Software Development for Modeling and Simulation of Computer Networks: Complex Systems Approach

2017 ◽  
pp. 193-206
Author(s):  
Andrzej Paszkiewicz ◽  
Marek Bolanowski
Keyword(s):  
Software Development ◽  
Complex Systems ◽  
Modeling And Simulation ◽  
Computer Networks ◽  
Systems Approach

scholarly journals Toward a Common Structure in Demographic Educational Modeling and Simulation: A Complex Systems Approach

2014 ◽  
Vol 11 (2) ◽  
Author(s):  
Porfirio Guevara-Chaves
Keyword(s):  
Complex Systems ◽  
Modeling And Simulation ◽  
Systems Approach ◽  
Educational Systems ◽  
Transition Rates ◽  
Education Systems ◽  
Common Structure ◽  
Complex Interactions ◽  
Underlying Mechanisms ◽  
Schooling Outcomes

This article identifies elements and connections that seem to be relevant to explain persistent aggregate behavioral patterns in educational systems when using complex dynamical systems modeling and simulation approaches. Several studies have shown what factors are at play in educational fields, but confusion still remains about the underlying mechanisms driving observed outcomes and therefore more guidance is needed. The framework suggested here throws some ideas in that direction stressing the relevance of nonlinear complex interactions via feedbacks between education systems’ transition rates ─ intake, repetition, dropout, and promotion ─ and schooling outcomes. Schooling outcomes reciprocally influence transition rates in the system generating aggregate patterns that continuously change (and are changed by) the inputs that endogenously determine them. Furthermore, this paper underscores practical and theoretical limitations of traditional quantitative models that can be addressed with a complex systems analysis and suggests future lines of investigation. Specifically, this article advocates a complexity approach ruled by the laws of thermodynamics to help detect corrupt practices in education systems and improve accountability and governance in such systems.

scholarly journals Soil as an Archetype of Complexity: A Systems Approach to Improve Insights, Learning, and Management of Coupled Biogeochemical Processes and Environmental Externalities

Soil Systems ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 39
Author(s):  
Benjamin L. Turner
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Environmental Externalities ◽  
Supporting Evidence ◽  
Soil System ◽  
Tillage Practices ◽  
Trade Offs ◽  
Tightly Coupled ◽  
Conservation Adoption ◽  
Systems Framework

Due to tightly coupled physical, chemical, and biological processes that often behave in nonlinear, counterintuitive ways, it is argued that soil is an archetype of a complex system. Unfortunately, human intuition and decision making has been shown to be inadequate when dealing with complex systems. This poses significant challenges for managers or policy makers responding to environmental externalities where soil dynamics play a central role (e.g., biogeochemical cycles) and where full ranges of outcomes result from numerous feedback processes not easily captured by reductionist approaches. In order to improve interpretation of these soil feedbacks, a dynamic systems framework is outlined (capturing feedback often excluded from investigation or left to intuition) and then applied to agroecosystem management problems related to irrigation or tillage practices that drive nutrient cycling (e.g., soil water, nitrogen, carbon, and sodium). Key soil feedbacks are captured via a variety of previously developed models simulating soil processes and their interactions. Results indicated that soil system trade-offs arising from conservation adoption (drip irrigation or no-tillage) provided reasonable supporting evidence (via compensating feedbacks) to managers justifying slow adoption of conservation practices. Modeling soils on the foundation provided in the complex systems sciences remains an area for innovations useful for improving soil system management.

scholarly journals Extreme events defined—A conceptual discussion applying a complex systems approach

Futures ◽  
2020 ◽  
Vol 115 ◽  
pp. 102490 ◽  
Author(s):  
Lisa Hanna Broska ◽  
Witold-Roger Poganietz ◽  
Stefan Vögele
Keyword(s):  
Complex Systems ◽  
Extreme Events ◽  
Systems Approach ◽  
Conceptual Discussion

A data-driven complex systems approach to early prediction of landslides

2018 ◽  
Vol 92 ◽  
pp. 137-141 ◽  
Author(s):  
Antoinette Tordesillas ◽  
Zongzheng Zhou ◽  
Robin Batterham
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Data Driven ◽  
Early Prediction

scholarly journals Stability analysis of the banking system: a complex systems approach

2017 ◽  
Vol 10 (3) ◽  
pp. 273-284
Author(s):  
Alan Karaev ◽  
Marina Melnichuk ◽  
Timur Guev ◽  
Grzegorz Mentel
Keyword(s):  
Stability Analysis ◽  
Complex Systems ◽  
Systems Approach ◽  
Banking System ◽  
System A

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 Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling

2018 ◽  
Vol 53 (9) ◽  
pp. 560-569 ◽  
Author(s):  
Adam Hulme ◽  
Jason Thompson ◽  
Rasmus Oestergaard Nielsen ◽  
Gemma J M Read ◽  
Paul M Salmon
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Computational Modelling ◽  
Sports Injury ◽  
Physical Workload ◽  
Complex Nature ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Starting Point ◽  
Injury Research

ObjectivesThere have been recent calls for the application of the complex systems approach in sports injury research. However, beyond theoretical description and static models of complexity, little progress has been made towards formalising this approach in way that is practical to sports injury scientists and clinicians. Therefore, our objective was to use a computational modelling method and develop a dynamic simulation in sports injury research.MethodsAgent-based modelling (ABM) was used to model the occurrence of sports injury in a synthetic athlete population. The ABM was developed based on sports injury causal frameworks and was applied in the context of distance running-related injury (RRI). Using the acute:chronic workload ratio (ACWR), we simulated the dynamic relationship between changes in weekly running distance and RRI through the manipulation of various ‘athlete management tools’.ResultsThe findings confirmed that building weekly running distances over time, even within the reported ACWR ‘sweet spot’, will eventually result in RRI as athletes reach and surpass their individual physical workload limits. Introducing training-related error into the simulation and the modelling of a ‘hard ceiling’ dynamic resulted in a higher RRI incidence proportion across the population at higher absolute workloads.ConclusionsThe presented simulation offers a practical starting point to further apply more sophisticated computational models that can account for the complex nature of sports injury aetiology. Alongside traditional forms of scientific inquiry, the use of ABM and other simulation-based techniques could be considered as a complementary and alternative methodological approach in sports injury research.

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