Looking into the future of complex dynamic systems

Rodolfo Baggio

MPRA Paper from University Library of Munich, Germany

Abstract: The desire to know and foresee the future is naturally bound to human nature. Traditional forecasting methods have looked after reductionist linear approaches: variables and relationships are monitored in order to foresee future outcomes with simplified models and to derive theoretical and practical implications. The limitations of this attitude have become apparent in many cases, mainly when dealing with dynamic evolving complex systems, that encompass numerous factors and activities which are interdependent and whose relationships might be highly nonlinear, resulting in an inherent unpredictability of their long-term behavior. Complexity science ideas are important interdisciplinary research themes emerged in the last few decades that allow to tackle the issue, at least partially. This paper presents a brief overview of the complexity framework as a means to understand structures, characteristics, relationships, and explores the most important implications and contributions of the literature on the predictability of a complex system. The objective is to allow the reader to gain a deeper appreciation of this approach.

Keywords: forecasting; predictability; complex systems; nonlinear analysis; time series (search for similar items in EconPapers)
JEL-codes: C00 C65 L83 (search for similar items in EconPapers)
Date: 2015-06-30
New Economics Papers: this item is included in nep-for and nep-hme
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:65549

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