In the footsteps of originators such as the 1969 Nobel Prize laureates in Economic Sciences Ragnar Frisch and Jan Tinbergen, a new chapter is published by pointing to the golden ratio as a solution for dynamic stability in economic cycles.
In his paper entitled “Non-Resonating Cycles in a Dynamic Model for Investment Behavior”, Bert de Groot, Professor of Governance and Strategic Investments at Erasmus School of Economics, describes the cyclical dynamics between the total amount invested within an economy and the proportion of malinvestments and its effects on the economy.
Frisch and Tinbergen had a mission; if they would understand economic cycles better, explain the existence of economic cycles and describe their characteristics, they would be able to provide better advice to governments and policy makers, to create positive social impact. Their early insights are augmented and now there is a mathematical theoretical model and empirical evidence to support that.
The model of Bert De Groot and fellow researchers Rene Segers en David Prins is inspired by the Austrian Business Cycle Theory (ABCT), which argues that investment behaviour and the issue of credit are at the root of economic cycles. The model is quasi-periodic and satisfies the conditions of Kolmogorov-Arnold-Moser (KAM) theory. KAM theory states that if a system is subjected to weak non-lineair perturbation, non-resonant cycles survive, remaining quasi-periodic. Resonant cycles, however, become unstable and will not last. In the context of their model, Bert de Groot and fellow researchers Segers and Prins conclude that for the cyclical motions to remain stable in the long run, the ratios of their cycle lengths should be sufficiently irrational. The golden ratio has that property. Coupling this with recent empirical findings, the researchers conclude that the detected cycles therein are indeed non-resonant and can be expected to remain stable.
- More information
Groot E.A. de, R. Segers, D. Prins (2022), Non-resonating cycles in a dynamic model for investment behavior, Technological Forecasting and Social Change, Volume 177, April 2022, 121515, https://doi.org/10.1016/j.techfore.2022.12151
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