scholarly journals On Modelling the Structural Quasiness of Complex Systems

2021 ◽  
Vol 16 ◽  
pp. 715-734
Author(s):  
Gianfranco Minati

Complex systems are usually represented by invariant models which at most admit only parametric variations. This approach assumes invariant idealized simplifications to model these systems. This standard approach is considered omitting crucial features of phenomenological interaction mechanisms related to processes of emergence of such systems. The quasiness of the structural dynamics that generate emergence of complex systems is considered as the main feature. Generation achieved through prevalently coherent sequences and combinations of interactions. Quasiness (dynamics of loss and recovery, equivalences, inhomogeneity, multiplicity, non-regularity, and partiality) represents the incompleteness of the interaction mechanisms, incompleteness necessary even if not sufficient for the establishment of processes of emergence. The emergence is extinguished by completeness. Complex systems possess local coherences corresponding to the phenomenological complexity. While quasi-systems are not necessarily complex systems, complex systems are considered quasi-systems, being not always systems, not always the same system, and not only systems. It is addressed the problem of representing the quasiness of coherence (quasicoherence), such as the ability to recover and tolerate temporary levels of incoherence. The main results of the study focus on research approaches to model quasicoherence through the changing of rules in models of emergence. It is presented a version of standard analytical approaches compatible with quasiness of systemic emergence and related mathematical issues. The same approach is considered for networks, artificial neural networks, and it is introduced the concept of quasification for fixed models. Finally, it is considered that suitable representations of structural dynamics and its quasiness are needed to model, simulate, and adopt effective interventions on emergence of complex systems.

PAMM ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Marcus Stoffel ◽  
Franz Bamer ◽  
Bernd Markert

2019 ◽  
Vol 61 (12) ◽  
pp. 893-907 ◽  
Author(s):  
A. F. Seleznev ◽  
A. S. Gavrilov ◽  
D. N. Mukhin ◽  
E. M. Loskutov ◽  
A. M. Feigin

Computation ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 64
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak ◽  
Junlin Hao

A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models.


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