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.
Artificial neural networks in structural dynamics
