Applying Complexity Science with Machine Learning, Agent-Based Models, and Game Engines: Towards Embodied Complex Systems Engineering

2018 ◽  
pp. 173-183
Author(s):  
Michael D. Norman ◽  
Matthew T. K. Koehler ◽  
Jason F. Kutarnia ◽  
Paul E. Silvey ◽  
Andreas Tolk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complex Systems ◽  
Systems Engineering ◽  
Complexity Science ◽  
Agent Based Models ◽  
Agent Based ◽  
Learning Agent ◽  
Game Engines

Who Is Unhappy for Brexit? A Machine-Learning, Agent-based study on Financial Instability

2020 ◽  
Author(s):  
Stathis Polyzos ◽  
Aristeidis Samitas ◽  
Marina-Selini Katsaiti
Keyword(s):  
Machine Learning ◽  
Financial Instability ◽  
Agent Based ◽  
Learning Agent

Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences

2019 ◽  
Vol 28 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Jens Koed Madsen ◽  
Richard Bailey ◽  
Ernesto Carrella ◽  
Philipp Koralus
Keyword(s):  
Complex Systems ◽  
Closed Form ◽  
Model Development ◽  
Cognitive Models ◽  
System Level ◽  
Model Parameters ◽  
Social Psychological ◽  
Agent Based Models ◽  
Agent Based ◽  
Closed Form Solutions

Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without application of significant simplifications. In addressing this problem, cognitive and social psychological sciences may profitably use agent-based models, which are widely employed to simulate complex systems. We show that these models can be used to explore how cognitive models scale in social networks to calibrate model parameters, to validate model predictions, and to engender model development. Agent-based models allow for controlled experiments of complex systems and can explore how changes in low-level parameters impact the behavior at a whole-system level. They can test predictions of cognitive models and may function as a bridge between individually and socially oriented models.

scholarly journals Proposal of a Conceptual Model to Represent Urban-Industrial Systems from the Analysis of Existing Worldwide Experiences

2021 ◽  
Vol 13 (16) ◽  
pp. 9292
Author(s):  
Carmen Ruiz-Puente
Keyword(s):  
Complex Systems ◽  
Conceptual Model ◽  
Environmental Impacts ◽  
Urban Areas ◽  
Industrial Symbiosis ◽  
Agent Based Models ◽  
Industrial Systems ◽  
Agent Based ◽  
Self Organized ◽  
Perspective Analysis

The adoption of Industrial Symbiosis (IS) practices within urban areas is gaining interest due to the environmental impacts entailed by the development of cities. However, there is still a lack of knowledge about how the relationships between industrial and urban areas can be modelled. In this context, this research aimed at posing a conceptual model to understand and represent Urban-Industrial Systems (UIS). To this end, a set of worldwide previous UIS experiences were overviewed to identify the agents, dynamics, and collaboration opportunities that characterize them. The multi-perspective analysis of these cases indicated that UIS are complex systems, which means that they are autonomous, self-organized, responsive, nonlinear, and willing to consolidate their resilience. As such, Agent-Based Models (ABM) were suggested to be the most suitable approach for their representation.

scholarly journals Heterogeneity: method and applications for complex systems analysis

2022 ◽  
Vol 2159 (1) ◽  
pp. 012013
Author(s):  
J M Redondo ◽  
J S Garcia ◽  
C Bustamante-Zamudio ◽  
M F Pereira ◽  
H F Trujillo
Keyword(s):  
Machine Learning ◽  
Complex Systems ◽  
Research And Development ◽  
Systems Analysis ◽  
Ecological Systems ◽  
Agent Based Modeling ◽  
Time Lags ◽  
Agent Based ◽  
Physical Systems ◽  
Sustainability Analysis

Abstract Socio-ecological systems like another physical systems are complex systems in which are required methods for analyzes their non-linearities, thresholds, feedbacks, time lags, and resilience. This involves understanding the heterogeneity of the interactions in time and space. In this article, we carry out the proposition and demonstration of two methods that allow the calculation of heterogeneity in different contexts. The practical effectiveness of the methods is presented through applications in sustainability analysis, land transport, and governance. It is concluded that the proposed methods can be used in various research and development areas due to their ease of being considered in broad modeling frameworks as agent-based modeling, system dynamics, or machine learning, although it could also be used to obtain point measurements only by replacing values.

scholarly journals Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey

2021 ◽  
Vol 11 (12) ◽  
pp. 5736
Author(s):  
Jianbo Gao ◽  
Bo Xu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Complex Systems ◽  
Complexity Science ◽  
Complexity Measures ◽  
Highly Nonlinear ◽  
Course Material ◽  
Training Courses ◽  
The Many ◽  
Abnormal Behaviors

Mankind has long been fascinated by emergence in complex systems. With the rapidly accumulating big data in almost every branch of science, engineering, and society, a golden age for the study of complex systems and emergence has arisen. Among the many values of big data are to detect changes in system dynamics and to help science to extend its reach, and most desirably, to possibly uncover new fundamental laws. Unfortunately, these goals are hard to achieve using black-box machine-learning based approaches for big data analysis. Especially, when systems are not functioning properly, their dynamics must be highly nonlinear, and as long as abnormal behaviors occur rarely, relevant data for abnormal behaviors cannot be expected to be abundant enough to be adequately tackled by machine-learning based approaches. To better cope with these situations, we advocate to synergistically use mainstream machine learning based approaches and multiscale approaches from complexity science. The latter are very useful for finding key parameters characterizing the evolution of a dynamical system, including malfunctioning of the system. One of the many uses of such parameters is to design simpler but more accurate unsupervised machine learning schemes. To illustrate the ideas, we will first provide a tutorial introduction to complex systems and emergence, then we present two multiscale approaches. One is based on adaptive filtering, which is excellent at trend analysis, noise reduction, and (multi)fractal analysis. The other originates from chaos theory and can unify the major complexity measures that have been developed in recent decades. To make the ideas and methods better accessed by a wider audience, the paper is designed as a tutorial survey, emphasizing the connections among the different concepts from complexity science. Many original discussions, arguments, and results pertinent to real-world applications are also presented so that readers can be best stimulated to apply and further develop the ideas and methods covered in the article to solve their own problems. This article is purported both as a tutorial and a survey. It can be used as course material, including summer extensive training courses. When the material is used for teaching purposes, it will be beneficial to motivate students to have hands-on experiences with the many methods discussed in the paper. Instructors as well as readers interested in the computer analysis programs are welcome to contact the corresponding author.

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