商业银行信贷市场的非对称信息博弈及基于Agent的SWARM仿真
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摘要
文章首先对数学模型和计算机模型进行了阐述,对两种不同的建模方法分别作了详细的描述。构建了一个基于数学方程模型的实例,并求出其迭加解,较好地体现了数学模型的特征及应用。较全面地介绍了复杂适应系统中的典型计算机模型的生成过程,总结了计算机模型不同于数学模型的特性。
然后把博弈论严密的逻辑结构和分析方法当作解决经济中的非对称信息问题的一个有效分析工具,运用非对称信息博弈方法,系统分析我国商业银行信贷市场的决定、制约因素,结合具体问题分别建立了我国商业银行信贷市场的事前逆向选择、事后道德风险的数学模型,并在这一模型基础上提出可行的商业银行信贷市场完善、改进的建议。
文章用博弈数学模型对商业银行信贷市场非对称信息问题进行了建模。但传统的博弈模型是围绕具有完美理性的主体一那些能完美地预测自身行为后果(包括其他主体的反应)的主体建立的。这就是说参与博弈的主体的效用函数是主体在均衡路径上或非均衡路径上的行为的确定函数。而对于意外的不寻常的动态因素通常被视为主体“犯错误”或小概率的效用,不确定的偶然事件所造成。然而,面对真实的银行信贷市场,导致均衡变化的因素是多方面的,复杂的。博弈论虽然擅长处理经济系统中的非对称信息及机制设计,但博弈论面临一个严重的问题,就是它对参与主体在理性和行为能力基本假设方面上的理性基础采用的是一种“完全理性”的假设。目前包括合作博弈和非合作博弈理论都假设博弈参与者对参与的博弈有一个一致模型,认为他们总是做出最优化的选择并相信其他博弈参与者也会做出优化选择,认为参与者都具备关于博弈的公共知识。从有一个确定效用函数,完全理性假设出发,博弈论的一致模型属于数学模型的范畴,它实质上仍是化为数学方程求解。而事实上商业银行信贷市场中的各个博弈参与主体是“有限理性”的。有限理性意味着博弈方之间的策略均衡往往是学习调整的结果,而不是选择性的结果。而且,即使达到了均衡也可能再次偏离。因此建立在完全理性基础上的博弈分析方法和均衡概念,对于分析有限理性博弈的问题在某些方面并不太适用。
针对有限理性的博弈主体,我们引入了复杂适应系统的概念来描述。这首先要对经济系统进行重新认识。现在的银行金融体系不是封闭的、机械的和线性的简单系统,而是开放的复杂的、具有适应性的、非线性的复杂系统。如此庞大而复杂的系统,光靠数学模型方法来解析其中的复杂关系是不够的。文章通过构建微观经济主体,运用SWARM软件平台,模拟银行信贷市场的生成,让博弈主体在此环境中,进行仿生命的运动,在仿真系统中对银行机构的涌现进行仿真模拟。通过改进模型,为微观经济主体逐步添加新的属性,提高其智能性,以图从更全面的方位去思考智能主体与客观世界的同一性。仿真结果表明,主体智能性的提高,是导致银行机构涌现复杂性的根源,同时微观经济主体的有限理性和非对称信息的不断调整、改进,使得系统静态均衡更趋成熟、稳定。这一现象让我们对现实的银行信贷市场有更深刻认识。这类问题没有一个确定的效用函数,系统中的主体是有限理性的适应性主体。求解这类问题要绕开建立“数学方程”的数学模型,而代之以用计算机程序定义的“计算机模型”。
本论文结合当前国际经济理论前沿,从实际情况出发建立了商业银行信贷市场的非对称信息博弈数学模型,系统分析了解决该问题的机制设计。并针对博弈论在模型设定和分析中的困难,建立了一个基于Agent的直接计算机模型,在特定的软件平台构筑博弈环境,排除人为因素的干扰,使博弈结果可靠,有效率,更具直接性,现实性。
全文以数学模型到计算机模型为主线,突出了它们各自的特征,共分八章。第一章是绪论,提出论文的研究意义,简要概括国内外相关研究的状况,对论文整体思路、内容及创新进行论述。第二章,简要论述数学模型,并以作者所撰写的一篇论文为实例,详细论述了一类“非常规”系统的数学模型与仿真方法,借以突出“数学模型”的主要特征。至于博弈数学模型,放在第四章进行详细论述。第三章,对计算机模型的重要性进行阐述,分析复杂适应系统及涌现论中的主要计算机模型及建模方法,总结计算机模型的特性。第四章,分析我国商业银行信贷市场的存在风险,运用非对称信息博弈对国内学者很少涉及的事前逆向选择、事后道德风险进行分析建模,设计最优的合约。对改进现实情况提出建议。第五章,介绍复杂适应系统的形成和特性。本章内容包括了作者撰写的另一篇论文《基于复杂适应系统的经济建模仿真方法》,对复杂银行信贷系统进行理论阐述,比较计算机模型与博弈论数学模型的异同,提出基于Agent的计算机建模思想。第六章,介绍SWARM软件平台及其运行原理。第七章,在SWARM平台上构建基于Agent的银行信贷市场生成模型,对系统中的微观经济主体的行为规则、含义、结构特征进行定义和形式化,进行模拟仿真,并对仿真结果进行分析。第八章,是对整个论文的回顾和展望。
The mathematical model and the computer model are illustrated firstly in this thesis. The two different modeling methods are described in detail separately. A real case about a model based on the mathematics equation is constructed. In the real case, superposition solution is got and this manifests the characteristic and practice of the mathematic model. Secondly, the production process of the classic computer model in complex adaptive module is also described. The characteristics of the mathematical model differs from the computer model are in summarized.
Regarding the strict logical and the analysis method of the game theory as an effective tool to solve the asymmetrical information problems in economy. With the asymmetrical information game methods, the decision and the restriction factors of China lending market of commercial bank are analysis in general. Combined with the real practice problems, the mathematic model about Adverse selection in advance and moral hazard afterward in lending market of China commercial bank is set up. The feasible suggestions how to improve the China commercial lending market are pointed out according to this model.
By using the game mathematic model, the asymmetrical information problems in the lending market of commercial bank builds up a model. However the traditional game model is set up based on the completely rational agents, which can forecast the result cased by their own behaviors (including the others responses). In another words, the utility function of the agents in the game are the definite function on the equilibrium and unequilibrium way. The unexpected dynamic factor are always to be regard as the agents which are make mistakes or called " small probability" caused by the indefinite accident. However, a lot of factors can cause the real lending market lose its equilibrium. Even through the game theory is better to deal with the asymmetrical information and mechanism design in economic system, it still have to face a serious issue,. That is the complete rational agent regarded as the basic supposition. Currently, the agents participated in the game are all believed the others in the game will do the optimistic choices and each of them have the knowledge about the game. Actually the same model is utilized in the cooperative game and the uncooperative game.
With a definite utility function and the complete rational supposition, The game model is still included in the category of the mathematical model. The game model actually changes into mathematics equation solution. However, the agent participated in the game are limited rationality in the lending market of the commercial bank. The limited rationality means each side participated in the game make the strategy based on the adjusted result during the studying process not the selective result. Moreover, the equilibrium will be broken again because of the deviation. Therefore, the game analysis method and equilibrium concept based on the complete rational supposition is limited to used to analysis the game issues with the limited rational.
In view of the game agent with the limited rationality, we introduce the concept "complex adaptive system" to illustrate. Firstly, we should review the economic system. The bank financial system is not a closed, machined and simple linear system. It is open, compatible and complex nonlinear system. Such huge and complex system can not only use the mathematical model to analyze its complex relationship. A microscopic economic agent has been built in this thesis. Utilizing the SWARM software platform to simulate the bank lending market and putting the game agents imitate the life movement in such environment, we carry the bank organization's emergence in the simulated system. By improving the model, the new nature are be added in the microscopic economic agent. The new nature can improve the intelligence of the agents and it can help to ponder the identity between the intelligent agent and the object world in a more comprehensive aspects. According to the simulative results, the improved agent intelligence can be regard as the root to cause the bank organization emergence. At the same time, the continual readjusted limited rationality and the asymmetrical information in the microscopic economic agent can make the static equilibrium in the system to be more mature and stable. This phenomenon make us have a more profound understanding to the real bank lending market. There is no definite utility function in such kind of the issues. The agents in the system are the agents with the limited rationality and adaptive. To solve such kind of question must use the computer program to define " the computer model" instead of the " mathematical model".
Combined with the international economy theory front, a asymmetrical information game mathematical model about the lending market of the commercial bank is established. The model analysis the mechanism design which can be used to solve the issues systematical. And in view of the difficulties in the model set and analysis which meet in the game theory, a direct computer model is established based on the microscopic economic agent. With the specific software platform, the game environment has been built and this make the game results more reliable, effective , direct and more practical.
The whole thesis is divided into eight chapters. The introduction proposed the significance to do the thesis. In this chapter, general domestic and foreign related researches are brief illustrated. The second chapter, the mathematical model is elaborated briefly. Taking a paper "one kind of the unconventional systemic simulation scheme" as a real case, main features of the "mathematic model" are elaborated. As the game mathematic model, the author will illustrate in the chapter 4. In the third chapter, the importance of the computer model is described. The main computer model and the modeling methods in complex adaptive system and the emergent theory are also been analyzed. The key features of the compute model are summarized in this chapter. In the chapter four, the author analyzes the real existed risks of the lending market of China commercial bank. By using the asymmetrical information game, A optimal contract is designed and the analyzed model is built by the adverse selection in advance and moral hazard afterward, which is very little involved in the analysis of the domestic scholar. A serious suggestions to improve the real situation in the real world are proposed in this chapter. In the firth chapter, the author introduces complex adaptive system's formation and characteristics. Another article "the CAS-based economic simulation mothod" illustrates the complex lending market in theory. This article compare the difference between the computer model an the game mathematical model and proposes a computer model based on the agent. The sixth chapter, the SWARM software platform and the operation principle are introduced. In the seventh chapter, the model about lending market based on the agent is build up on the SWARM platform. This model defines and formalizes the behavior rule , the meaning, the structural features of the microscopic economic agent in the system. At the same time, the model carries on the simulation. In the Eight chapter, the author review the whole thesis.
然后把博弈论严密的逻辑结构和分析方法当作解决经济中的非对称信息问题的一个有效分析工具,运用非对称信息博弈方法,系统分析我国商业银行信贷市场的决定、制约因素,结合具体问题分别建立了我国商业银行信贷市场的事前逆向选择、事后道德风险的数学模型,并在这一模型基础上提出可行的商业银行信贷市场完善、改进的建议。
文章用博弈数学模型对商业银行信贷市场非对称信息问题进行了建模。但传统的博弈模型是围绕具有完美理性的主体一那些能完美地预测自身行为后果(包括其他主体的反应)的主体建立的。这就是说参与博弈的主体的效用函数是主体在均衡路径上或非均衡路径上的行为的确定函数。而对于意外的不寻常的动态因素通常被视为主体“犯错误”或小概率的效用,不确定的偶然事件所造成。然而,面对真实的银行信贷市场,导致均衡变化的因素是多方面的,复杂的。博弈论虽然擅长处理经济系统中的非对称信息及机制设计,但博弈论面临一个严重的问题,就是它对参与主体在理性和行为能力基本假设方面上的理性基础采用的是一种“完全理性”的假设。目前包括合作博弈和非合作博弈理论都假设博弈参与者对参与的博弈有一个一致模型,认为他们总是做出最优化的选择并相信其他博弈参与者也会做出优化选择,认为参与者都具备关于博弈的公共知识。从有一个确定效用函数,完全理性假设出发,博弈论的一致模型属于数学模型的范畴,它实质上仍是化为数学方程求解。而事实上商业银行信贷市场中的各个博弈参与主体是“有限理性”的。有限理性意味着博弈方之间的策略均衡往往是学习调整的结果,而不是选择性的结果。而且,即使达到了均衡也可能再次偏离。因此建立在完全理性基础上的博弈分析方法和均衡概念,对于分析有限理性博弈的问题在某些方面并不太适用。
针对有限理性的博弈主体,我们引入了复杂适应系统的概念来描述。这首先要对经济系统进行重新认识。现在的银行金融体系不是封闭的、机械的和线性的简单系统,而是开放的复杂的、具有适应性的、非线性的复杂系统。如此庞大而复杂的系统,光靠数学模型方法来解析其中的复杂关系是不够的。文章通过构建微观经济主体,运用SWARM软件平台,模拟银行信贷市场的生成,让博弈主体在此环境中,进行仿生命的运动,在仿真系统中对银行机构的涌现进行仿真模拟。通过改进模型,为微观经济主体逐步添加新的属性,提高其智能性,以图从更全面的方位去思考智能主体与客观世界的同一性。仿真结果表明,主体智能性的提高,是导致银行机构涌现复杂性的根源,同时微观经济主体的有限理性和非对称信息的不断调整、改进,使得系统静态均衡更趋成熟、稳定。这一现象让我们对现实的银行信贷市场有更深刻认识。这类问题没有一个确定的效用函数,系统中的主体是有限理性的适应性主体。求解这类问题要绕开建立“数学方程”的数学模型,而代之以用计算机程序定义的“计算机模型”。
本论文结合当前国际经济理论前沿,从实际情况出发建立了商业银行信贷市场的非对称信息博弈数学模型,系统分析了解决该问题的机制设计。并针对博弈论在模型设定和分析中的困难,建立了一个基于Agent的直接计算机模型,在特定的软件平台构筑博弈环境,排除人为因素的干扰,使博弈结果可靠,有效率,更具直接性,现实性。
全文以数学模型到计算机模型为主线,突出了它们各自的特征,共分八章。第一章是绪论,提出论文的研究意义,简要概括国内外相关研究的状况,对论文整体思路、内容及创新进行论述。第二章,简要论述数学模型,并以作者所撰写的一篇论文为实例,详细论述了一类“非常规”系统的数学模型与仿真方法,借以突出“数学模型”的主要特征。至于博弈数学模型,放在第四章进行详细论述。第三章,对计算机模型的重要性进行阐述,分析复杂适应系统及涌现论中的主要计算机模型及建模方法,总结计算机模型的特性。第四章,分析我国商业银行信贷市场的存在风险,运用非对称信息博弈对国内学者很少涉及的事前逆向选择、事后道德风险进行分析建模,设计最优的合约。对改进现实情况提出建议。第五章,介绍复杂适应系统的形成和特性。本章内容包括了作者撰写的另一篇论文《基于复杂适应系统的经济建模仿真方法》,对复杂银行信贷系统进行理论阐述,比较计算机模型与博弈论数学模型的异同,提出基于Agent的计算机建模思想。第六章,介绍SWARM软件平台及其运行原理。第七章,在SWARM平台上构建基于Agent的银行信贷市场生成模型,对系统中的微观经济主体的行为规则、含义、结构特征进行定义和形式化,进行模拟仿真,并对仿真结果进行分析。第八章,是对整个论文的回顾和展望。
The mathematical model and the computer model are illustrated firstly in this thesis. The two different modeling methods are described in detail separately. A real case about a model based on the mathematics equation is constructed. In the real case, superposition solution is got and this manifests the characteristic and practice of the mathematic model. Secondly, the production process of the classic computer model in complex adaptive module is also described. The characteristics of the mathematical model differs from the computer model are in summarized.
Regarding the strict logical and the analysis method of the game theory as an effective tool to solve the asymmetrical information problems in economy. With the asymmetrical information game methods, the decision and the restriction factors of China lending market of commercial bank are analysis in general. Combined with the real practice problems, the mathematic model about Adverse selection in advance and moral hazard afterward in lending market of China commercial bank is set up. The feasible suggestions how to improve the China commercial lending market are pointed out according to this model.
By using the game mathematic model, the asymmetrical information problems in the lending market of commercial bank builds up a model. However the traditional game model is set up based on the completely rational agents, which can forecast the result cased by their own behaviors (including the others responses). In another words, the utility function of the agents in the game are the definite function on the equilibrium and unequilibrium way. The unexpected dynamic factor are always to be regard as the agents which are make mistakes or called " small probability" caused by the indefinite accident. However, a lot of factors can cause the real lending market lose its equilibrium. Even through the game theory is better to deal with the asymmetrical information and mechanism design in economic system, it still have to face a serious issue,. That is the complete rational agent regarded as the basic supposition. Currently, the agents participated in the game are all believed the others in the game will do the optimistic choices and each of them have the knowledge about the game. Actually the same model is utilized in the cooperative game and the uncooperative game.
With a definite utility function and the complete rational supposition, The game model is still included in the category of the mathematical model. The game model actually changes into mathematics equation solution. However, the agent participated in the game are limited rationality in the lending market of the commercial bank. The limited rationality means each side participated in the game make the strategy based on the adjusted result during the studying process not the selective result. Moreover, the equilibrium will be broken again because of the deviation. Therefore, the game analysis method and equilibrium concept based on the complete rational supposition is limited to used to analysis the game issues with the limited rational.
In view of the game agent with the limited rationality, we introduce the concept "complex adaptive system" to illustrate. Firstly, we should review the economic system. The bank financial system is not a closed, machined and simple linear system. It is open, compatible and complex nonlinear system. Such huge and complex system can not only use the mathematical model to analyze its complex relationship. A microscopic economic agent has been built in this thesis. Utilizing the SWARM software platform to simulate the bank lending market and putting the game agents imitate the life movement in such environment, we carry the bank organization's emergence in the simulated system. By improving the model, the new nature are be added in the microscopic economic agent. The new nature can improve the intelligence of the agents and it can help to ponder the identity between the intelligent agent and the object world in a more comprehensive aspects. According to the simulative results, the improved agent intelligence can be regard as the root to cause the bank organization emergence. At the same time, the continual readjusted limited rationality and the asymmetrical information in the microscopic economic agent can make the static equilibrium in the system to be more mature and stable. This phenomenon make us have a more profound understanding to the real bank lending market. There is no definite utility function in such kind of the issues. The agents in the system are the agents with the limited rationality and adaptive. To solve such kind of question must use the computer program to define " the computer model" instead of the " mathematical model".
Combined with the international economy theory front, a asymmetrical information game mathematical model about the lending market of the commercial bank is established. The model analysis the mechanism design which can be used to solve the issues systematical. And in view of the difficulties in the model set and analysis which meet in the game theory, a direct computer model is established based on the microscopic economic agent. With the specific software platform, the game environment has been built and this make the game results more reliable, effective , direct and more practical.
The whole thesis is divided into eight chapters. The introduction proposed the significance to do the thesis. In this chapter, general domestic and foreign related researches are brief illustrated. The second chapter, the mathematical model is elaborated briefly. Taking a paper "one kind of the unconventional systemic simulation scheme" as a real case, main features of the "mathematic model" are elaborated. As the game mathematic model, the author will illustrate in the chapter 4. In the third chapter, the importance of the computer model is described. The main computer model and the modeling methods in complex adaptive system and the emergent theory are also been analyzed. The key features of the compute model are summarized in this chapter. In the chapter four, the author analyzes the real existed risks of the lending market of China commercial bank. By using the asymmetrical information game, A optimal contract is designed and the analyzed model is built by the adverse selection in advance and moral hazard afterward, which is very little involved in the analysis of the domestic scholar. A serious suggestions to improve the real situation in the real world are proposed in this chapter. In the firth chapter, the author introduces complex adaptive system's formation and characteristics. Another article "the CAS-based economic simulation mothod" illustrates the complex lending market in theory. This article compare the difference between the computer model an the game mathematical model and proposes a computer model based on the agent. The sixth chapter, the SWARM software platform and the operation principle are introduced. In the seventh chapter, the model about lending market based on the agent is build up on the SWARM platform. This model defines and formalizes the behavior rule , the meaning, the structural features of the microscopic economic agent in the system. At the same time, the model carries on the simulation. In the Eight chapter, the author review the whole thesis.
引文
[DING02]丁永健.经济模型的发展与比较.经济师.No.1,pp40-41.2002
[MA99]马光伟等.Agent思维状态模型.软件学报.V10(4),pp.342-348.1999
[WANG82]王仁,黄文彬著.塑性力学引论.北京:北京大学出版社.1982
[WANG87]王贵友.从混沌到有序.武汉:湖北人民出版社.198
[WANG01]王雪霞.SWARM经济仿真:基于代理人的模型和面向对象的程序设计.数量经济技术经济研究.NO.5,pp.63-64.2001
[FANG98]方福康.复杂经济系统的演化分析.北京:21实际100个科学难题.1998
[MA099]毛新军,侯义斌.Agent的概念模型及其应用技术.计算机研究与发展.V36(11),pp.1310-1316.1999
[FANG05]方美琪,张树人.复杂系统建模与仿真.北京:中国人民大学出版社.2005
[MA006]毛锦等.基于信息不对称的银行贷款合约分析与设计.金融研究.NO.10,pp.127-132.2006
[TIAN07]田丰等.一类“非常规”系统的仿真方法.V19(23),pp.5500-5506.2007
[TIAN08]田丰等.基于复杂适应系统的经济建模仿真方法.计算机仿真.NO.12.2008.
[XU00]许国志等.系统科学.上海:上海科技教育出版社.2000
[SUN02]孙建,叶民强.基于主体的SWARM建模分析及其应用.福建电脑.NO.11,pp.26-27.2002
[LI85]李坚石等.关于低压电器开关的CAD.计算结构力学及其应用.No.1,pp.61-69.1985
[L193]李后平等.分形理论的哲学发轫.成都:四川大学出版社.1993
[L195]李坚石.电接触热过程有限元模型在精密机电元件的应用.高校应用数学学报.No.10,pp.113-123.1995
[L196]李坚石.微动开关静、动过程与电接触热过程综合模型及模拟.计算物理,V13,No.4,pp.433-438.1996
[ZHANG96]张维迎.关于信息经济学.光明日报.10-17,1996
[ZHANG96]张维迎.搏弈论与信息经济学.上海:上海人民出版社.1996
[SONG97]宋立 现代西方规制理论及其演进.经济学动态.No.9,pp.59-63.1997
[WANG01]汪浩瀚.论主流经济理论演进的若干新特征.经济评论.No.1,pp.93-96.2001
[L101]李群,宣慧玉.基于Agent仿真技术在经济建模中的应用.系统工程理论方法应用.V10(3),pp.221-225.2001
[SONG04]宋学锋等.复杂性科学研究进展-全国第一、二届复杂性科学学术研讨会论文集.北京:科学出版社.2004
[L105]李红权,马超群.股市收益率与波动性长期记忆效应的理论与实证研究.财经研究.NO.6,pp29-37.2005
[L105]李红权,马超群.中国证券市场的混沌动力学特征研究.中国管理科学.NO.13,pp.194-200.2005
[L106]李红权.金融市场的复杂性与金融风险管理.财经科学.No.10,pp.9-16.2006
[CHEN00]陈平等.经济混沌和演化经济学.北京:经济科学出版社.2000
[CHEN05]陈春明.我国信息产业发展对策研究.学术交流,No.5,pp.69-70.2005
[HU99]胡舜耕等.多Agent系统的理论、技术及其应用.计算机科学.V26(2),pp.20-24.1999
[GA000]高阳等.基于Markov对策的多Agent强化学习模型及算法研究.计算机研究与发展.V37(3),pp.257-263.2000
[GA001]高波等.思考型Agent的基本结构.华中科技大学学报.V29(2),pp.25-27.2001
[QIAN90]钱学森等.一个科学新领域-开放的复杂巨系统及其方法论.自然杂志.No.13,pp.3-10.1990
[A181]爱德华.威尔逊著,张圣坤等译.NONSAP,几何和材料非线性静动力分析.上海:上海交通大学,上海外贸计算中心.1981
[HUANG07]黄曼慧.我国国内对信息经济学的研究综述.现代情报.No.2,pp.12.2007
[LIA004]廖守亿,戴金海.复杂适应系统及基于Agent的建模与仿真方法.系统仿真学报.V16(1),pp.113-117.2004
[AHRW98]Ahrweiler,P.,and Gilbert.N.Computer Simulation in Science and Technology Studies.New York:Springer Verlag.1998
[AKER70]Akerlof,G.The Market for 'Lemons':Qualitative Uncertainty and Market Mechanism.Quarterly Journal of Economics.V89,pp.488-500.1970
[AUTH97]Authur,W.B.,and John Holland.Asset Pricing Under Endogenous Expectations in an Artifial Stock Market.The Economy as an Evolving Complex System Ⅱ.Addison-Wesley Press.1997
[AXEL97]Axelrod,R.Advancing the art of simulation in the social sciences.Simulating Social Phenomena.Berlin:Springer.1997
[BALCI94]Balci,O.Validation,verification,and testing techniques throughout the life cycle of a simulation study.V53,pp.121-173.1994
[BARON89]Baron,D.Design of Regulatory Mechanism and Institutions.Handbook of Industrial Organization.North-Holland Press.1989
[BENE97]Benedikt Stefansson.Swarm:An Object Oriented Simulation Platform Applied to Markets and Organizations,Evolutionary Programming Ⅵ,Lecture Notes in Computer Science,V1212.New York:Springer-Verlag.1997
[BESA87] Besanko, H. Competitive Equilibrium in the Credit Market under Asymmetric Information. Journal of Economic Theory. V42(1), pp. 167-182. 1987
[BEST87] Bester, H., and Helwig, M. Moral Hazard and Equilibrium Credit Rationing: An Overview of the Issues. Discussion Paper. Bonn. 1987
[BRIAN90] Brian, A. Positive Feedback in The Economy. Scientific American. V262, pp.92-99.1990
[CHEN88] Chen Ping. Empirical and Theoretical Evidence of Monetary Chaos. System Dynamics Review. V4, pp. 81-108. 1988
[DIAM83] Diamond, Dybvig. Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy. V32, pp. 239-243. 1983
[DIAM84] Diamond D. W. Financial intermediation and delegated monitoring. Review of Economic Studies. V51, pp. 393-414. 1984
[D0RAN97] Doran, J. E.. From computer simulation to artificial societies. Transactions of the Society for Computer Simulation International. No. 14, pp. 69-77. 1997
[D0RAN97] Doran, J. E. Foreknowledge in artificial societies. Simulating social phenomena. Berlin: Springer. 1997
[EPST96] Epstein, J, M., and Axtell, R. Growing Artificial Societies. Massachusetts: MIT press. 1996
[FRAN00] Francesco Luna and Benedikt Stefansson. Agent-Based Modelling and Object Oriented Programming. Kluwer Academic Pub. 2000
[FRAN02] Francesco Luna. Agent-Based Methods in Economics and Finance: Simulations in Swarm. Kluwer Academic Pub. 2002
[FUDEN90] Fudenberg, D, Holmstrom, B. Short-term Contracts and Long-term Agency Relationships. Journal of Economic. Theory. V51, pp. 1-31. 1990
[FUDEN90] Fudenberg, D., and Tirole, J. Moral Hazard and Renegotiation in Agency. Econometrica. V58, ppl279-1320. 1990
[FUDEN91] Fudenberg, Drew & Jean Tirole. Game Theary. MIT. 1991
[GIBB92] Gibbons, Robert. A Primer in Game Theory. London: Harvester wheatsheaf. 1992
[GILB96] Gilbert, G. N. Simulations as a research strategy. Social science microsimulation. Berlin: Springer. 1996
[G0LD89] Goldberg, D, E. Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley. 1989
[GROSS83] Grossman, S and 0. Hart. An Analysis of the Principal-Agent Problem. Econometrica. V51, pp. 7-45. 1983
[HEFF96] Heffernan, S. Modern Banking in Theory and in Practice. London: Jone Wiley and sons. 1996
[HUBER97] Hubmerman B A, Lukose R M, Social diemmas and Internet Congestion, Science, 1997 document]; available from http://www. santafe. edu/pro.jects/swarm
[NALE83] Nalebuff, B. J., and Stiglitz, J. E. Prizes and Incentives: Towards a General of Compensation and Competition. Bell Journal of Economics. V14, pp. 21-43.1983
[PERRONE] Perrone, A. SwarmJournal, available at http://www. dma. unive. it/～alex/swarm
[PETER94] Peters, E. Fractal market analysis: Applying Chaos Theory to Investment and Economics. New York: John Wiley&sons, Inc. 1994
[RASM94] Eric. Rasmusen. Game and Information: An Introduction to Game Theory. Cambridge: Blackwell Publisher. 1994
[RIOR87] Riordan, M. H., and Sappington, D. E. Information, Incentives, and the Organizational Mode. Quarterly Journal of Economics. V102(2), pp. 243-263. 1987
[RUBI83] Rubinstein, A., andYaari, M. E. Repeated Insurance Contract and Moral Hazard. Journal of Economic. V30, pp. 74-97. 1983
[SALAS92] Salas-Fumas, V. Relative Performance of Management: The Effects on Industrial Competition and Risk Sharing. V30, pp. 74-97. 1992
[SCHEI89] Scheinkman, J., and LeBaron, B. Nonlinear Dynamics and Stock Returns. Journal of Business. V62(3), pp. 311-337.1989
[SEN96] Sen, S., and Sekaran, M. Multi-agent Coordination with Learning Classifier System. Lecture Notes in Artificial Intelligence. New York: Spinger Verlag. V1042, pp.113-126.1996
[STEF97] Stefansson, B. SWARM: An Object Oriented Simulation Platform Applied to Markets and Organizations. Evolutionary ProgrammingVI. New York: Springer-Verlag. 1997
[STIGLITZ81] Stiglitz J.E., Weiss A. Credit rating in market with imperfect information. American Economic Review. V71, pp. 393-410. 1981
[TERNA98] Terna, P. Simulation Tools for Social Scientist: Building Agent Based Models with Swarm. Journal of Artificial Societies and Social Simulation. V1(2), pp. 86-93. 1998
[THAL87] Thaler, R, H. The January Effect. Journal of Economic Perspectives. V51(l), pp.197-201. 1987
[TIAN08] Feng Tian. Agent-based Modeling and Simulation of the Bank Lending Market. International Conference on Information Technology and Environmental System Science. 2008
[TVER92] Tversky, A., and Kahneman, D. Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty. V5, pp. 297-323. 1992
[WOOL95] Wooldrige, N., and Jennings, N, R. Intelligent Agent: Theory and Practice. The Knowledge Engineering Review. V10(2), pp.115-152. 1995
[XAVIER97] Xavier Freixas and Jean-Charles Rochet. Microeconomics of banking. MIT.1997
[YANG86] Yang Zhong hui and Li Jianshi. The CAD for the microswitch [C]//JC-CEMCA, NAGOTA JAPAN , pp. 239-249. JULY 15-18,1986
[YU92] Zong yuan YU, Jianshi Li, Wenpin Ni. A three stage Design for AK-type microswitch[C]//ICECM 92 Hangfhow, pp. 95-97. Nov. 11-14, 1992
[MA99]马光伟等.Agent思维状态模型.软件学报.V10(4),pp.342-348.1999
[WANG82]王仁,黄文彬著.塑性力学引论.北京:北京大学出版社.1982
[WANG87]王贵友.从混沌到有序.武汉:湖北人民出版社.198
[WANG01]王雪霞.SWARM经济仿真:基于代理人的模型和面向对象的程序设计.数量经济技术经济研究.NO.5,pp.63-64.2001
[FANG98]方福康.复杂经济系统的演化分析.北京:21实际100个科学难题.1998
[MA099]毛新军,侯义斌.Agent的概念模型及其应用技术.计算机研究与发展.V36(11),pp.1310-1316.1999
[FANG05]方美琪,张树人.复杂系统建模与仿真.北京:中国人民大学出版社.2005
[MA006]毛锦等.基于信息不对称的银行贷款合约分析与设计.金融研究.NO.10,pp.127-132.2006
[TIAN07]田丰等.一类“非常规”系统的仿真方法.V19(23),pp.5500-5506.2007
[TIAN08]田丰等.基于复杂适应系统的经济建模仿真方法.计算机仿真.NO.12.2008.
[XU00]许国志等.系统科学.上海:上海科技教育出版社.2000
[SUN02]孙建,叶民强.基于主体的SWARM建模分析及其应用.福建电脑.NO.11,pp.26-27.2002
[LI85]李坚石等.关于低压电器开关的CAD.计算结构力学及其应用.No.1,pp.61-69.1985
[L193]李后平等.分形理论的哲学发轫.成都:四川大学出版社.1993
[L195]李坚石.电接触热过程有限元模型在精密机电元件的应用.高校应用数学学报.No.10,pp.113-123.1995
[L196]李坚石.微动开关静、动过程与电接触热过程综合模型及模拟.计算物理,V13,No.4,pp.433-438.1996
[ZHANG96]张维迎.关于信息经济学.光明日报.10-17,1996
[ZHANG96]张维迎.搏弈论与信息经济学.上海:上海人民出版社.1996
[SONG97]宋立 现代西方规制理论及其演进.经济学动态.No.9,pp.59-63.1997
[WANG01]汪浩瀚.论主流经济理论演进的若干新特征.经济评论.No.1,pp.93-96.2001
[L101]李群,宣慧玉.基于Agent仿真技术在经济建模中的应用.系统工程理论方法应用.V10(3),pp.221-225.2001
[SONG04]宋学锋等.复杂性科学研究进展-全国第一、二届复杂性科学学术研讨会论文集.北京:科学出版社.2004
[L105]李红权,马超群.股市收益率与波动性长期记忆效应的理论与实证研究.财经研究.NO.6,pp29-37.2005
[L105]李红权,马超群.中国证券市场的混沌动力学特征研究.中国管理科学.NO.13,pp.194-200.2005
[L106]李红权.金融市场的复杂性与金融风险管理.财经科学.No.10,pp.9-16.2006
[CHEN00]陈平等.经济混沌和演化经济学.北京:经济科学出版社.2000
[CHEN05]陈春明.我国信息产业发展对策研究.学术交流,No.5,pp.69-70.2005
[HU99]胡舜耕等.多Agent系统的理论、技术及其应用.计算机科学.V26(2),pp.20-24.1999
[GA000]高阳等.基于Markov对策的多Agent强化学习模型及算法研究.计算机研究与发展.V37(3),pp.257-263.2000
[GA001]高波等.思考型Agent的基本结构.华中科技大学学报.V29(2),pp.25-27.2001
[QIAN90]钱学森等.一个科学新领域-开放的复杂巨系统及其方法论.自然杂志.No.13,pp.3-10.1990
[A181]爱德华.威尔逊著,张圣坤等译.NONSAP,几何和材料非线性静动力分析.上海:上海交通大学,上海外贸计算中心.1981
[HUANG07]黄曼慧.我国国内对信息经济学的研究综述.现代情报.No.2,pp.12.2007
[LIA004]廖守亿,戴金海.复杂适应系统及基于Agent的建模与仿真方法.系统仿真学报.V16(1),pp.113-117.2004
[AHRW98]Ahrweiler,P.,and Gilbert.N.Computer Simulation in Science and Technology Studies.New York:Springer Verlag.1998
[AKER70]Akerlof,G.The Market for 'Lemons':Qualitative Uncertainty and Market Mechanism.Quarterly Journal of Economics.V89,pp.488-500.1970
[AUTH97]Authur,W.B.,and John Holland.Asset Pricing Under Endogenous Expectations in an Artifial Stock Market.The Economy as an Evolving Complex System Ⅱ.Addison-Wesley Press.1997
[AXEL97]Axelrod,R.Advancing the art of simulation in the social sciences.Simulating Social Phenomena.Berlin:Springer.1997
[BALCI94]Balci,O.Validation,verification,and testing techniques throughout the life cycle of a simulation study.V53,pp.121-173.1994
[BARON89]Baron,D.Design of Regulatory Mechanism and Institutions.Handbook of Industrial Organization.North-Holland Press.1989
[BENE97]Benedikt Stefansson.Swarm:An Object Oriented Simulation Platform Applied to Markets and Organizations,Evolutionary Programming Ⅵ,Lecture Notes in Computer Science,V1212.New York:Springer-Verlag.1997
[BESA87] Besanko, H. Competitive Equilibrium in the Credit Market under Asymmetric Information. Journal of Economic Theory. V42(1), pp. 167-182. 1987
[BEST87] Bester, H., and Helwig, M. Moral Hazard and Equilibrium Credit Rationing: An Overview of the Issues. Discussion Paper. Bonn. 1987
[BRIAN90] Brian, A. Positive Feedback in The Economy. Scientific American. V262, pp.92-99.1990
[CHEN88] Chen Ping. Empirical and Theoretical Evidence of Monetary Chaos. System Dynamics Review. V4, pp. 81-108. 1988
[DIAM83] Diamond, Dybvig. Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy. V32, pp. 239-243. 1983
[DIAM84] Diamond D. W. Financial intermediation and delegated monitoring. Review of Economic Studies. V51, pp. 393-414. 1984
[D0RAN97] Doran, J. E.. From computer simulation to artificial societies. Transactions of the Society for Computer Simulation International. No. 14, pp. 69-77. 1997
[D0RAN97] Doran, J. E. Foreknowledge in artificial societies. Simulating social phenomena. Berlin: Springer. 1997
[EPST96] Epstein, J, M., and Axtell, R. Growing Artificial Societies. Massachusetts: MIT press. 1996
[FRAN00] Francesco Luna and Benedikt Stefansson. Agent-Based Modelling and Object Oriented Programming. Kluwer Academic Pub. 2000
[FRAN02] Francesco Luna. Agent-Based Methods in Economics and Finance: Simulations in Swarm. Kluwer Academic Pub. 2002
[FUDEN90] Fudenberg, D, Holmstrom, B. Short-term Contracts and Long-term Agency Relationships. Journal of Economic. Theory. V51, pp. 1-31. 1990
[FUDEN90] Fudenberg, D., and Tirole, J. Moral Hazard and Renegotiation in Agency. Econometrica. V58, ppl279-1320. 1990
[FUDEN91] Fudenberg, Drew & Jean Tirole. Game Theary. MIT. 1991
[GIBB92] Gibbons, Robert. A Primer in Game Theory. London: Harvester wheatsheaf. 1992
[GILB96] Gilbert, G. N. Simulations as a research strategy. Social science microsimulation. Berlin: Springer. 1996
[G0LD89] Goldberg, D, E. Genetic Algorithms in Search. Optimization and Machine Learning. Addison-Wesley. 1989
[GROSS83] Grossman, S and 0. Hart. An Analysis of the Principal-Agent Problem. Econometrica. V51, pp. 7-45. 1983
[HEFF96] Heffernan, S. Modern Banking in Theory and in Practice. London: Jone Wiley and sons. 1996
[HUBER97] Hubmerman B A, Lukose R M, Social diemmas and Internet Congestion, Science, 1997 document]; available from http://www. santafe. edu/pro.jects/swarm
[NALE83] Nalebuff, B. J., and Stiglitz, J. E. Prizes and Incentives: Towards a General of Compensation and Competition. Bell Journal of Economics. V14, pp. 21-43.1983
[PERRONE] Perrone, A. SwarmJournal, available at http://www. dma. unive. it/～alex/swarm
[PETER94] Peters, E. Fractal market analysis: Applying Chaos Theory to Investment and Economics. New York: John Wiley&sons, Inc. 1994
[RASM94] Eric. Rasmusen. Game and Information: An Introduction to Game Theory. Cambridge: Blackwell Publisher. 1994
[RIOR87] Riordan, M. H., and Sappington, D. E. Information, Incentives, and the Organizational Mode. Quarterly Journal of Economics. V102(2), pp. 243-263. 1987
[RUBI83] Rubinstein, A., andYaari, M. E. Repeated Insurance Contract and Moral Hazard. Journal of Economic. V30, pp. 74-97. 1983
[SALAS92] Salas-Fumas, V. Relative Performance of Management: The Effects on Industrial Competition and Risk Sharing. V30, pp. 74-97. 1992
[SCHEI89] Scheinkman, J., and LeBaron, B. Nonlinear Dynamics and Stock Returns. Journal of Business. V62(3), pp. 311-337.1989
[SEN96] Sen, S., and Sekaran, M. Multi-agent Coordination with Learning Classifier System. Lecture Notes in Artificial Intelligence. New York: Spinger Verlag. V1042, pp.113-126.1996
[STEF97] Stefansson, B. SWARM: An Object Oriented Simulation Platform Applied to Markets and Organizations. Evolutionary ProgrammingVI. New York: Springer-Verlag. 1997
[STIGLITZ81] Stiglitz J.E., Weiss A. Credit rating in market with imperfect information. American Economic Review. V71, pp. 393-410. 1981
[TERNA98] Terna, P. Simulation Tools for Social Scientist: Building Agent Based Models with Swarm. Journal of Artificial Societies and Social Simulation. V1(2), pp. 86-93. 1998
[THAL87] Thaler, R, H. The January Effect. Journal of Economic Perspectives. V51(l), pp.197-201. 1987
[TIAN08] Feng Tian. Agent-based Modeling and Simulation of the Bank Lending Market. International Conference on Information Technology and Environmental System Science. 2008
[TVER92] Tversky, A., and Kahneman, D. Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty. V5, pp. 297-323. 1992
[WOOL95] Wooldrige, N., and Jennings, N, R. Intelligent Agent: Theory and Practice. The Knowledge Engineering Review. V10(2), pp.115-152. 1995
[XAVIER97] Xavier Freixas and Jean-Charles Rochet. Microeconomics of banking. MIT.1997
[YANG86] Yang Zhong hui and Li Jianshi. The CAD for the microswitch [C]//JC-CEMCA, NAGOTA JAPAN , pp. 239-249. JULY 15-18,1986
[YU92] Zong yuan YU, Jianshi Li, Wenpin Ni. A three stage Design for AK-type microswitch[C]//ICECM 92 Hangfhow, pp. 95-97. Nov. 11-14, 1992