scholarly journals Complexity In Psychological Self-Ratings: Implications for research and practice

2020 ◽  
Author(s):  
Merlijn Olthof ◽  
Fred Hasselman ◽  
Anna Lichtwarck-Aschoff
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Complex Dynamics ◽  
Initial Conditions ◽  
Regime Shifts ◽  
Fundamental Aspect ◽  
Time Varying ◽  
Term Prediction ◽  
Sensitive Dependence

Background: Psychopathology research is changing focus from group-based ‘disease models’ to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations, regime shifts, transitions between different dynamic regimes, and, sensitive dependence on initial conditions, also known as the ‘butterfly effect’, the divergence of initially similar trajectories.Methods: We analysed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis and the Sugihara-May algorithm.Results: Self-ratings concerning psychological states (e.g., the item ‘I feel down’) exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item ‘I am hungry’) exhibited less complex dynamics and their behaviour was more similar to random variables. Conclusions: Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are ‘moving targets’ whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process-monitoring, short-term prediction, and just-in-time interventions, are discussed.

scholarly journals Complexity in psychological self-ratings: implications for research and practice

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Merlijn Olthof ◽  
Fred Hasselman ◽  
Anna Lichtwarck-Aschoff
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Complex Dynamics ◽  
Initial Conditions ◽  
Regime Shifts ◽  
Fundamental Aspect ◽  
Time Varying ◽  
Term Prediction ◽  
Sensitive Dependence

Abstract Background Psychopathology research is changing focus from group-based “disease models” to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range temporal correlations; regime shifts, transitions between different dynamic regimes; and sensitive dependence on initial conditions, also known as the “butterfly effect,” the divergence of initially similar trajectories. Methods We analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm. Results Self-ratings concerning psychological states (e.g., the item “I feel down”) exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item “I am hungry”) exhibited less complex dynamics and their behavior was more similar to random variables. Conclusions Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are “moving targets” whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.

Visualising the invisible: performing chaos theory

Leonardo ◽  
2020 ◽  
pp. 1-8
Author(s):  
Emma Weitkamp
Keyword(s):  
Complex Systems ◽  
Chaos Theory ◽  
Initial Conditions ◽  
Starting Point ◽  
Weather And Climate ◽  
Butterfly Effect ◽  
Long Term Behavior ◽  
The Invisible

Edward Lorenz, the pioneering figure in the field of chaos theory coined the phrase “butterfly effect” and posed the famous question “Does the flap of a butterfly's wings in Brazil set off a tornado in Texas?” In posing the question, Lorenz sought to highlight the intrinsic difficulty of predicting the long term behavior of complex systems that are sensitive to initial conditions, like, for example, the weather and climate; these systems are often referred to as chaotic. Taking Lorenz' butterfly as a starting point, Chaos Cabaret sought to explore the nuances of chaos theory through performance and music.

scholarly journals The sleeping brain as a complex system

2011 ◽  
Vol 369 (1952) ◽  
pp. 3697-3707 ◽  
Author(s):  
Eckehard Olbrich ◽  
Peter Achermann ◽  
Thomas Wennekers
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Systems Approach ◽  
Complex Dynamics ◽  
Activity Patterns ◽  
Complexity Science ◽  
Sleep Stages ◽  
Sleep Regulation ◽  
Theme Issue ◽  
The Brain

‘Complexity science’ is a rapidly developing research direction with applications in a multitude of fields that study complex systems consisting of a number of nonlinear elements with interesting dynamics and mutual interactions. This Theme Issue ‘The complexity of sleep’ aims at fostering the application of complexity science to sleep research, because the brain in its different sleep stages adopts different global states that express distinct activity patterns in large and complex networks of neural circuits. This introduction discusses the contributions collected in the present Theme Issue. We highlight the potential and challenges of a complex systems approach to develop an understanding of the brain in general and the sleeping brain in particular. Basically, we focus on two topics: the complex networks approach to understand the changes in the functional connectivity of the brain during sleep, and the complex dynamics of sleep, including sleep regulation. We hope that this Theme Issue will stimulate and intensify the interdisciplinary communication to advance our understanding of the complex dynamics of the brain that underlies sleep and consciousness.

A Complex-Systems Approach to Organizations

2000 ◽  
Vol 9 (2) ◽  
pp. 69-74 ◽  
Author(s):  
Daniel J. Svyantek ◽  
Linda L. Brown
Keyword(s):  
Complex Systems ◽  
Organizational Behavior ◽  
Systems Approach ◽  
Initial Conditions ◽  
Nonlinear Behavior ◽  
System Behavior ◽  
Physical Sciences ◽  
Systems Research ◽  
Context Specific ◽  
Insight Into

The physical sciences have developed new theories of nonlinear behavior of complex systems. Defining characteristics of complex systems include (a) being composed of many variables that interact strongly to determine system behavior, (b) sensitivity to initial conditions, and (c) stability across time. Two complex-system concepts, phase spaces and attractors, provide insight into the evolution of system behavior and make prediction of future behavior possible. It is proposed that complex-systems research has application to the study of organizations and social behavior. Organizational attractors exist and seem to be both sensitive to initial conditions and stable. The discussion of concepts from complex systems, and their application to organizations, provides insight into how organizational research should be conducted. If organizations are assumed to exhibit nonlinear behavior, more historical, longitudinal, and qualitative research methods should be used to provide context-specific descriptions of organizational behavior.

A COMPLEX SYSTEMS APPROACH TO LEARNING IN ADAPTIVE NETWORKS

2001 ◽  
Vol 05 (02) ◽  
pp. 149-180 ◽  
Author(s):  
PETER M. ALLEN
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Knowledge Generation ◽  
Adaptive Networks ◽  
Term Survival ◽  
Complex Products ◽  
Industrial Networks ◽  
Complex Systems Thinking ◽  
Systems Models

In today's economy, the key ingredients in success and survival are adaptability and the capacity to learn and change. Recent progress in the theory of complex systems provides a new basis for our understanding of how this may actually occur, and the factors on which it depends. Complex systems thinking shows what assumptions underlie the reduction of some part of reality to a mechanical model. They demonstrate that the simplicity and "knowledge" derived from such representations can lead to an understanding that entirely misses the most important, strategic changes that may occur. Complex systems models reveal the key processes that underlie "learning", and recognise the limits to knowledge and the inherent reality of uncertainty. They demonstrate the fundamental importance of internal, microdiversity within systems, as the source of exploration that drives learning. These ideas are explained and presented in a simple model of emergent co-evolution, where the exploration of internal diversity leads to the formation of a complex, with synergetic attributes. The paper describes and models briefly the uncertainties inherent in the definition and development of a new product or service. A further model involving complex products is briefly described which shows the importance of "search" in "knowledge generation" for the success of adaptive industrial networks and clusters. All this leads to the statement of a "law of excess diversity" which states that the long-term survival of a system requires more internal diversity than appears requisite at any time.

scholarly journals Chaos in a simple model of a delta network

2020 ◽  
Vol 117 (44) ◽  
pp. 27179-27187
Author(s):  
Gerard Salter ◽  
Vaughan R. Voller ◽  
Chris Paola
Keyword(s):  
Chaotic Behavior ◽  
Initial Conditions ◽  
Fundamental Limits ◽  
Experimental Systems ◽  
Flux Partitioning ◽  
Sensitive Dependence ◽  
Statistical Behavior ◽  
The Mean ◽  
Simple Numerical Model

The flux partitioning in delta networks controls how deltas build land and generate stratigraphy. Here, we study flux-partitioning dynamics in a delta network using a simple numerical model consisting of two orders of bifurcations. Previous work on single bifurcations has shown periodic behavior arising due to the interplay between channel deepening and downstream deposition. We find that coupling between upstream and downstream bifurcations can lead to chaos; despite its simplicity, our model generates surprisingly complex aperiodic yet bounded dynamics. Our model exhibits sensitive dependence on initial conditions, the hallmark signature of chaos, implying long-term unpredictability of delta networks. However, estimates of the predictability horizon suggest substantial room for improvement in delta-network modeling before fundamental limits on predictability are encountered. We also observe periodic windows, implying that a change in forcing (e.g., due to climate change) could cause a delta to switch from predictable to unpredictable or vice versa. We test our model by using it to generate stratigraphy; converting the temporal Lyapunov exponent to vertical distance using the mean sedimentation rate, we observe qualitatively realistic patterns such as upwards fining and scale-dependent compensation statistics, consistent with ancient and experimental systems. We suggest that chaotic behavior may be common in geomorphic systems and that it implies fundamental bounds on their predictability. We conclude that while delta “weather” (precise configuration) is unpredictable in the long-term, delta “climate” (statistical behavior) is predictable.

scholarly journals Complex dynamics and control investigation of a Cournot triopoly game formed based on a log-concave demand function

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770281 ◽  
Author(s):  
K Alnowibet ◽  
SS Askar ◽  
AA Elsadany
Keyword(s):  
Local Stability ◽  
Demand Function ◽  
Complex Dynamics ◽  
Initial Conditions ◽  
Phase Portraits ◽  
Control Scheme ◽  
Sensitive Dependence ◽  
Dynamics And Control ◽  
The Stability ◽  
And Control

This article investigates the dynamics of a Cournot triopoly game whose demand function is characterized by log-concavity. The game is formed using the bounded rationality approach. The existence and local stability of steady states of the game are analyzed. We find that an increase in the game parameters out of the stability region destabilizes the Cournot–Nash steady state. We confirm our obtained results using some numerical simulation. The simulation shows the consistence with the theoretical analysis and displays new and interesting dynamic behaviors, including bifurcation diagrams, phase portraits, maximal Lyapunov exponent, and sensitive dependence on initial conditions. Finally, a feedback control scheme is adopted to overcome the uncontrollable behavior of the game’s system occurred due to chaos.

scholarly journals Studying behaviour change mechanisms under complexity

2020 ◽  
Author(s):  
Matti Toivo Juhani Heino ◽  
Keegan Phillip Knittle ◽  
Chris Noone ◽  
Fred Hasselman ◽  
Nelli Hankonen
Keyword(s):  
Complex Systems ◽  
Behaviour Change ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Systems Approach ◽  
Complex Dynamics ◽  
Behaviour Change Techniques ◽  
Behaviour Change Interventions ◽  
Complex Adaptive ◽  
Change Mechanisms

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It 1) outlines the complexity of behaviours and behaviour change interventions, 2) introduces readers to some key features of complex systems and how these relate to human behaviour change, and 3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e. interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for nonlinear time series analysis which is able to quantify complex dynamics. The supplemental website (https://git.io/Jffrm) provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.

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