Chapter 4: Standard Principles of Planning
4.6. Material Balance -part2- :Mathematical Models
The application of material balance standards in sustainable economic planning requires rigorous mathematics; getting it wrong could lead to a disruption in the supply and demand relationship in a planned economy, and if anything, supply to meet demand could stagnate, leading to chronic shortages. This is why precise mathematics is necessary for the material balance.
In this regard, the theory of linear programming has long been developed. This is a mathematical method that was researched and developed from the perspective of optimally allocating limited resources, mainly within the planned economy of the former Soviet Union, and the Soviet mathematical economist Leonid Kantorovich in particular was a pioneer in this field.
The theory of linear programming itself can also be applied to production and transportation plans for individual companies in a market economy, and even in the Western market economy, Dutch-American mathematical economist Tjalling Koopmans developed a method of activity analysis that determines the finite combinations of production factors required to produce a single product.
Kantorovich and Koopmans were jointly awarded the Nobel Prize in Economic Sciences in 1975 for their work in the East and West, respectively, and it was intriguing to see how planned economy theory and market economy theory intersected through linear programming.
These pioneering linear programming theories were the product of research at a time when environmental sustainability had not yet been incorporated into either planned or market economies, so further theoretical evolution will be required before they can be applied to a sustainable planned economy.
In this regard, linear programming, simply put, is a mathematical method for obtaining optimal target values within constraints described by first-order expressions, and therefore in a sustainable planned economy, it could be applied to obtaining optimal production target values within the constraints of environmental balance, which serves as the primary standard principle.
However, linear programming is just one type of mathematical programming in the broad sense that provides formulas for humans to use when rigorously mathematically converting plans into mathematics, and there are other mathematical programming methods such as nonlinear programming, which is the opposite of linear programming, and dynamic programming, which divides the optimization problem into multiple stages to prevent combinatorial explosion, and seeks a solution by successively increasing the number of stages.
It may be necessary to use dynamic programming, while still using linear programming as a basis, to derive the optimal solution within the constraints of environmental balance, which is difficult to predict and has multiple predictive scenarios. In any case, the application of such mathematical programming requires the use of supercomputers and artificial intelligence, which serve as the physical foundation.
In this respect, it has been pointed out that in the former Soviet Union's planned economy, inadequate computerization was a technical obstacle to rigorous planning, but this may be due to the fact that the former Soviet Union's planned economy was based on a monetized economy and state initiative, and the state had limited capacity to invest in advanced computerization. The Soviet Union's planned economy was based on a monetary economy and state initiative.
In contrast, since a sustainable planned economy does not essentially presuppose a monetized economy, monetary investment is not necessary - it is not even an issue in the first place. Under a monetized economy, whether it be a nation or a company, the use of advanced computerization and artificial intelligence would require huge investment, but under a sustainable planned economy it is by no means difficult.
👉The papers published on this blog are meant to expand upon my On Communism.
