As a system, a simple system can provide one single path to one answer only one solution and one way to figure out the problem. In this regard, a complex system can provide different and multiple ways toward multiple answers based on the choices made, which makes it viable that one may encounter a system which changes depending on those selections. These aspects also signify the adaptiveness of the complex systems. This adaptiveness is revealed further as more insights are developed, the answers may keep changing, which results in further and more advanced learning. During the pursuit of the use of an appropriate mathematical model, the description and analysis of a system relies on the way the system is perceived [Karaca, Y. (2022)], [Huggett, R.J. (1985)]. Hence, when complex systems are under consideration, large collection of components interacting locally with one another at smaller scales and owing self-organization to manifest global behaviors and structures at larger scales without external intervention from the environment necessitate the understanding and / or predicting the properties of such highly intricate collection based on the whole knowledge pertaining to its constituents.
All these elements explained entail novel mathematical frameworks within the scope of ever-changing dynamics and components. Thus, complexity and nonlinear sciences aim at gaining global understanding by considering and evaluating the various interactional factors of systems, branches of possible states as well as high-dimensional manifolds while monitoring actuality considered as diachrony, encompassing the historical and evolutionary path which has undergone different critical points on the manifold [Karaca, Y. (2022)]. As a consequence of the progresses in imaging technologies and integration of systems approaches, quantitative science has also been going under a constant reform. Therefore, robust conclusions to be derived from quantitative data compel a measure of variability in complex systems along with the experiments performed under intricate complex processes.
Concerning data, if a complex system is to be explored, it is important not to disregard the outlier data points as those points may be applicable as clustered measurements. To put it differently, it is important not be deceived by the trap of discarding data that do not suit to the related hypothesis tested for the subject of interest may not be straightforward or linear, providing only black or white answers. For these reasons, it is recommended not to turn the hypothesis driven research into a hypothesis forced one [How robust are your data?, (2009)], [Karaca, Y. (2022)]. At this point, reliability and robustness of data as well as its complexity in collection, storage, sharing and utilization can be provided in different areas also bring about another highly controversial and challenging point, which is the ethical considerations. In that regard, it is important to know about the different dimensions in various fields. Some of the points associated with ethics are informed consent, the way data are stored, the length of keeping and who will have access to the data. All these points also have their total legal dimensions. Another significant aspect about ethics to raise is the transparency of data, suggesting accountability and openness, referring to the control flow of the data under consideration in machine learning algorithms. To give one example from the applied aspects, transparency in medicine would help the enabling of evidence-based decisions which is pivotal to foster trust among the related parties [Joshi, M., & P. Bhardwaj, (2018)]. Transparency is also employed in science, engineering, business and social sciences, ranging from communities to administrations, from legal entities to organizations [Karaca, Y. (2022)]. One may refer to...
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