Tossicodipendenze, psicoanalisi e complessitŕ: la dissociazione farmaco-indotta

2009 ◽  
pp. 509-532
Author(s):  
Fabio Beni
Keyword(s):  
Nonlinear Dynamics ◽  
Drug Addiction ◽  
Complexity Theory ◽  
Driving Force ◽  
Present Situation ◽  
Self Organization ◽  
Dissociative Mechanism ◽  
Psychoanalytic Therapy ◽  
Interpersonal Psychoanalysis

- Drug addictions are understood, within a perspective of interpersonal psychoanalysis, taking into consideration dissociative mechanisms within a framework inspired by nonlinear dynamics theories. Considering the present situation in which psychoanalytic therapy is almost excluded from the treatment of drug addictions, in an attempt of resuming a dialogue with psychoanalysis it is assumed that drug addiction are the driving force of a particular dissociative mechanism. The perturbation connected in a nonlinear way to the effect of the drug originates and preserves a dissociative process, depicted through the concept of self-organization, an idea adopted from complexity theory. Drug addiction would therefore be especially sensitive to those psychotherapeutic approaches, such as interpersonal psychoanalysis, that emphasize the concept of dissociation.

New Approach for Researching Forest Ecosystem

2012 ◽  
Vol 472-475 ◽  
pp. 3384-3389
Author(s):  
Zai Qiang Huo ◽  
Xue Qun Zhu
Keyword(s):  
Complex System ◽  
Forest Ecosystem ◽  
Driving Force ◽  
Structure And Function ◽  
The Self ◽  
Self Organization ◽  
New Approach ◽  
Edge Of Chaos ◽  
And Function ◽  
Research Frontiers

It is valuable to be researched in the application of science of complexity to the forest ecosystem. Forest ecosystem is an adaptive complex system which is suggested to be at the edge of chaos or at the criticality. The inner interaction of a forest ecosystem is the main driving force for the self-organization, complexity and order in the forest ecosystem. Forest ecosystem complexity is one of the research frontiers of ecological and evolutionary problems presently. The application of science of complexity to the forest ecosystem complexity studies, its concept, background, methodology and theory are briefly introduced. The forest ecosystem complexity is defined as the structure and function diversity, self-organization and the order of an ecosystem. Its main methods include the cellular automaton, genetic algorithm, game theory, complex network, etc. This paper has discussed mechanism and development of forest ecosystem complexity, by applying the principle and methods of science of complexity, which is a new approach for understanding ecological and evolutionary problems.

scholarly journals Chaos, Complexity, and Contingency Theories: A Comparative Analysis and Application to the 21st Century Organization

2020 ◽  
Vol 9 (1) ◽  
pp. 44
Author(s):  
Franklin M. Lartey
Keyword(s):  
Complexity Theory ◽  
21St Century ◽  
Chaos Theory ◽  
Self Organization ◽  
Organizational Leaders ◽  
Unexpected Events ◽  
Regulatory Changes ◽  
Starting Point ◽  
Key Concepts ◽  
Organizational Purpose

Organizations in the 21st century deal with constant changes such as globalization, technological evolutions, regulatory changes, competition, and other unexpected events, among others. These challenges can be viewed and addressed through the lenses of contemporary theories. This paper selected three contemporary theories namely chaos, complexity, and contingency theories, and presented their foundations and characteristics by comparing and contrasting their key concepts. These concepts include nonlinearity, feedback, bifurcation, strange attractors, fractals, and self-organization for chaos theory; nonlinearity, dynamism, feedback, self-organization, emergence, and adaptability for complexity theory; and adaptation, equifinality, effectiveness, and congruency for contingency theory. Examples of studies and organizational applications of these theories were provided, and implications for scholars and organizational leaders were discussed. By explaining notions such as how the capacity of a system could be greater than the sum of the capacities of its subunits, this paper can act as a starting point for anyone seeking to understand the three theories or use them for research or organizational purpose.

Anisotropic Effect and the Crystals Nature on the Elastic Fields Dispersal Generated in a Three Layers Material

2009 ◽  
Vol 83-86 ◽  
pp. 1069-1075
Author(s):  
Mourad Brioua ◽  
Rachid Benbouta ◽  
Kamel Zidani
Keyword(s):  
Strain Field ◽  
Driving Force ◽  
Linear Equations ◽  
Anisotropic Elasticity ◽  
Semiconductor Nanostructures ◽  
Self Organization ◽  
Burgers Vector ◽  
Elastic Fields ◽  
Numerical Resolution ◽  
Layer Composite

Semiconductor nanostructures are interesting objects for many microelectronic and optoelectronic applications. Nevertheless, to use them, it is necessary to control their size, their density and their spatial distribution. In the last decade, many researches have been done to control these parameters. One of these researches is the elaboration of a functional substrate inducing a lateral self-organization of nanostructures. The organization driving force is the strain field induced on the surface by a buried dislocations network. The purpose of this work is the numerical resolution, in the case of anisotropic elasticity, of the problem of a misfit dislocation located between an infinite substrate and two-layer composite. The elastic fields of stresses are calculated for various orientations of the Burgers vector, by inversion of 30x30 arrays of linear equations. The composite NiSi2/Si / (001) GaAs, that made the object of several investigations, is treated like example.

Sources of nonlinearity and complexity in geomorphic systems

2003 ◽  
Vol 27 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Jonathan D. Phillips
Keyword(s):  
Nonlinear Dynamics ◽  
Landscape Management ◽  
Surface Processes ◽  
Self Organization ◽  
Complex Behavior ◽  
Historical Evidence ◽  
Nonlinear Complexity ◽  
Storage Effects ◽  
Analytical Tools ◽  
Geomorphic Processes

Nonlinearity is common in geomorphology, though not present or relevant in every geomorphic problem. It is often ignored, sometimes to the detriment of understanding surface processes and landforms. Nonlinearity opens up possibilities for complex behavior that are not possible in linear systems, though not all nonlinear systems are complex. Complex nonlinear dynamics have been documented in a number of geomorphic systems, thus nonlinear complexity is a characteristic of real-world landscapes, not just models. In at least some cases complex nonlinear dynamics can be directly linked to specific geomorphic processes and controls. Nonlinear complexities pose obstacles for some aspects of prediction in geomorphology, but provide opportunities and tools to enhance predictability in other respects. Methods and theories based on or grounded in complex nonlinear dynamics are useful to geomorphologists. These nonlinear frameworks can explain some phenomena not otherwise explained, provide better or more appropriate analytical tools, improve the interpretation of historical evidence and usefully inform modeling, experimental design, landscape management and environmental policy. It is also clear that no nonlinear formalism (and, as of yet, no other formalism) provides a universal meta-explanation for geomorphology. The sources of nonlinearity in geomorphic systems largely represent well-known geomorphic processes, controls and relationships that can be readily observed. A typology is presented, including thresholds, storage effects, saturation and depletion, self-reinforcing feedback, self-limiting processes, competitive feedbacks, multiple modes of adjustment, self-organization and hysteresis.

scholarly journals Complexity Theory, Nonlinear Dynamics, and Change: Augmenting Systems Theory

10.18060/137 ◽  
2007 ◽  
Vol 8 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Ralph Woehle
Keyword(s):  
Nonlinear Dynamics ◽  
Social Work ◽  
Difference Equation ◽  
Systems Theory ◽  
Complexity Theory ◽  
Change Processes ◽  
Edge Of Chaos ◽  
Good Measurement ◽  
Logistic Difference Equation ◽  
Over Time

Social work change processes are addressed in terms of complexity theory and nonlinear dynamics, adding the edge-of-chaos, as well as chaos to the entropy and homeostasis of ecosystems theory. Complexity theory sees the edge-of-chaos as valuable to living systems.A logistic difference equation is utilized to model the nonlinear dynamics of the hypothetical contentment of an individual. The modeling suggests that substantial input would be required to move an individual from homeostasis to the beneficial stage at the edge-of-chaos, but that too much input might result in chaos.With good measurement and data observed over time, social work might benefit from complexity theory and nonlinear dynamics, which are already advancing in related disciplines.

Chaos Theory and Complexity Theory

2013 ◽  
Author(s):  
Keith Warren
Keyword(s):  
Nonlinear Dynamics ◽  
Dynamical Systems ◽  
Complex Systems ◽  
Systems Theory ◽  
Complexity Theory ◽  
Chaos Theory ◽  
Mathematical Framework ◽  
Nonlinear Interactions ◽  
Simple Systems ◽  
Over Time

Chaos theory and complexity theory, collectively known as nonlinear dynamics or dynamical systems theory, provide a mathematical framework for thinking about change over time. Chaos theory seeks an understanding of simple systems that may change in a sudden, unexpected, or irregular way. Complexity theory focuses on complex systems involving numerous interacting parts, which often give rise to unexpected order. The framework that encompasses both theories is one of nonlinear interactions between variables that give rise to outcomes that are not easily predictable. This entry provides a nonmathematical introduction, discussion of current research, and references for further reading.

scholarly journals Insights from the study of complex systems for the ecology and evolution of animal populations

2019 ◽  
Vol 66 (1) ◽  
pp. 1-14 ◽  
Author(s):  
David N Fisher ◽  
Jonathan N Pruitt
Keyword(s):  
Population Dynamics ◽  
Complex Systems ◽  
Complexity Theory ◽  
Complexity Science ◽  
Self Organization ◽  
Emergent Properties ◽  
Ecological Data ◽  
Animal Populations ◽  
Key Characteristics ◽  
And Behavior

Abstract Populations of animals comprise many individuals, interacting in multiple contexts, and displaying heterogeneous behaviors. The interactions among individuals can often create population dynamics that are fundamentally deterministic yet display unpredictable dynamics. Animal populations can, therefore, be thought of as complex systems. Complex systems display properties such as nonlinearity and uncertainty and show emergent properties that cannot be explained by a simple sum of the interacting components. Any system where entities compete, cooperate, or interfere with one another may possess such qualities, making animal populations similar on many levels to complex systems. Some fields are already embracing elements of complexity to help understand the dynamics of animal populations, but a wider application of complexity science in ecology and evolution has not occurred. We review here how approaches from complexity science could be applied to the study of the interactions and behavior of individuals within animal populations and highlight how this way of thinking can enhance our understanding of population dynamics in animals. We focus on 8 key characteristics of complex systems: hierarchy, heterogeneity, self-organization, openness, adaptation, memory, nonlinearity, and uncertainty. For each topic we discuss how concepts from complexity theory are applicable in animal populations and emphasize the unique insights they provide. We finish by outlining outstanding questions or predictions to be evaluated using behavioral and ecological data. Our goal throughout this article is to familiarize animal ecologists with the basics of each of these concepts and highlight the new perspectives that they could bring to variety of subfields.

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