii
International Conference on System Science, 2005
[156] David, Sichman and Coelho, "Agent-Based Social Simulation with Coalitions in Social Reasoning", Proceedings of MABS'00, Revised and Additional Papers, LNAI 1979, Spring Verlag, P. 245-266, 2001
[157] Wagner and Tulba, "Agent-Oriented Modeling and Agent-Based Simulation", Proceedongs of 5th International Workshop on Agent-Oriented Information Systems, 2003
[158] Simon, Chang and Pullen, "An agent architecture for network support of distributed simulation systems", Proceedings of the 7th IEEE Ingernational Symposium on Distributed Simulation and Real-Time Application, 2003
[159] Anderson, "An Agent-Based Event Driven Foraging Model", Natureal Resource Modeling, Vol. 15, No.1, 2002
[160] Oprea, "Coordination in an Agent-Based Virtual Enterprise", Studies in Informatics and Control, Vol.12, No.3, 2003
[161] Noe, Rebello and Wang, "Corporate Financing: An Artificial Agent-Based Analysis", The Journal of Finance, Vol.LVIII, No.3, June 2003
[162] Grothmann, "Multi-Agent Market Modeling Based on Neural Networks", Doctor thesis, Faculty of Economics, University of Bremen, Germany
[163] David Meen and Geoffrey Meen, "Social Behaviour as a Basis for Modelling the Urban Housing Market: A Review", Urban Studies, Vol.40, No.5-6, 917-935,2003
[2]许国志,顾基发,车宏安《系统科学与工程研究》,上海科技教育出版社,2000.10
[3]成思危,《复杂科学与管理》,南昌大学学报,2000.3
[4]方福康,《复杂经济系统的演化分析》,http://www.systemscience.org/zjjs/ffktxt1.htm
[5]方福康,《经济和生态环境的复杂性研究》,http://www.biox.cn/content/bioxall/200412173172.htm
[6]张世伟,《基于主体的宏观经济微观模拟模型》,财经科学,2004.1
[7]吴军,《紧缩与扩张》,清华大学出版社,2001.10
[8]成思危,《发展计划的制定与管理》,经济科学出版社,2004.3
[9]解三明,《中国经济增长潜力和经济周期研究》,中国计划出版社,2001.10
[10]汪红驹,《中国货币政策有效性研究》,中国人民大学出版社,2003,5
[11]武献华 庞明川,《中国经济转轨中的失业、通货膨胀与经济增长》,东北财经大学出版社,2003.4
[12]宋学锋,杨列勋,曹庆仁《复杂性科学研究进展》,科学出版社,2004.2
[13]高鸿桢,《实验经济学导论》,中国统计出版社,2003.4
[14]奧斯卡.兰格,《经济控制论导论》,中国社会科学出版社,1981
[15]曼内斯库,《经济控制论》,新时代出版社,1985
[16]波拉特,《信息经济》,中国展望出版社,1987
[17]理查德 H 戴,《混沌经济学》,上海译文出版社,1996
[18]贝纳西,《市场非均衡经济学》,上海译文出版社,1989
[19]沈华嵩,《经济系统的自组织理论》,中国社会科学出版社,1991
[20]陈禹,《CAS理论的发展与应用》,首都经济贸易大学多主体仿真理论与应用研讨会主题报告,2005.1
[21]钱小安,《中国货币政策的形成与发展》,上海人民出版社,2000
[22]贝塔朗菲,《一般系统论——基础,发展,应用》,北京,社会科学文献出版社,1987
[23]邹珊刚等,《系统科学》,上海人民出版社,1987
[24]夏绍玮,杨家本,杨振斌,《系统工程概论》,清华大学出版社,1995
[25]弗里德里希,克拉默,《混沌与秩序——生物系统的复杂结构》,上海科技教育出版社,2000
[26]刘洪,《经济系统预测的混沌理论原理与方法》,科学出版社,2003
[27]伍海华,李道叶,翟锋,《资本市场复杂性》,经济科学出版社,2003
[28]熊光楞,肖田元,张燕云,《连续系统仿真与离散事件系统仿真》,清华大学出版社,1991
[29]王红卫,《建模与仿真》,科学出版社,2002
[30]伍尔德里奇,《多Agent系统引论》,电子工业出版社,2003
[31] Leigh Testatsion, 2002, "Agent-Based Computational Economics: Growing Economies from the Bottom Up", Artificial Life, Vol. 8 No. 1,55-82
[32] Gintis, 2000, "Game theory evolving, Princeton", NJ: The Princeton University Press
[33] Dawid, 1999, "Adaptive learning by genetic algorithms: Analytical result and applications to economic models", Revised Second Edition, Berlin: Springer-Verlag
[34] Vriend, 2000, "An illustration of the essential difference between individual and social learning and its consequences for computational analyses", Journal of Economic Dynamics and Control 24,1-19
[35] Marks, 1998, "Evolved perception and behaviour in oligopolies", Journal of Economic Dynamics and Control 22, 1209-1233
[36] Axelrod, 1997, "The complexity of cooperation: Agent-based model of conflict and cooperation, Princeton", N.J.: The Princeton University Press
[37] Schelling, 1978, "Micromotives and macrobehavior", New York, NY: W. W. Norton Company
[38] Epstein and Axtell, 1996, "Growing artificial societies: Social science from the bootom up", Cambridge, MA: MIT Press
[39] Holland, 1995, "Hidden order: How adaptation builds complexity", Reading, MA: Addison-Wesley
[40] Riolo, 1997, "The effects and evolution of tag-mediated selection of partners in populations playing the interated prisoner's dilemma" In Thomas Baeck, Proceedings of the International Conference on Genetic Algorithms, San Francisco, CA: Morgan Kauffman
[41] Arifovic and Eaton, 1995, "Coordination via genetic learning", Computational Economics 8, 181-203
[42] Epstein, 2001, "Learning to be thoughtless: Social norms and individual computation", Computational Economics 18,9-24
[43] Tay and Linn, 2001, "Fuzzy inductive reasoning, expectation formation, and the behavior of security", Journal of Economic Dynamics and Control 25, 321-361
[44] LeBaron, 2001, "Empirical Regularities from interacting long and short horizon investors in an agent-based stock market", IEEE transactions on Evolutionary Computation 5, 442-455
[45] Izumi and Ueda, 2001, "Phase transition in a foreign exchange market: Analysis based on an artificial market approach", IEEE Transactions on Evolutionary Computation 5, 456-470
[46] Chen and Yeh, 2001, "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market", Journal of Economic Dynamics and Contral 25, 363-393
[47] Howitt and Clower, 2000, "The emergence of economic organization", Journal of Economic Behavior and Organization 41,55-84
[48] Bower and Bunn, 2001, "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and wales electricity market", Journal of Economic Dynamics and Control 25, 561-592
[49] Bunn and Oliveira, 2001,"Agent-based simulation: An application to the New Electricity Trading Arrangements of England and Wales", IEEE Transections on Evolutionary Computation 5,493-503
[50] Williamson and Masten, 1999,"The Economics of transaction costs, Northhampton", MA: Edward Elgar Publishing
[51] Albin and Foley, 1992, "Decentralized, dispersed exchange without an auctioneer: A simulation study", Journal of Economic Behavior and Organization 18, 27-51
[52] Kirman, 1997, "The economy as an interactive system", Proceedings Vol. XXVII, SFI Studies in the Sciences of Complexity, Reading, MA: Addison-Wesley
[53] Tesfatsion, 1997, "How Economists can get alife", Proceedings Vol. XXVII,SFI Studies in the Sciences of Complexity. Reading, MA: Addison-Wesley
[54] Tesfatsion, 1997, "A trade network game with endogenous partner selection", Dordrecht, The Netherlands: Kluwer Academic Publishers
[55] Vriend, 1995, "Self-organization of market: An example of a computational approach", Computational Economics 8
[56] Tesfatsion, 1998, "Preferential partner selection in evolutionary labor markets: A study in agent-based computational
economics", Proceedings of the Seventh Annual Conference on Evolutionary Programing. Burlin: Springer-Verlag
[57] Tesfatsion, 2001, "Structure, behavior, and market power in an evolutionary labor market with adaptive search", Journal of Economic Dynamics and Control 25, 419-457
[58] Tassier and Menczer, 2001, "Emerging small-world referral network in evolutionary labor markets", IEEE Transactions on Evolutionary Computation 5, 482-492
[59] Kirman and Vriend, 2001, "Evolving market structure: An ACE model of price dispersion and loyalty", Journal of Economic Dynamics and Control 25,459-502
[60] Holland, 1992, "Adaptation in natural and artificial system", Cambridge, MA: The MIT Press
[61] Rouchier, 2001, "A multiagent model for describing transhumance in North Cameroon", Journal of Economic Dynamics and Control 25, 527-559
[62] De Vany and Lee, 2001, "Quality signals in information cascades and the dedtribution of motion picture box office revenues", Jiurnal of Economic Dynamics and Control 25, 593-614
[63] Prietula, Carley and Gasser, 1998, "Simulating organizations: Computational models of institutions and groups", Cambridge, MA: The MIT Press
[64] Dawid, Reimann and Bullnheimer, 2001, "To innovate or not to innovate?, IEEE Transactions on Evolutionary Computation 5,471-481
[65] Miller and Andreoni, 1990, "A coevolutionary model of free riding behavior: Replicator dynamics as an explanation of the experimental results", Santa Fe Institute Working Paper
[66] Andreoni and Miller, 1995, "Auctions with adaptive artificial agents", Games and Economic Behavior 10, 39-64
[67] Arifovic, 1994, "Genetic algorithm learning and the cobweb model", Journal of Economic Dynamics and Control 18,3-28
[68] Arifovic, 1996, "The Behavior of the exchange rate in the genetic algorithm and experimental economics", Journal of Political Economy 104, 510-541
[69] Aathur, 1991, "On designing economic agents that behave like human agents: A behavioral approach to bounded rationality", American Economic Review 81, 353-359
[70] Chan, LeBaron and Poggio, 1999,"Agent-based models of financial markets: A comparison with experimental markets", MIT Artificial Markets Project, Paper No. 124
[71] Marimon, McGrattan and Sargent, 1990, "Money as a medium of exchange in an economy with artificially intelligent agents", Journal of Economic Dynamics and Control 14,329-373
[72] Duffy, 2001, "Learning to speculate: Experiments with artificial and real agents", Journal of Economic Dynamics and Control 25, 295-319
[73] Pingle and Tesfatsion, 2001, "Non-employment benefits and the evolution of worker-employer cooperation: Experiments with real and computational agents", Economic Report 55, Iowa State University
[74] Basu, Pryor, Quint and Arnold "ASPEN: A Microsimulation Model of the Economy", Sandia Report 1996
[75] Dianne C. Barton, "ASPEN-EE: An Agnet-Based Model of Infrastructure Interdependency", Sandia Report 2000
[76] Dianne C. Barton, "Simulation Economic Effects of Disfuptions in the Telecommunications",Sandia Report 2004
[77] David A. Schoenwand, Dianne C Barton, Mark A. Ehlen, "An Agent-based Simulation Laboratory for Economics and Infrastructure Interdependency", Sandia Report 2004
[78] Arthur, Holland, LeBaron, Palmer and Tayler, 1996, "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market", Working paper of Santa Fe Institute
[79] LeBaron, "Building the Santa Fe Artificial Stock Market", Working paper of Santa Fe Institute, 2002
[80] Johnson, "What I learned from the Artificial Stock Market", Working paper of Santa Fe Institute, 2001
[81] Ehrentreich, 2002,"A Corrected Version of the Santa Fe Institute Artificial Stock Market Model", Martin-Luther-Universitat Halle-Wittenberg
[82] Pingle and Tesfatsion, 2003,"l Evolution of Worker-Employer Nerworks and Behaviors Under Alternative Non-Employment Benefits: An Agent-Based Computational Study"
[83] Ormerod, Rosewell and Smith, 2001, "An Agent-based Model of the Evolution of Market Structure and Competition", Volterra Consulting Ltd.
[84] Impullitti and RebMann, "An Agent-based Model of Wealth Distribution", CEPA Working Paper 2002
[85] Bigus, Schlosnagle, Pilgrim, Mills and Diao, "ABLE: A toolkit for building nultiagent autonomic systems", http://www.research.ibm.com/journal/sj/413/bigus.html
[86] Miles Parker, "What is Ascape and Why Should you Care?", Journal of Artificial Societies and Social Simulation, Volume 4 No. 1, January 2001
[87] "D-OMAR: Distributed Operator Model Architecture", http://omar.bbn.com/
[88] "Gambit: Software Tools for Game Theory", http://econweb.tamu.edu/gambit/
[89] "JASA - Java Auction Simulator API", http://www.csc.liv.ac.uk/~sphelps/jasa/
[90] "The MadKit Project (a Mult-Agent Development Kit)", http://www.madkit.org/
[91] Corliss, Gulyas, Kozsik and Fazekas, "Model Design Interface - A CASE Tool for the Multi-Agent Modeling", SwarmFest 99
[92] D.J. Higham and N.J. Higham, "MATLAB Guide (Second Edition)", SIAM 2005
[93] Murat Yildizoglu's, "Evolutionary Modelling Tools", http://beagle.u-bordeaux4.fr/yildi/ evolmod.html
[94] David McFadzean, 1994, "SimBioSys: A Class Framework for Biological Simulations", http://www.lucifer.com/~david/thesis/
[95] Margarita and Sonnessa,"Sim2Web: An Open Source System for Web-Enabling Economic and Financial Simulations", http://wf.econ.unito.it/sim2web
[96] Jerferson, "Virtual time", ACM Transactions on Programming Languages and Systems 7,3(July 1985), P.405-425
[97] Jerferson, "Virtual time II: The Cancelback protocol for storage management in distributed simulation", Proceedings of the 9th Annual ACM Symposium on Principles of Distributed Computation (Aug. 1990), P.75-90
[98] James A. Sprigg, Jr., Craig R. Jorgensen, Richard J. Pryor, "Approach and Development Strategy for an Agent-Based Model of Economic Confidence", Sandia Report 2004
[99] James A. Sprigg, Jr., Mark A. Ehlen, "Full Employment and Competition in the ASPEN Economic Model: Implications for Modeling Acts of Terrorism", Sandia Report 2004
[100] James A. Sprigg, "Market Disruption, Cascading Effects and Economic Recovery - A Life-Cycle Hypothesis Model", Sandia Report 2004
[101] Leigh Tesfatsion, "Agent-Based Computational Economics", ISU Economics Working Paper No 1, 2003.8
[102] Marcus Daniels, "Integrating Simulation Technologies with Swarm", SDG Report
[103] John Foster, "Why is Economics not a Complex Systems Science?", Discussion Paper No. 336,School of Economics, The University of Queensland
[104] Casti, "Complexification", Harker Collins 1994
[105] Casti et al., "Boundaries and Barriers -on the limit to Scientific Knowledge", Addison-Wesley, 1996
[106] Bossomaier and Green "Pattern in the Sand, Computer, Complexity and Life", Allen & Unwin, 1998
[107] Bergmann and Bennett, "A Microsimulated Transactions Model of the United StatesEconomy", Baltimore: The Johns Hopkins University Press, 1986
[108] Stefansson, 1997,"Swarm: An Object Oriented Simulation Platform Applied to Markets and Organizations", Evolutionary Programming Ⅵ, New York: Springer-Verlag
[109] Page, 1999, "Computational Models from A to Z"
[110] Arthur, Durlauf, Lane, 1997, "The Economy as an Evolving Complex System", Addison-Wesley, Reading, Mass, 1997
[111] Basu and Pryor, 1997, "Growing a Market Economy", Sandia report. SAND-97-2093
[112] Minar, Burkhart, Lanton and Askenazi, 1996, "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations", SFI working paper, http://www.santafe.edu/projects/swarm
[113] Alvin and Foley, 1992, "Decentralized, Dispersed Exchange without an Auctioneer", Journal of Economic Behavior and Organization, 18, 27-51
[114] Axtell, Axelrod, Epstein and Cohen, 1996, "Aligning simulation models: a case study and results", Computational and Mathematical Organization Theory, 1, 123-141
[115] Cyert and March, 1963, "A behavioral theory of the firm", Englewood Cliffs, N. J., Prentice-Hall, 1963
[116] Epstein and Axtell, 1996, "Growing artificial societies: social science from the bottom up", Washington, DC: Brookings and Cambridge, MA: MIT Press
[117] Kauffman, Macready and Dickinson, 1994, "Divide to coordinate: coevolutionary problem solving", Santa Fe Institute Working Paper, 94-06-031
[118] Kauffman, 1995, "At home in the universe", Oxford and New York: Oxford University Press. See Especially 252-64
[119] March, 1978, "Bounded rationality, ambiguity and the engineering of choice", Bell Journal of Economics, 9, 587-608
[120] Poundstone, 1985, "The recursive universe", Chicago, IL: Contemporary Books
[121] Schelling, 1974, "On the Ecology of Micromotives", In The corporate society, Robert Morris (ed.), 19-64
[122] LeBaron, 2001, "A Builder's Guide to Agent Based Financial Markets",Quantitative Finance, Vol. 1, No.2, P.254
[123] Barnett, Greve and Park, "An Evolutionary model of Organizational Performance", Strategic Management Journal, 15: 11-28
[124] Chattoe, 2000, "Why is Building Multi-Agnet Models of Social System so Difficult? A Case Study of Innovation Diffusion", Department of Sociology University of Oxford
[125] Vanputte, Osbom and Hiles, "A Composite Agent Architecture for Multi-Agent Simulations", MOVES Institute NavalPostgraduate School University Circle
[126] McFadzean, Stewart and Tesfatsion, "A Computational Laboratory for Evolutionary Trade Networks", IEEE Transactions on Evolutionary Computation, Vol. 2001
[127] Anderlini and Fell, "Costly bargaining and renegotiation", Econometrica, 69(2001),377-411 Press, 1986
[128] Potts, "The New Evolutionary Microeconomics: Complexity, Competence and Adeptive Behavior", Cheltenham: Edward Elgar, 2000
[129] Mirowski, "More Heat then Light: Economics as Social Physics", New York, NY: Cambridge Universisty Press, 1989
[130] Byrne, "Complexity Theory and the Social Sciences: An Introduction", London: Routledge, 1998
[131] Depew and Weber, "Darwinism Evolving: System Dynamics and the Geneology of Natural Selection", Cambridge,
MA: MIT Press, 1995
[132] Penrose, "Biological analogies in the theory of the firm", American Economic Review, 42 (1952), 809-19
[133] Freeman and Louca, "As Time Goes By: From the Industrial Revolutions to the Information Revolution", Oxford: Oxford University Press, 2001
[134] Nelson and Winter, "An Evolutionary Theory of Economic Change", Cambridge, MA: Belknap Press of Harvard University Press, 1982
[135] Dopfer, Foster and Potts, "Micro-meso-Macro", Journal of Evolutionary Economics, 14(2004), 263-280
[136] Flieth and Foster, "Interactive expectations", Journal of Evolutionary Economics, 12(2002), 375-396
[137] Alchian, "Uncertainty, evolution and economic theory" ,Journal of Political Economy, 58(1950),221-22
[138] Kauffman, "The Origins of Order: Self-Organization and Selection in Evolution", New York, NY: Oxford University Press, 1993
[139] Foster, "Is there a role for transactions cost eoconomics if we view firm as complex adaptive systems?", Contemporary Economic Policy, 18 (2000b), 369-385
[140] Foster and Metcalfe, "Frontiers of Evolutionary Economics: Competition, Self-Organization and Innovation Policy", Cheltenham: Edward Elgar, 2001
[141] Metcalfe, Foster and Tamlogan, "Adaptive Economic Growth", Cambridge Journa of Economics, 29 (2004),
[142] Foster and Wild, "Econometric Modelling in the Presence of Evolutionary Change", Cambridge Journal of Economics, 23 (1999), 749-770
[143] Forster, "Competitive Selection, Self-Organization and Joseph A.Schumpeter", Journal of Evolutionary Economics, 10 (2000a), 311-328
[144] Foster, "Economics and the self-organization approach: Alfred Marshall revisited?", The Economic Journal, 103 (1993), 975-91
[145] Foster, "From simplistics to complex systems in economics", Cambridge Journal of Economics, 29, 2005-5-14
[146] Amblard, Ferrand and Hill,"How a Conceptual Framework can Help to Design Models Following Decreasing Abstraction", Proceedongs of 13~th SCS-European Simulation Symposium, Marseille (France), Oct. 2001
[147] Bowles and Gintis, "Walrasian economics in retrospect", Quarterly Journal of Economics, 115, 1411-39
[148] Friedman, "The Methodology of positive economics" in Essays in Positive Economics, M. Friedman,Chicago, IL: University of Chicago Press, 1953
[149] Rosser, "On the Complexities of Complex Economic Dynamics", Journal of Economic Perspectives, 13 (1999), 169-192
[150] Hayek, "The theory of complex phenomena", in Studies in Philosophy, Policys and Econoics, F. Hayek, Chicago, IL: University of Chicago Press, 1967
[151] Foley, "Unholy Trinity: Labour, Land and Capital", London: Routledge, 2003
[152] Brooks and Wiley, "Evolution as Entropy: Toward a Unified Theory of Biology", Chicago, IL: Chicago University
[153] McKelvey, "Complexity theory in organization science: seizing the promise or becoming a fad?", Emergence, 1 (1999), 3-32
[154] Popper, "The Poverty of Historicism", London: Routledge, 1944
[155] Entriken and wan, "Agent-Based Simulation of an Automatic Mitigation Procedure", Proceedongs of the 38~th Hawaii
International Conference on System Science, 2005
[156] David, Sichman and Coelho, "Agent-Based Social Simulation with Coalitions in Social Reasoning", Proceedings of MABS'00, Revised and Additional Papers, LNAI 1979, Spring Verlag, P. 245-266, 2001
[157] Wagner and Tulba, "Agent-Oriented Modeling and Agent-Based Simulation", Proceedongs of 5th International Workshop on Agent-Oriented Information Systems, 2003
[158] Simon, Chang and Pullen, "An agent architecture for network support of distributed simulation systems", Proceedings of the 7th IEEE Ingernational Symposium on Distributed Simulation and Real-Time Application, 2003
[159] Anderson, "An Agent-Based Event Driven Foraging Model", Natureal Resource Modeling, Vol. 15, No.1, 2002
[160] Oprea, "Coordination in an Agent-Based Virtual Enterprise", Studies in Informatics and Control, Vol.12, No.3, 2003
[161] Noe, Rebello and Wang, "Corporate Financing: An Artificial Agent-Based Analysis", The Journal of Finance, Vol.LVIII, No.3, June 2003
[162] Grothmann, "Multi-Agent Market Modeling Based on Neural Networks", Doctor thesis, Faculty of Economics, University of Bremen, Germany
[163] David Meen and Geoffrey Meen, "Social Behaviour as a Basis for Modelling the Urban Housing Market: A Review", Urban Studies, Vol.40, No.5-6, 917-935,2003