Characterization of the Onset to Chaos in Economy

The economic history of several countries, including Brazil, demonstrates that no matter how much one plans, economic and social chaos can be present in their economies. This fact reveals that the old views on determinism, control and predictability of economic models are not confirmed in current times. The chaos and complexity of the business environment leave governments, companies, and people with the impression that they are being pulled into a vortex that goes beyond political, economic, and social activity. At all institutional levels, organization and complexity can occur by generating order out of disorder and information out of chaos. This complicated pattern of evolution can happen through the systems identified as self-organizing that are part of General Systems Theory.

Economica, 1993

This paper is the outcome o f a series o f lectures given during several visits to the European University Institute.

2016

ABSTRACT: The current global crisis has generated in economic and management theory numerous questions / dilemmas, because the magnitude of the crisis surprising the entire capitalist world. Juglar cycles, Kuznets cycles and Kondratieff cycles are well known in economic theory but their findings have been deliberately ignored by policy makers. The current theory discusses about the chaotics of the business environment, the instability and highly sinuous evolution of economic life. Regarding the study of cyclicity in business at the firm level, this subject has been and remains more complex by its very nature. From the perspective of our research, we aim to highlight the relation of interdependence between the macroeconomic cycles and business development at the company/firm level.

This article aims to present the causes of the chaos that has prevailed in the world economy for centuries and how to eliminate them. These causes are explained by the absence of feedback and control mechanisms in the world capitalist system that would allow mitigating the harmful consequences generated by long waves or long economic cycles of the world economy presented by Russian economist Nikolai Dimitrievitch Kondratieff who shows how the world capitalist system has evolved with cycles that go from prosperity to economic decline from 1780 to 2010, as well as mitigating the harmful consequences generated by waves of innovation presented by Austrian economist Joseph Schumpeter who shows how technological innovations introduced into production processes in each Kondratieff cycle are responsible for the expansion and decline of the world capitalist economy. To deal with the cyclical crises of capitalism and put an end to global economic chaos, as well as ensuring that technological advancement does not cease and contributes to the progress of humanity, the only solutions for stabilizing the global economy are the adoption of Keynesianism in each country and globally and the existence of a world government.

2010

The impact of increasing leverage in the economy produces hyperreaction of market participants to variations of their revenues. If the income of banks decreases, they mass-reduce their lendings; if corporations sales drop, and due to existing debt they cannot adjust their liquidities by further borrowings, then they must immediately reduce their expenses, lay off staff, and cancel investments. This hyperreaction produces a bifurcation mechanism, and eventually a strong dynamical instability in capital markets, commonly called systemic risk. In this article, we show that this instability can be monitored by measuring the highest eigenvalue of a matrix of elasticities. These elasticities measure the reaction of each sector of the economy to a drop in its revenues from another sector. This highest eigenvalue-the spectral radius-of the elasticity matrix, can be used as an early indicator of market instability and potential crisis. Grandmont (1985) and subsequent research showed the possibility that the "invisible hand" of markets become chaotic, opening the door to uncontrolled swings. Our contribution is to provide an actual way of measuring how close to chaos the market is. Estimating elasticities and actually generating the indicators of instability will be the topic of forthcoming research.

Journal of Monetary Economics, 1988

An empirical assessmez? of a linear-stochastic perspective for Canadian macroecono series is presented. The methods used are based on the mathematics of 'chaos'. Present evidence suggests that low-order deterministic chaos does not provide a satisfactory characterization of the data. The absence of significant nonlinear structure for the investment and unemployment series is of particular note in light of past findings with American data. The degree to which the use of a time trend can impose a pseudo-structure on the data is illhlstrated. *This research was partly supported by a grant from the Research Excellence Program of University of Guelph. We would like to thank William Brock, Roger Farmer, Chera Sayers, Jose Scheinkman for helpful discussions ab chaos. ne suggestions of an anonymous referee were quite useful. Our i~tellectu~ debt to acknowledged. Any deficiencies remain our responsibility.

Studies in Nonlinear Dynamics & Econometrics, 1996

The possibility of cycles and chaos arising from nonlinear dynamics in economics emerged in the literature in the 1980s, and it came as a surprise. 1 The possibility of deterministic cycles in economic models had been noted before, for example in the well-known multiplier-accelerator models, but not in equilibrium models with complete markets, no frictions, and full intertemporal arbitrage. 2 The reason for the surprise was understandable: deterministic fluctuations in equilibrium models involve predictable changes in relative prices which should be ruled out by intertemporal arbitrage. In models of overlapping generations, however, finite lives can restrict complete arbitrage over time. As a result, some people thought, and still think, that cycles that are shorter than the agents' postulated lifespans would not be possible in equilibrium models, and therefore are irrelevant for business-cycle analysis. This view is clearly wrong, and of course ignores the extensive literature on cycles and chaos in optimal growth models with infinitely lived agents. In such models deterministic cycles in relative prices occur easily, but the amplitudes of the cycles remain within bounds of the discount rate. 3 It is not difficult to show in the context of multisector growth models, say with Cobb-Douglas production functions, that for any positive discount rate there is a large class of technologies for which cycles occur. (See Benhabib and Rustichini [1990].) Getting chaos, however, is harder. Recent works by Sorger (1992), by Mitra (1995), and by Nishimura and Yano (1995) give lower bounds for the discount rate, below which chaos is ruled out for one-sector models of optimal growth. Yet even in that context, going to a multisector framework may considerably lower the bounds on the discount rate thus far established. A second reason for the attention that chaotic dynamics received in the economics literature regards prediction. The common wisdom has been that economic fluctuations are driven by exogenous shocks. Chaotic dynamics not only supplied an alternative explanation for at least some part of economic fluctuations, but also provided an excuse for economists' difficulties with forecasting. Sometimes, however, an important feature of chaotic dynamics that makes forecasting difficult, namely, sensitive dependence on initial conditions, is used in a cavalier way to explain short-run dynamics, forgetting that the effect of sensitive dependence becomes significant only after some periods, but not in the very short run. When it became obvious that very-standard equilibrium models could easily generate cycles and chaos, the attention in the literature naturally turned to the empirical plausibility of such dynamics. The most interesting approach, inspired by developments in natural sciences and mathematics, was also atheoretical, and reminiscent of VAR methods of time series. 4 The idea was to try to infer whether a particular economic time series was generated by a deterministic, low (at most four-or five-) dimensional system that was chaotic, or whether it came from a simple (linear) stochastic system. It is not difficult to see that such inferences are hard to make when the time-series data is short, as is the case with most economic series, with the exception of financial data. It is not surprising, then, that many applications of this approach are in the area of finance, but even there, where we have very high-frequency data, it is hard to pick up fluctuations that may occur at lower

We discuss some issues and challenges facing economic modellers when confronted with data generated within a non-linear world. The pitfalls associated with the linearization of inherently non-linear models are raised and the concept of robustness defined and proposed as a property of scientifically valid models. The existence of chaos in economic time series is discussed and an example, using financial data, presented.

Journal of Futures Studies, 2015

We utilised methods of chaos theory that were originally used in a 1990's study to analyse the behaviour of various Hungarian socio-economic macro indicators, both historically and their expected behaviour in the future. In this study, we present the method adapted to PC and the behaviour of the selected macro indicators. We characterize the pathways our society and economy has experienced and where they are heading to into the future by the means of these indicators. Comparing the present results of analysis with the results twenty years ago (when today's present was the future) we came to the conclusion that most of the indicators became less chaotic, thus the socio-economic courses were getting more stable over the past two decades. We conclude that the opportunity to change them is slowly diminishing, it will be more and more difficult to renew the Hungarian socio-economic indicators, and to turn the processes to more desirable courses. Recommendations for change interve...

2012

Complexity is one of the most important characteristic properties of the economic behaviour. The new field of knowledge called Chaotic Dynamic Economics born precisely with the objective of understanding, structuring and explaining in an endogenous way such complexity. In this paper, and after scanning the principal concepts and techniques of the chaos theory, we analyze, principally, the different areas of Economic Science from the point of view of complexity and chaos, the main and most recent researches, and the present situation about the results and possibilities of achieving an useful application of those techniques and concepts in our field.