基于认知层次模型和扩展ABM方法的资本市场异象研究——e-Science在社会科学中的初步应用(英文)
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摘要
具有微观宏观一体化特征的基于主体的建模和模拟方法 (ABM)正在迅速兴起和广泛应用,为探索经济和金融市场的复杂性提供了有力工具,展现了e-Science应用于经济学及人文社会科学研究中的美好前景,但必须通过行为分析深化等丰富其理论基础。本文利用e-Science及相关技术,试图通过深化行为分析和模拟结构演变过程,揭示社会经济的复杂现象等传统理论方法难以解决的问题;具体在研究中扩展了认知层次行为模型(CH)和ABM方法,基于中国股市上的真实投资行为,在MatLab和Netlogo软件平台上重点用计算实验方法比较了机构投资者和个体投资者对市场基本信息和政策信号的不同响应模式和反应强度,如此能比传统理论方法更有效和深入地分析发现微观层面上异质性主体的关键行为特征与市场总体的异常现象或典型化事实之间的相互联系和影响,也从实证角度在一定程度上支持了中国股市具有政策敏感性的特征。
Agent-based modeling(ABM) with micro-macro link features is useful in exploring economic and capital market complexities and suggests the potential application of e-Science in areas of humanities and social studies. However, the theoretical foundations of ABM must be enriched through behavioral analysis. So e-Science and related technology are used to conduct in-depth behavioral analysis and structure evolution simulation and to determine complex social and economic phenomena that cannot be addressed by traditional theories and methods. The cognitive hierarchy model(CH) depicts key behavioral characteristics, and the ABM method is expanded based on active data obtained from investor behavior in the Chinese stock market. The responses of policy signals to the price reaction pattern in the stock market are differentiated through computational experiments via MatLab and NetLogo in the CH model extension. Analyzing the relationship between heterogeneous investors displaying real CH behavior and capital market emergence is more efficient than current methods, and the computational experiment based on extensive ABM technology supports the empirical conclusion that China's stock market is policy-sensitive.
Agent-based modeling(ABM) with micro-macro link features is useful in exploring economic and capital market complexities and suggests the potential application of e-Science in areas of humanities and social studies. However, the theoretical foundations of ABM must be enriched through behavioral analysis. So e-Science and related technology are used to conduct in-depth behavioral analysis and structure evolution simulation and to determine complex social and economic phenomena that cannot be addressed by traditional theories and methods. The cognitive hierarchy model(CH) depicts key behavioral characteristics, and the ABM method is expanded based on active data obtained from investor behavior in the Chinese stock market. The responses of policy signals to the price reaction pattern in the stock market are differentiated through computational experiments via MatLab and NetLogo in the CH model extension. Analyzing the relationship between heterogeneous investors displaying real CH behavior and capital market emergence is more efficient than current methods, and the computational experiment based on extensive ABM technology supports the empirical conclusion that China's stock market is policy-sensitive.
引文
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[19]Miller,J.H.&Page,S.E.:Complex adaptive systems:an introduction to computational models of social life.Princeton university press,2007.
[20]Schelling,T.C.:Micromotives and macrobehavior.New York:Norton,1978.
[21]Shao,P.:Bounded rationality,cognitive hierarchy and investment game.The journal of quantitative&technical economics.10:145-155,2010.
[22]Tesfatsion L.and Judd K.L.:Handbook of computational economics,volume 2:Agent-based computational economics.Amsterdam,the Netherlands:Holland/Elsevier,2006.
[23]Vicsek T.:Complexity:the bigger picture.Nature,vol.418,Jul.11,2000.
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[26]Wang,G.C.:Deepening micro-behavioral analysis and exploring the complexity of macro-economy.Jiangsu Social Sciences,No.3:20-28,2013.
①Blake LeBaron,http://people.brandeis.edu/~blebaron/blogs/complexintro.html.
②Leigh Tesfation,http://www2.econ.iastate.edu/tesfatsi/abmread.htm#ABMSocSci.
[2]Axtell,R.:The complexity of exchange.The economic journal,Features,Jun.,115(504):193-210,2005.
[3]Axelrod,R.:The complexity of cooperation:Agentbased models of competition and collaboration.Princeton university press,1997.
[4]Bonabeau,E.:Agent-based modeling:methods and techniques for simulating human systems,May 14,99(S3):7280-7287,2002.
[5]Chen,S.-H.:Varieties of agents in agent-based computational economics:Ahistorical and an interdisciplinary perspective.Journal of Economic Dynamics and Control,doi:10.1016/j.jedc..09.003,1-25,2011.
[6]Camerer,C.,Ho,T.and Chong K.:Experimental economics,models of thinking,learning and teaching in games.AEA papers and proceedings,93(2):192-195,2003.
[7]Camerer,C.F.,Ho T.and Chong J.K.:A cognitive hierarchy model of games.Quarterly journal of economics,119(3):861-898,2004.
[8]Duffy,J.:Agent-based models and human subject experiments,In L.Tesfatsion and K.L.Judd(Eds.),Handbook of computational economics,volume 2:Agent-based computational economics.Amsterdam,the Netherlands:Holland/Elsevier,2006.
[9]Flake,G.W.:The computational beauty of nature:computer explorations of fractals,chaos,complex systems,and adaptation.MA:The MIT Press,Cambridge,1998.
[10]Farmer,J.D.,and Foley,D.:The economy needs agentbased modeling,Nature,460(6):685-686,Aug.,2009.
[11]Granovetter,M.:Threshold models of collective behavior.American journal of sociology,83(6):1420-1443,1978.
[12]Gilbert,N.:Agent-based models,Series:quantitative applications in the social sciences.SAGE publications,2008.
[13]Hommes,C.H.:Heterogeneous agent models in economics and finance.In L.Tesfatsion and K.L.Judd(Eds.),Handbook of computational economics,volume 2:Agent-based computational economics.Amsterdam,the Netherlands:Holland/Elsevier,2006.
[14]LeBaron,B.:Evolution and time horizons in an agentbased stock market,Macroeconomic Dynamics 5(2):225-254,2001.
[15]Long Y.T.and Wang G.C.:The market effect of asymmetry investment behavior and agent-based computational research.The 21st century quantitative economics,Volume 12,2012.
[16]Laszlo,E.:The systems view of the world.Cresskill,Hampton press,1996.
[17]Macal and North:AGENT-BASED MODELING AND SIMULATION:ABMS EXAMPLES,in Proceedings of the 2008 Winter Simulation Conference,S.J.Mason et.al.(eds.),IEEE,2008.
[18]Manzan,S.&Westerhoff,F.H.:Heterogeneous expectations,exchange rate dynamics and predictability.Journal of economic behavior&Organization,64(1):111-128,2007.
[19]Miller,J.H.&Page,S.E.:Complex adaptive systems:an introduction to computational models of social life.Princeton university press,2007.
[20]Schelling,T.C.:Micromotives and macrobehavior.New York:Norton,1978.
[21]Shao,P.:Bounded rationality,cognitive hierarchy and investment game.The journal of quantitative&technical economics.10:145-155,2010.
[22]Tesfatsion L.and Judd K.L.:Handbook of computational economics,volume 2:Agent-based computational economics.Amsterdam,the Netherlands:Holland/Elsevier,2006.
[23]Vicsek T.:Complexity:the bigger picture.Nature,vol.418,Jul.11,2000.
[24]Wang,G.C.:Behavior orientation of contemporary economics and Chinese opportunity.Journal of graduate school of Chinese academy of social sciences,Jul.(No.4):49-56,2010.
[25]Wang,G.C.:The evolvement and beyond of rationalism in modern economics.Social Sciences in China,No.7:66-82,2012.
[26]Wang,G.C.:Deepening micro-behavioral analysis and exploring the complexity of macro-economy.Jiangsu Social Sciences,No.3:20-28,2013.
①Blake LeBaron,http://people.brandeis.edu/~blebaron/blogs/complexintro.html.
②Leigh Tesfation,http://www2.econ.iastate.edu/tesfatsi/abmread.htm#ABMSocSci.