Abstract
This paper suggests the possibility of incorporating the methodology of fuzzy logic theory into Harrod’s economic growth model, a classic model of economic dynamics for studying the growth of a developing economy based on the assumption that an economy with only savings and investment income is in equilibrium when savings are equal to investment. This model was the first precursor to exogenous growth models, which in turn gave rise to endogenous growth models. This article therefore represents a first step towards introducing fuzzy logic into economic growth models. The study concerned considers consumption and savings to depend on income by means of uncertain factors, and investment to depend on the variation of income through the accelerator factor, which we consider uncertain. These conditions are used to determine the equilibrium growth rate of income and investment, as well as the uncertain values for these variables in terms of fuzzy numbers. As a result, the new model is shown to expand the classical model by incorporating uncertainty into its variables.
Highlights
As is well known, macroeconomics studies how economic systems work from an aggregate point of view as a result of the interactions that take place between different economic agents
The idea of considering the future in a pessimistic, optimistic and highly plausible scenario is the basis of these types of numbers that collect all values with some degree of possibility greater than zero. It is the ease of use of these mathematical structures [4,5,6,7] that allows uncertainty to be incorporated efficiently into any economic model regarding the behavior of economic, social and financial scenarios
We study the particular case for which e b are triangular fuzzy numbers a and e e (TFN)
Summary
Macroeconomics studies how economic systems work from an aggregate point of view as a result of the interactions that take place between different economic agents. The aim of this article is to present a complementary and novel point of view, introducing a fuzzy logic model in order to obtain results based on an infinity of possible inputs. This new modelling based on fuzzy logic [2]. The idea of considering the future in a pessimistic, optimistic and highly plausible scenario is the basis of these types of numbers that collect all values with some degree of possibility greater than zero It is the ease of use of these mathematical structures [4,5,6,7] that allows uncertainty to be incorporated efficiently into any economic model regarding the behavior of economic, social and financial scenarios.
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