Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

Systems, 4(4), 37, 2016, DOI: 10.3390/systems4040037

18 Pages Posted: 23 Aug 2016 Last revised: 16 Nov 2016

See all articles by Geoff Boeing

Geoff Boeing

Northeastern University - School of Public Policy and Urban Affairs; Northeastern University - Network Science Institute

Date Written: 2016

Abstract

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This paper has three aims. First it discusses several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second it introduces to an interdisciplinary audience the foundations of nonlinear dynamics, chaos, fractals, self-similarity, and the limits of prediction – arguing that information visualization is a key way to engage with these concepts. Finally it presents Pynamical, a tool to visualize and explore nonlinear dynamical systems' behavior.

Keywords: visualization, nonlinear systems, dynamics, chaos, fractal, attractor, bifurcation, cobweb plot, phase diagram

JEL Classification: A00

Suggested Citation

Boeing, Geoff, Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction (2016). Systems, 4(4), 37, 2016, DOI: 10.3390/systems4040037. Available at SSRN: https://ssrn.com/abstract=2823988 or http://dx.doi.org/10.2139/ssrn.2823988

Geoff Boeing (Contact Author)

Northeastern University - School of Public Policy and Urban Affairs ( email )

343 Holmes Hall
360 Huntington Avenue
Boston, MA 02115
United States

Northeastern University - Network Science Institute ( email )

177 Huntington Avenue
Boston, MA MA 02115
United States

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