Fuzzy cognitive model of agricultural economic growth

Abstract

Agrarian growth is becoming increasingly important to many countries as the global demand for food rises, natural resources become scarcer, and environmental problems deepen. Herein, I propose a mechanism for designing agricultural growth management strategies that is based on fuzzy cognitive logic. The research presented is built on three main findings. First, it integrates established theories of economic growth, economic cyclicality, and sectoral market theories into a model of agricultural growth management. This enables the identification of main growth factors and the determination of the nature of their effects on agricultural dynamics. Second, I develop an algorithm for cognitive analysis of agricultural growth management and justify both this mathematical apparatus and the tools it uses. And third, I conduct a computational experiment that applies cognitive technologies to generate what I believe is the best agricultural economic growth strategy for Russia.

KEYWORDS:

• Agriculture
• economic growth
• cognitive modeling
• fuzzy cognitive map
• agricultural growth strategy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Technological orders (waves of innovation, techno-economic paradigm) are groups of aggregates of technologically related industries allocated in the structure of the economy, connected to each other by the same type of technological ‘chains’ and forming reproducing integrity. There are six technological structures: I – the beginning of the first industrial revolution; II – the era of steam; III – the era of steel; IV – the era of oil; V – the era of computers and telecommunications; VI – the era of nanotechnology. The leading researchers of this issue are Glazyev (1993) and Perez (2002).

2 The criteria are formulated by the author using FAO (2014, 2018b).

3 The design of the FCM and its analysis were carried out using the IGLA DSS software (2019).

4 From the set of formed basic alternatives, it is necessary to select nondominated ones (y_i), which are superior to any other (y_j) from this set according to the criteria for fulfilling the corresponding inequalities:

1. the difference between the final target concepts| v_Tk – v_Rki | ≤ | v_Tk – v_Rkj |, where the subscript T denotes the target value and subscript R denotes the result of the implementation of the alternative.

2. the force of control actions |y_ki| ≤ |y_kj|, where y_ki – control action on the k-th concept when implementing the alternative y_i, and y_kj is the control action on the k-th concept when implementing the alternative y_j.

5 Calculations of the initial state values of the concepts are presented in Appendix 1.