@article{MAKHILLIJEPE202115625343,
    title = {On Modelling the Structural Quasiness of Complex Systems},
    journal = {International Journal of Electrical and Power Engineering},
    volume = {15},
    number = {6},
    pages = {52-68},
    year = {2021},
    issn = {1990-7958},
    doi = {ijepe.2021.52.68},
    url = {https://makhillpublications.co/view-article.php?issn=1990-7958&doi=ijepe.2021.52.68},
    author = {Mustefa},
    keywords = {Coherence,emergence,incompleteness,interaction mechanism,quasification,quasiness},
    abstract = {Complex systems are usually represented by
structurally invariant models acquiring their characteristic
properties in simulations. This approach assumes and
infers idealized simplifications to models these systems.
We consider this standard approach as omitting crucial
features of phenomenological interaction mechanisms
related to processes of emergence of such complex
systems. We consider, as the main feature, the quasiness
of the structural dynamics that generate complex systems.
Generation achieved through prevalently coherent
sequences and combinations of interactions. Quasiness
(dynamics of loss and recovery, in homogeneity,
multiplicity, non-regularity, and partiality) represents the
incompleteness of the interaction mechanisms. Complex
systems possess local coherences corresponding to the
phenomenological complexity. Complex systems are
considered quasi-systems, not always systems, not always
the same system, and not only systems. We address
problems of representing the quasiness of coherence
(quasi-coherence) such as the ability to recover and
tolerate temporary levels of incoherence. The main results
of the study focus on modelling quasi-coherence through
the changing of rules in models of emergence. This is in
contrast to models of fixed structural rules allowing only
parametrical variations. We present a version of standard
analytical approach compatible with quasiness of systemic
emergence and related mathematical issues. The same
approach is considered for networks, artificial neural
networks and we introduce the concept of quasification
for fixed models. Finally, we assert that suitable
representations of structural dynamics and its quasiness
are needed to model, simulate, and adopt effective
interventions on emergence of complex systems. In direct
contrast to standard methods that only consider their
properties.}
    }