基于复杂网络理论的农村信贷系统建模及仿真研究
发布时间：2018-07-15 07:32
【摘要】：全面深入的认识农村信贷系统是科学进行农村金融改革的前提，而虽然目前学界已经在社会学、经济学等方面做了大量研究但对如何深入、系统、直观认识农村信贷体系这一社会经济复杂系统仍然缺乏更为有效的方法和思路。复杂网络作为一种有效的复杂系统研究方法在科技、经济、社会等方面都得到了广泛的运用，为解决这一问题提供了新的条件。 本文从农村借贷系统出发将农村借贷系统抽象为复杂网络的形式，首先从农户借贷产生的基本影响因素出发分析借贷网络的节点及边的特征，得到网络建模的基本建设；其次，运用R软件等统计建模工具对农户信贷系统进行复杂网络建模，并计算其基本的统计物理指标，得出农村借贷网络的复杂网络性质；最后，对农户主体的变化和借贷系统的风险进行假设分析了借贷网络对于主体改进的效力以及网络系统的稳定性。 文章得出的主要结论有： （1）通过计量分析得出影响农户借贷产生的核心因素为经济资本（收入）因素和社会资本因素，其中社会资本因素的影响明显大于经济资本因素。 （2）在引入社会资本损失和努力效用的前提下，利用博弈论的方法分析了农户借贷网络链接的产生机制，得出了民间借贷网络更有利于产生农户借贷的结论。 （3）在经济资本和社会资本两个因素的基本假设下从民间借贷网络和正规借贷渠道出发分析借贷网络模型的建立，通过R软件成功建立了基于农户链接机制分析的农户借贷网络基本模型。 （4）通过对农户借贷网络模型的统计物理分析得出了该网具有小世界和无标度特征，并进一步分析了网络的链接特征，此外通过对网络中低收入群体的覆盖情况分析，发现民间借贷网络可以有效促进低收入农户融资。 （5）通过假设经济增长和农民组织化两个方面的主体变化，得出两种因素的改善都会农户借贷网络得到改进。通过对不同网络组织类型的区分得出随机网络组织对农户借贷网络的效力影响更明显，同时针对性地开发社会网络会对借贷网络改进有良好效果。 （6）文章对农户民间借贷网络面临的点风险和面风险进行假设。得出遭遇面风险时，农户有差异化的阀值有利于系统风险的识别和准备。而面对点风险，借贷网络可能遭受的影响差异极大，网络对随机攻击具有鲁棒性，对针对大度节点的选择性攻击具有脆弱性。 本文的创新体现在以下三个方面： （1）本文将复杂网络的观点引入农村借贷系统的研究当中通过对影响系统的基本元素进行分析完成系统网络的拓扑建模，发现了农村信贷网络的形成机制及一系列网络的特征以及其所代表的经济、社会含义。 （2）本文在对农村借贷网络基本元素的计量和博弈研究中引入了一些新的变量和考虑因素，完善了前人在此方面的分析。 （3）本文在信贷网络模型基础上，从政策改进效力和系统风险传播两个方面进行了分析，发现了不同假设下农村信贷系统的系统特征。
[Abstract]:The comprehensive and in - depth understanding of the rural credit system is the precondition for the reform of rural finance science . Although the current academic circle has done a lot of research in sociology , economics and so on , it still lacks more effective methods and thinking on how to deeply and systematize the social and economic complex system of rural credit system . Complex network has been widely used as an effective complex system research method in science , technology , economy , society and so on . It provides new conditions for solving this problem .
In this paper , the rural credit system is abstracted into the form of complex network from the rural credit system . Firstly , the characteristics of the nodes and edges of the lending network are analyzed from the basic influencing factors of the farmers ' borrowing , so as to get the basic construction of network modeling ;
Secondly , using the statistical modeling tools such as R software to model the complex network of the farmer credit system , and calculate the basic statistical physics index , the complex network character of the rural credit network is obtained ;
Finally , the paper analyzes the change of the main body of the farmer and the risk of the lending system , and analyzes the effect of the loan network on the improvement of the subject and the stability of the network system .
The main conclusions are as follows :
( 1 ) Through the measurement analysis , it is concluded that the core factors influencing the farmers ' borrowing are the economic capital ( income ) factor and the social capital factor , among which the influence of social capital factor is obviously bigger than the economic capital factor .
( 2 ) Under the premise of introducing social capital loss and effort utility , the paper analyzes the production mechanism of farmer ' s loan network link by using game theory , and obtains the conclusion that the private lending network is more beneficial to produce farmer ' s credit .
( 3 ) Based on the basic assumption of the two factors of economic capital and social capital , the establishment of the loan network model is analyzed from the private borrowing network and the formal borrowing channel , and the basic model of the farmer ' s loan network based on the analysis of the farmer ' s linkage mechanism is successfully established through the R software .
( 4 ) Through the statistical physical analysis of the farmer ' s lending network model , this paper concludes that the network has small world and no scale feature , further analyzes the link characteristics of the network , and finds that the private lending network can effectively promote the financing of low - income farmers by analyzing the coverage of low - income groups in the network .
( 5 ) By assuming the main changes in the two aspects of economic growth and farmer ' s organization , it is concluded that the improvement of the two factors will improve the peasant households ' lending network . By differentiating the types of different network organizations , it is clear that the effect of random network organization on the farmers ' lending network is more obvious , and the social network can be developed in a targeted way to improve the credit network improvement .
( 6 ) The paper makes a hypothesis on the point and face risks faced by peasant households ' private lending network . When confronted with the risk of face , the threshold value of farmer ' s differentiation is beneficial to the identification and preparation of systematic risk .
The innovation of this article is embodied in the following three aspects :
( 1 ) This paper introduces the idea of complex network into the research of rural credit system . Through analyzing the basic elements of the influencing system , the topology modeling of the system network is completed , the formation mechanism of the rural credit network and the characteristics of a series of networks and the economic and social implications of the network are found .
( 2 ) The paper introduces some new variables and considerations into the measurement and game research of the basic elements of rural lending network , and perfects the analysis of the former in this aspect .
( 3 ) Based on the credit network model , this paper analyzes two aspects of policy improvement effectiveness and systematic risk transmission , and finds out the system characteristics of rural credit system under different assumptions .
【学位授予单位】：西北农林科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：O157.5;F832.43
本文编号：2123328
[Abstract]:The comprehensive and in - depth understanding of the rural credit system is the precondition for the reform of rural finance science . Although the current academic circle has done a lot of research in sociology , economics and so on , it still lacks more effective methods and thinking on how to deeply and systematize the social and economic complex system of rural credit system . Complex network has been widely used as an effective complex system research method in science , technology , economy , society and so on . It provides new conditions for solving this problem .
In this paper , the rural credit system is abstracted into the form of complex network from the rural credit system . Firstly , the characteristics of the nodes and edges of the lending network are analyzed from the basic influencing factors of the farmers ' borrowing , so as to get the basic construction of network modeling ;
Secondly , using the statistical modeling tools such as R software to model the complex network of the farmer credit system , and calculate the basic statistical physics index , the complex network character of the rural credit network is obtained ;
Finally , the paper analyzes the change of the main body of the farmer and the risk of the lending system , and analyzes the effect of the loan network on the improvement of the subject and the stability of the network system .
The main conclusions are as follows :
( 1 ) Through the measurement analysis , it is concluded that the core factors influencing the farmers ' borrowing are the economic capital ( income ) factor and the social capital factor , among which the influence of social capital factor is obviously bigger than the economic capital factor .
( 2 ) Under the premise of introducing social capital loss and effort utility , the paper analyzes the production mechanism of farmer ' s loan network link by using game theory , and obtains the conclusion that the private lending network is more beneficial to produce farmer ' s credit .
( 3 ) Based on the basic assumption of the two factors of economic capital and social capital , the establishment of the loan network model is analyzed from the private borrowing network and the formal borrowing channel , and the basic model of the farmer ' s loan network based on the analysis of the farmer ' s linkage mechanism is successfully established through the R software .
( 4 ) Through the statistical physical analysis of the farmer ' s lending network model , this paper concludes that the network has small world and no scale feature , further analyzes the link characteristics of the network , and finds that the private lending network can effectively promote the financing of low - income farmers by analyzing the coverage of low - income groups in the network .
( 5 ) By assuming the main changes in the two aspects of economic growth and farmer ' s organization , it is concluded that the improvement of the two factors will improve the peasant households ' lending network . By differentiating the types of different network organizations , it is clear that the effect of random network organization on the farmers ' lending network is more obvious , and the social network can be developed in a targeted way to improve the credit network improvement .
( 6 ) The paper makes a hypothesis on the point and face risks faced by peasant households ' private lending network . When confronted with the risk of face , the threshold value of farmer ' s differentiation is beneficial to the identification and preparation of systematic risk .
The innovation of this article is embodied in the following three aspects :
( 1 ) This paper introduces the idea of complex network into the research of rural credit system . Through analyzing the basic elements of the influencing system , the topology modeling of the system network is completed , the formation mechanism of the rural credit network and the characteristics of a series of networks and the economic and social implications of the network are found .
( 2 ) The paper introduces some new variables and considerations into the measurement and game research of the basic elements of rural lending network , and perfects the analysis of the former in this aspect .
( 3 ) Based on the credit network model , this paper analyzes two aspects of policy improvement effectiveness and systematic risk transmission , and finds out the system characteristics of rural credit system under different assumptions .
【学位授予单位】：西北农林科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：O157.5;F832.43
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