Self-organization has also been conflated with the idea of emergence, and indeed one can occur without the other, thus nullifying the thesis of strong reliance between the two. Moreover, western philosophical traditions have been quite vocal in their skepticism about emergence and order within a structure, if there isn’t a presence of an external agency, either in the form of God, or in some a priori principle. But these traditions are indeed in for a rude shock, since there is nothing mystical about emergence and even self-organization (in cases where they are thought to be in conflated usage). Not just an absence of mysticism characterizing self-organization, but, even stochasticity seems to be a missing link in the said principle. Although, examples supporting the case vary according to the diverse environmental factors and complexity inherent in the system, the ease of working through becomes apparent, if self-organization or autopoiesis is viewed as a the capacity exhibited by the complex systems in enabling them to change or develop the internal structure spontaneously, while adapting and manipulating with their environment in the ongoing process. This could very well be the starting point in line with a working definition of autopoiesis. A clear example of this kind would be the human brain (although, brains of animals could suffice this equally well), which shows a great proclivity to learn, to remember in the midst of its development. Language is another instance, since in its development, a recognition of its structure is mandated, and this very structure in its attempt to survive and develop further under circumstances that are variegated, must show allegiance to adaptability. Even if, language is guided by social interactions between humans, the cultural space conducive for its development would have strong affinity to a generalized aspect of linguistic system. Now, let us build up on the criteria of determining what makes the system autopoietic, and thereby see what are the features that are generally held to be in common with autopoietic systems.
Self-organization abhors predetermined design, thus enabling the system to dynamically adapt to the regular/irregular changes in the environment in a nonlinear adherence. Even if emergence can occur without the underlying principle of self- organization, and vice versa, self-organization is in itself an emergent property of the system as a whole, with the individual components acting on local information and general principles. This is crucial, since the macroscopic behavior emerges out of microscopic interactions that in themselves are carriers of scant information, and in turn have a direct component of complexity associated with them when viewed microscopically. This complexity also gets reflected in their learning procedures, since for systems that are self-organizing, it is only the experiential aspects from previous encounters compared with the recent ones that help. And what would this increase in complexity entail? As a matter of fact, complexity is a reversal of entropy at the local level, thus putting the system at the mercy of a point of saturation. Moreover, since the systems are experiential, they are historical and hence based on memory. If such is the case, then it is safe to point out that these systems are diachronic in nature, and hence memory forms a vital component of emergence. Memory as anamnesis is unthinkable without selective amnesia, for piling up information does trade off with relevance of information simultaneously. For information that goes under the name of irrelevant, is simply jettisoned, and the space created in this manner is utilized for cases pertaining to representation. Not only representation sort of makes a back door entry here, it is also convenient for this space to undergo a systematic patterning that is the hallmark of these systems. Despite the patterns being the hallmark of self-organization, it should in no way be taken to mean that these systems are stringently teleological, because, the introduction of nonlinear functions that guide these systems introduce at the same time the shunning off of a central authority, or anthropomorphic centrality, or any external designer. Linear functions could partake in localized situations, but at the macroscopic level, they lose their vitality, and if complex systems stick on to their loyalty towards linear functions, they fade away in the process of trying hard to avoid negotiating this allegiance. Just as allegiance to nonlinearity is important for self-organization, so is an allegiance to anti-reductionism. That is due to the fact of micro-level units having no knowledge about the macro-level effects, while at the same time, these macro-level effects manifest themselves in clusters of micro-level units, thus ruling out any sort of independent level- based descriptions. The levels are stacked, intertwined, and most importantly, any resistance to reductionist discourse in trying to explicate the emergence within the system has no connotation for resistance to materialist properties themselves emerging.
Clusters of information flow into the system from the external world that have an influencing impact on the internal makeup of the system and in turn triggers off interactions in tune with Hebb’s law to alter weights. With the process in full swing, two possibilities could take shape, viz, formation of a stable system of weights based on the regularity of a stable cluster, and association between sets of these stable clusters as and when they are identified. This self-organizing principle is not only based on learning, but at the same time also cautious with sidelining those that are potentially futile for the system. Now, when such information flows into the system, sensors and/or transducers are set up, that obligate varying levels of intensity of activity to some neurons and nodes over others. This is of course to be expected, and the way to come to terms with a regulated pattern of activity is the onus of adjustments of weights associated with neurons/nodes. A very important factor lies in the fact of the event denoting the flow of information from the external world into the system to occur regularly, or at least occasionally, lest the self-organizing or autopoietic system should fail to record in memory such occurrences and eventually fade out. Strangely, the patterns are arrived at without any reliance upon differentiated micro-level units to begin with. In parallel with neural networks, the nodes and neurons possess random values for their weights. The levels housing these micro- level nodes or neurons are intertwined to increase their strength, and if there is any absence of self-persisting positive feedback, the autopoietic system can in no way move away from the dictates of undifferentiated states it began with. As the nodes are associated with random values of weights, there is a race to show superiority, thus arresting the contingent limitless growth under the influence of limitless resources, thereby giving the emerging structure some meaningful essence and existence. Intertwining of levels also results in consensus building, and therefore effectuates meaning as accorded to these emergent structures of autpoietic systems. But this consensus building could lead astray the system from complexity, and hence to maintain the status quo, it is imperative for these autopoietic systems to have a correctional apparatus. The correctional apparatus spontaneously breaks the symmetry that leads the system away from complexity by either introducing haphazard fault lines in connections, or chaotic behaviors resulting from sensitivity to minor fluctuations as a result of banking on nonlinearity. Does this correctional apparatus in any way impact memory gained through the process of historicality? Apparently not. This is because of the distributed nature of memory storage, which is largely due to weights that are non-correspondingly symbolic. The weights that show their activity at the local scale are associated with memory storage through traces, and it is only due to this fact that information gets distributed over the system generating robustness. With these characteristic features, autopoietic systems only tend towards organizing their structures to the optimum, with safely securing the complexity expected within the system.