scholarly journals Narrativity in Complex Systems

2021 ◽  
pp. 31-50
Author(s):  
Hans U. Fuchs ◽  
Federico Corni ◽  
Elisabeth Dumont
Keyword(s):  
Dynamical Systems ◽  
Complex Systems ◽  
Systems Thinking ◽  
Dynamical Model ◽  
Complex Dynamical Systems ◽  
Natural Complex ◽  
Making Sense ◽  
Heuristic Strategy ◽  
Narrative Understanding

AbstractHumans use narrative for making sense of their environment. In this chapter we ask if, and if so how and to what extent, our narrative mind can help us deal scientifically with complexity. In order to answer this question, and to show what this means for education, we discuss fundamental aspects of narrative understanding of dynamical systems by working on a concrete story. These aspects involve perception of complex systems, experientiality of narrative, decomposition of systems into mechanisms, perception of forces of nature in mechanisms, and the relation of story-worlds to modelling-worlds, particularly in so-called ephemeral mechanisms. In parallel to describing fundamental issues, we develop a practical heuristic strategy for dealing with complex systems in five steps. (1) Systems thinking: Identify phenomena and foreground a system associated with these phenomena. (2) Mechanisms: Find and describe mechanisms responsible for these phenomena. (3) Forces of nature: Learn to perceive forces of nature as agents acting in these mechanisms. (4) Story-worlds and models: Learn how to use stories of forces (of nature) to construct story-worlds; translate the story-worlds into dynamical-model-worlds. (5) Ephemeral mechanisms for one-time, short-lived, unpredictable, and historical (natural) events: Learn how to create and accept ephemeral story-worlds and models. Ephemeral mechanisms and ephemeral story-worlds are a means for dealing with unpredictability inherent in complex dynamical systems. We argue that unpredictability does not fundamentally deny storytelling, modelling, explanation, and understanding of natural complex systems.

ADAPTABILITY AND SENSITIVITY OF COMPLEX SYSTEMS

2013 ◽  
Vol 23 (09) ◽  
pp. 1350163 ◽  
Author(s):  
ZHI-CHENG YE ◽  
QING-DUAN FAN ◽  
QIN-BIN HE ◽  
ZENG-RONG LIU
Keyword(s):  
Dynamical Systems ◽  
Complex Systems ◽  
Equilibrium Point ◽  
Stable Equilibrium ◽  
Equilibrium Points ◽  
Dynamical Behavior ◽  
Stable Equilibrium Point ◽  
Complex Dynamical Systems ◽  
Scientific Communities ◽  
Mathematical Definitions

Recently, the study on the dynamical behavior of complex dynamical systems has become a focal subject in the field of complexity. In particular, the system's adaptability and sensitivity have attracted increasing attention from various scientific communities. In this paper, we focus on some properties of complexity to gain a better understanding of it. Two descriptive mathematical definitions of attractors' adaptability and sensitivity are introduced from the viewpoint of dynamical systems. Then, these new descriptions are applied to analyze the adaptability and sensitivity of stable equilibrium points. In addition, a method is introduced for improving both the adaptability and sensitivity of a system with a stable equilibrium point.

scholarly journals Modular Interdependency in Complex Dynamical Systems

2005 ◽  
Vol 11 (4) ◽  
pp. 445-457 ◽  
Author(s):  
Richard A. Watson ◽  
Jordan B. Pollack
Keyword(s):  
Side Effects ◽  
Dynamical Systems ◽  
Complex Systems ◽  
Complex Dynamical Systems ◽  
Short Term ◽  
Dynamical Behaviors

Herbert A. Simon's characterization of modularity in dynamical systems describes subsystems as having dynamics that are approximately independent of those of other subsystems (in the short term). This fits with the general intuition that modules must, by definition, be approximately independent. In the evolution of complex systems, such modularity may enable subsystems to be modified and adapted independently of other subsystems, whereas in a nonmodular system, modifications to one part of the system may result in deleterious side effects elsewhere in the system. But this notion of modularity and its effect on evolvability is not well quantified and is rather simplistic. In particular, modularity need not imply that intermodule dependences are weak or unimportant. In dynamical systems this is acknowledged by Simon's suggestion that, in the long term, the dynamical behaviors of subsystems do interact with one another, albeit in an “aggregate” manner—but this kind of intermodule interaction is omitted in models of modularity for evolvability. In this brief discussion we seek to unify notions of modularity in dynamical systems with notions of how modularity affects evolvability. This leads to a quantifiable measure of modularity and a different understanding of its effect on evolvability.

scholarly journals Managing Complex Dynamical Systems

2011 ◽  
Vol 4 (3) ◽  
Author(s):  
John C. Cox ◽  
Robert L. Webster ◽  
Jeanie A. Curry ◽  
Kevin L. Hammond
Keyword(s):  
Dynamical Systems ◽  
Complex Systems ◽  
Data Sets ◽  
Complex Dynamical Systems ◽  
Managerial Action ◽  
Insight Into

Management commonly engages in a variety of research designed to provide insight into the motivation and relationships of individuals, departments, organizations, etc. This paper demonstrates how the application of concepts associated with the analysis of complex systems applied to such data sets can yield enhanced insights for managerial action.

Scaling

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Distribution Function ◽  
Dynamical Systems ◽  
Complex Systems ◽  
Power Law ◽  
Scaling Laws ◽  
Power Laws ◽  
Distribution Functions ◽  
Temporal Process ◽  
Approximate Power

Scaling appears practically everywhere in science; it basically quantifies how the properties or shapes of an object change with the scale of the object. Scaling laws are always associated with power laws. The scaling object can be a function, a structure, a physical law, or a distribution function that describes the statistics of a system or a temporal process. We focus on scaling laws that appear in the statistical description of stochastic complex systems, where scaling appears in the distribution functions of observable quantities of dynamical systems or processes. The distribution functions exhibit power laws, approximate power laws, or fat-tailed distributions. Understanding their origin and how power law exponents can be related to the particular nature of a system, is one of the aims of the book.We comment on fitting power laws.

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