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Books by Grigory Yavlinsky
Economics and Politics in Russia
The Center for Economic and Political Research (EPIcenter)
Nizhni Novgorod-Moscow, 1992

3.2. Prognosis and Strategy

Problems in Constructing the Model

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The lack of economic self-regulation mechanisms, which were acceptable from the viewpoint of the human factor, inevitably engendered state regulation of the economy, together with attempts to enhance it. It transpired that the perfection of one or other method of management depends to a great extent on the ability to appraise the consequences of implemented decisions for sufficiently long periods of time and thereby predetermine the strategy for economic development.

Such appraisals require ideological rather than instrumental formulation, and are currently represented by the ideology of systems analysis and systems-dynamics modelling. The powerful influence of systems analysis as ideological orientation, especially of a practical nature, began to be manifested particularly strongly in economic research, following the consolidation of the principle of inter- relations between elements of a system via reciprocal ties. The concept of "reciprocal contacts" clarified the regularity of the development and functioning of the widest range of systems, including economic systems (econometrics had singularly failed to achieve this goal), and served as the foundation for the scientific discipline called systems dynamics, created as part of systems analysis. Without broaching the general theoretical aspect of systems dynamics, we will simply remark that systems-dynamics modelling comes down basically to the mathematical formalization of a whole complex of initial situations of systems dynamics, and enables one to directly obtain specific results, which are ready for application. The specificity in this case literally denotes the figures reflecting the future dynamics of all of the model 's parameters, in tabular or graphic form.


The graphs in figures 1 and 2 depict qualitatively distinct results of the modelling of two simple processes of regulation, which differ only in the numerical value of a single parameter.

Figure 1 Amplification coefficient equal to -35

Figure 2 Amplification coefficient equal to -36

These processes may be interpreted economically as well, but it is far more important to draw the conclusion that even very minor differences in systems with the most trivial algorithmic and structural organization may lead to behaviour, which is extemely varied qualitatively. This example illustrates the topicality of application of systems-dynamics modelling in everyday situations, from the position of general systems approaches. A control entity which ignores this factor is unlikely to be very efficient. Economic systems contain nothing new in their behavior. This can easily be demonstrated by building a model to imitate the functioning of the economy of Nizhni Novgorod oblast. For clarity's sake we will proceed from the simple to the complex and cite three scenarios of this model.

Scenario 1

Let us assume that the aggregate production of Nizhni Novgorod oblast is ruled by the following: - if demand exceeds supply (a deficit situation), a decrease in demand and an increase in supply will occur; - if supply exceeds demand (a case of surplus production), an increase in demand and a decrease in supply will occur; - if supply equals demand, no changes will occur.

According to the oblast's inter-regional balance sheet for 1987, the value of the aggregate output produced by the oblast was 10,133,873,300 roubles, while product totalled 6,861,277,700 roubles. The results of the modelling are depicted in figure 3.

Figure 3 Dynamics of supply and demand for the aggregate presents a classic, albie purely theoretical "utopia".

Scenario 2

Let us complicate the model. Let us assume that the economy of Nizhni Novgorod oblast may be represented by the functioning of the following production complexes:

1. Fuel and energy;

2. Metallurgy;

3. Machine building;

4. Chemicals and timber;

5. Building materials industry;

6. Light industry;

7. Food industry;

8. Other industrial sectors;

9. Agriculture;

10. Other sectors of material production.

The data on supply and demand were taken from the inter- regional balance sheet for 1987, and summarized in the following table.

Supply and Demand (1,000 roubles)

Complex ______________________________Supply___________________ Demand

Fuel and energy_______________________________163547.4 _________________1277158.3

Metallurgy____________________________________737840.4_________________ 1013485.8

Machine building ______________________________3578582.3_________________1147203.8

Chemicals and timber___________________________1532567.3________________ 1153447.8

Construction materials industry ___________________378508.9_________________ 128703.3

Light industry _________________________________1396836.0_________________ 765103.8

Food industry _________________________________1425128.3 _________________586028.1

Other industrial sectors__________________________200935.6 __________________70229.4

Agriculture ____________________________________678191.7 _________________678191.7

Other sectors of material production_______________ 41685.7___________________ 41685.7

The regulation mechanism of this variant differs from the logic of variant no 1. owing to the following addition: - a state of deficit or surplus production in one complex influences the supply and demand of all the complexes.

The results of the modelling are depicted in figure 4.

Figure 4 Dynamics of production

Figure 5 Dynamics of production

A single comment is appropriate here -- that this is a non- classic trend for a classic economy.

Scenario 3

Although scenario No 2 is too abstract for the elaboration of any practical recommendations on its basis, it is still worthwhile to study its behaviour by changing one (to simplify any interpretation) of the algorithms. Given that in the preceding scenario the output of the machine-building complex underwent the strongest change, let us maintain this output level as a constant.

The results of such an influence are depicted in figure 5.

Of course, one should not claim that the results of modelling will always provide significant revelations. Simple systems can also be analysed without dynamic modelling. However, any attempt to obtain significant practical results almost always requires more complicated systems, as it is unlikely that a real economy will depend on a simple system. In this case, the effectiveness of the dynamic modelling is incomparable with that of any existing alternatives.


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