Looking into the future of complex dynamic systems

Baggio, Rodolfo (2015): Looking into the future of complex dynamic systems.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: forecasting, predictability, complex systems, nonlinear analysis, time series
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C00 - General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C65 - Miscellaneous Mathematical Tools; L - Industrial Organization > L8 - Industry Studies: Services > L83 - Sports ; Gambling ; Restaurants ; Recreation ; Tourism

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