Computational Modeling and Statistical Analysis: Discovering Simplicity in Complexity

Dear Colleagues,

In a broad context complexity implies that certain emergent properties of a system are difficult to predict even when underlying governing rules are known. Germane to different fields of study, emergent behavior, or the process of self-organization, can manifest in physical, chemical and biological systems, as well as in systems dealing with human behavior that spans psychology, sociology and economics. Transcending the phenomenon or field of study, understanding driving forces or factors that lead to collective behavior within a system is of interest here. As a bottom up approach, computational modeling elucidates how a relatively small number of rules adhered by the constituents of a system locally can produce collective global response. The response may be in the form of spatial pattern formation and/or temporal event cascades. Employing statistical analysis as a top down approach often reveals complexity exhibits statistical properties with certain structure, such as scaling laws associated with heavy tailed distributions with power-law decay. Along with collective or emergent behavior, self-similarity in spatial and/or temporal characteristics creates fractal patterns that are commonly observed in biological systems, more generally in soft-condensed matter, as well as macroscopic systems comprising agents. Another ubiquitous characteristic of systems exhibiting complexity is a high sensitivity in observed outcomes in response to small perturbations.

This thematic topic aims to uncover simplicity in complexity through computational modeling and statistical analysis, including the control of complex systems. Contributions to this special issue involving complexity defined by physical-based models, agent-based modeling or adaptive interactions in intelligent systems are welcomed. Of particular interest is the computational models, methods and theoretical foundations for generating or recognizing emergent and/or recurrent patterns in dynamical systems or stochastic processes. For example, a method could use machine learning to extract features that characterize patterns within systems exhibiting chaotic dynamics, to control emergent features generated by the chaotic dynamics or to predict outcomes with statistical descriptions based on analyzing experimental or simulation data.

Prof. Donald Jacobs
Guest Editor

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• self-organization and smart materials
• swarm intelligence and opinion dynamics
• cluster formation and mechanisms of aggregation
• self-similarity, scaling theory, power laws and fractals
• pattern formation and recognition in driven systems
• feature analysis applied to stochastic processes
• multiscale analysis in complex systems
• temporal event cascades in complex systems
• adaptive cellular automata models
• cognitive modelling and machine learning
• homeostasis and complexity through interacting agents
• perturbation/response analysis in complex systems