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Modelling in political science: system dynamics approach. Версия для печати Отправить на e-mail
Воскресенье, 16 Август 2009
Шкапина Марина, факультет политологии, 3-й курс

Abstract.
В работе кратко освещаются особенности применения метода моделирования (прежде всего, формального) в политической науке, а также характеризуются задачи, которые необходимо решить с помощью данного метода при исследовании политической сферы, такие, как учёт динамического характера политических процессов и рассмотрение политики как системы для эффективного её анализа.
Как один из возможных способов решения поставленных задач рассматривается метод системно-динамического моделирования (SDM), довольно широко распространённый в западной науке, но не получивший пока широкого применения в российской политологии.
В работе в общем плане характеризуются ключевые положения и понятия данной методологии, такие, как запасы, потоки, обратная связь; приводятся возможности использования системно-динамического подхода при построении моделей из сферы политики.

Modelling in political science: system dynamics approach.

The use of models has come to dominate much of the scientific study of politics. We use models, mathematical or otherwise, to investigate and illuminate causal mechanisms, generate comparative statistics, and understand the conditions under which we expect certain outcomes to occur. Today, the emphasis is on using models to generate testable predictions that subsequently serve as hypotheses in data analysis. The method of modelling is connected with construction of artificial ideal objects, situations which represent elements and the relations that similar with elements and the relations in reality. Thus, the model is an image of object, the description of system, process. The purpose of modelling is studying the received model.

While researching socioeconomic and sociopolitical systems of a society, we can observe a lot of complexity in the internal organization of these systems and in the processes occurring inside of them. Provided that, various techniques of the political systems analysis (including formal) are presented in political science, though internal complexity of the organization, and also necessity of studying a set of parameters do not allow describing the phenomena that are observable empirically with desirable accuracy and reliability. To decide the issue we need the methodology which would consider political sphere as a system that means as a set of the interconnected elements, each of which carries out the certain function inside of system. The underlying relationships and connections between the components of a system are called the structure of the system. Also dynamic character of public systems should be taken into account that means the consideration of change of system over time that occurs owing to interaction of elements. It allows revealing the relationships of cause and effect inherent in system, also to track the basic tendencies of development of the whole system. The way in which the elements composing a system vary over time is referred to as the behaviour of the system. A system’s structure determines the system’s behaviour. System dynamics can be used to analyze how the structure of political system can lead to the behaviour that system presents. System dynamics can also be used to analyze how structural changes in one part of a system might affect the behaviour of the system as a whole. Perturbing a system allows one to test how the system will respond under varying sets of conditions.

Such problems with system analysis are difficult for performance without detailed studying a set of the variables reflecting properties of system. The way of the decision is offered with methods of computer modelling of a political reality. In particular, the application in this sphere was found with a method of system dynamics modelling (SDM).
System dynamics was created during the mid-1950s by Professor Jay Forrester of the Massachusetts Institute of Technology (MIT). One of his goals was to define how his knowledge in science and engineering could be brought to determine the success or failure of corporations. From calculations of the stock-flow-feedback structure of the plants [4] Forrester was able to show how the instability in corporation employment was due to the internal structure of the firm and not to an external force such as the business cycle. These simulations were the beginning of the field of system dynamics. During the late 1950s and early 1960s, Forrester and a team of his helpers moved the emerging field of system dynamics to the formal computer modeling stage. In his book “Urban Dynamics” [7] Forrester deals with how cities grow and then stagnate, and how national policies for dealing with cities often lie between neutral and highly detrimental, both for the city as an institution and also for the low income, unemployed residents.

System dynamics now is currently being used not only for understanding industrial or urban processes but throughout the public and private sector for policy analysis and design. SDM has been used for a wide range of purposes, such as to capture the dynamic relationship of energy and the economy, to model the world petroleum market, to explore dynamics of economic growth to analyze the environmental implications of international trade, to understand different kinds of management, to analyze different policies for nation-building, to model software development and so on. The basis of the method is the recognition that the structure of any system (the many circular, interlocking, sometimes time-delayed relationships among its components) is often just as important in determining its behaviour as the individual components themselves. It is also claimed that because there are often properties-of-the-whole which cannot be found among the properties-of-the-elements, in some cases the behavior of the whole cannot be explained in terms of the behavior of the parts. What makes using system dynamics different from other approaches to studying complex systems is the use of feedback loops and stocks and flows.

These elements help to describe how even seemingly simple systems display baffling nonlinearity.
Stocks hold the current state of the system: what you would see if you were to take a snapshot of the system. Stocks fully describe the condition of the system at any point in time. Stocks, furthermore, do not change instantaneously: they change gradually over a period of time. Stocks are accumulations. Flows do the changing of stocks. Flows increase or decrease stocks not just once, but every unit of time. All systems that change through the time can be represented by using only stocks and flows.

Feedback is the process through which a signal travels through a chain of causal relations to reaffect itself. Feedback can be divided into two categories: positive and negative. Feedback is positive if an increase in a variable, after a delay, leads to a further increase in the same variable. Positive feedback is found in compounding, reinforcing, or amplifying systems that produce exponential behavior. On the other hand, feedback is negative if an increase in a variable eventually leads to a decrease in the variable. Negative feedback drives balancing or stabilizing systems that produce asymptotic or oscillatory behavior. The feedback loops are not information links or material links but causal links. The connector which ties the reproduction rate to the current number of resources in stock is called an information link because it carries the information about the current state of the system (the value of the stock) to the mechanisms that drive the system to change (the flow). The feedback loops do not drive the system but simply describe the flow of causality. Feedback loops are the basic structural elements of systems. Feedback in systems causes nearly all dynamic behavior.

System dynamics models are not derived statistically from time-series data. Instead, they are statements about system structure and the policies that guide decisions. Models contain the assumptions being made about a system. A model is only as good as the expertise which lies behind its formulation. A good computer model, as prof. Forrester noticed [5; 7] , is distinguished from a poor one by the degree to which it captures the essence of a system that it represents. Many other kinds of mathematical models are limited because they will not accept the multiple-feedback-loop and nonlinear nature of real systems. System dynamics differs in two important ways from common practice in the social sciences and government. Other approaches assume that the major difficulty in understanding systems lies in shortage of information and data. Once data is collected, people have felt confident in interpreting the implications. But Forrester argues that statement. The system dynamics approach starts with concepts and information on which people are already acting. Generally, available information about system structure and decision-making policies is sufficient. Available information is assembled into a computer model that can show behavioural consequences of well-known parts of a system. Generally, behaviour is different from what people have assumed. Complex systems behave in ways entirely different from our expectations derived from experience with simple systems. Because intuition is based on simple systems, people are misled when making decisions about complex systems.

From simple systems we learn that cause and effect are closely related in time and space. But in complex systems the cause of a symptom is usually far back in time and arises from an entirely different part of the system. To make matters even more misleading, complex systems usually present what appear to be causes that are close in time and space to the immediate problem, but those apparent causes are only coincident symptoms. In a simple system, a goal can be accomplished and a task finished. However, in complex systems there is nearly always a tradeoff. If the short-term goal is maximized, the result is a longer-term undesirable consequence. The good example of such a process is excessive welfare programs in many countries relieved immediate social pressures but led to mounting governmental debt and severe political consequences as expenditures had to be curtailed [3]. In simple systems, it is clear what must be done to achieve a goal. But in complex systems, the obvious decisions are often ineffective. An exceedingly large fraction of policies in complex systems have very little effect. Nevertheless those low-leverage policies receive most of the attention in business and government. Experience from simple systems misguides people to take actions that the system itself can defeat [3] . In simple systems, the cause of a failure is clear. In simple systems, the source of a problem is evident and lies in our own actions. In complex systems, causes are hidden and blame can be attributed to scapegoats through which correction is not possible.

SDM recognizes the complex interactions among many feedback loops, rejects notions of linear cause-and-effect, and requires the analyst to view a complete system of relationships whereby the cause might also be affected by the effect. SDM enables analysts to uncover dynamics that is not seen by first view. Moreover, SDM allows the analyst an increased level of flexibility as SDM utilizes both conceptual understanding as well as empirical data collection.
Thus, SDM gives ample opportunities for modelling political sphere, in fact political systems are dynamic, contain feedback mechanisms and delays. Also political processes can be presented in terms of use of material, power and information resources (in the form of stocks and flows). Besides SDM's ability to include not quantitative variables is appreciable advantage at modelling political processes.

System-dynamic modelling can be applied in systems of decision-making as it allows analyzing the huge number of alternatives, strategies also to spend scenario calculations, as like as to investigate dynamics of social systems' development.

References

1.    Choucri N., Madnick S.E., Siegel M.D. Research initiative to understand and model state stability: exploting system dynamics// Composite Information Systems Laboratory (CISL) Sloan School of Management, 2005.
2.    Clarke K.A., Primo D.M. Modernizing political science: a model-based approach. Annual Meeting of the Midwest Political Science Association, Chicago, IL, April 2005.
3.    Coronado, Alan E. Beginner Modeling Exercises: Mental Simulation: Adding Constant Flows. MIT System Dynamics in Education Project, 2001.
4.    Forrester J. Counterintuitive behavior of social systems// Technology Review published by the Alumni Association of the Massachusetts Institute of Technology, 1971.
5.    Forrester, Jay W. Industrial Dynamics. Portland, OR: Productivity Press, 1961.
6.    Forrester, Jay W. System Dynamics and K-12 Teachers. A lecture at the University of Virginia School of Education, 1996.
7.    Forrester, Jay W. System dynamics, system thinking and soft OR// System Dynamics Review: Vol. 10, No. 2, 1994.
8.    Forrester, Jay. Urban Dynamics. Portland, OR: Productivity Press, 1969.
9.    Forrester, Jay W. World dynamics (2 ed.). Portland, OR: Productivity Press, 1973.
10.    Martin, Lesli A. An Introduction to Feedback. MIT System Dynamics in Education Project, 1997.
11.    Martin, Lesli A. Beginner Modeling Exercises. MIT System Dynamics in Education Project, 1997.
12.    Martin, Lesli A. The first step. MIT System Dynamics in Education Project, 1997.

 
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