The Paradigm of Complexity
5. Features of critical complexity
Having traced a selective history of complexity, and having made a couple of critical comments on how the paradigm of critical complexity compares with some of the movements that characterise this history, we are now in a position to elaborate on a number of features that constitute the paradigm of critical complexity. However, before doing so, it is important to note that the concept of a ‘paradigm’ comes with its own difficulties. Whilst it is impossible to formulate the premises of a meta-paradigm (that is, a superior system which is both meta-human and meta-social (Morin, 2008: 51)), the paradigm of critical complexity does claim a certain ‘universality for its grasp of its object in the sense that it deals with [all complex systems] and not just sections’ (Luhmann, 1995: xlvii). However, the paradigm of critical complexity simultaneously ‘claims neither to reflect the complete reality of its object, nor to exhaust all the possibilities of knowing its object’ (xlvii). Allen (2000: 78) describes this difficulty in terms of a paradox: on the one hand we wish to gather systemic knowledge about the objects of study; yet, on the other hand, the objects of study are characterised as intricate or hard (indeed, impossible!) to unravel.
The features of critical complexity described in this section are meant to illuminate aspects of our reality ‘exactly like the towers in a concentration camp, which were built to allow the captors to better look at the society and its outside environment’ (Morin, 2008: 50). However, it must also be kept in mind that the paradigm of critical complexity demands an attitude of modesty on the part of the theorist, and a concession that we are also captives in our theoretical models in that ‘complex thought requires the integration of the observer and the conceiver in its observation and conception’ (51). Having provided this caveat, the analysis can proceed with a summary of the features of critical complexity:
5.1. Complex systems are not complicated systems
Cilliers (1998: 3) states that an important distinction exists between complicated and complex systems47: whereas a complicated system may initially look complex (due to the large number of components that may constitute the system, and/or the sophistication of the tasks that the systems can perform), the hallmark of a complicated system is that it is – in principle – solvable. In other
46 For a description of how these features correspond with the insights of affirmative postmodernism, see: McKelvey, 2002: 13-14.
47 Also see Richardson (2001; 2002) for a description of the differences between complex and complicated systems.
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words, given enough information and resources, the dynamics of a complicated system can be fully understood. Cilliers (3) offers the jumbo jet as an example of a complicated system.
As opposed to complicated systems, complex systems are ‘constituted by such intricate sets of non- linear relationships and feedback loops that only certain aspects of them can be understood at a time’ (3). Following Weaver (see footnote 45), we can state that complex systems display organised complexity (which, as explained earlier, means that systems are inherently complex due to their organising processes). Since only certain aspects of complex systems can be understood at a time, it also means that complex systems are not reducible or compressible (or, to reiterate Rosen’s (1985) words, a system is complex precisely ‘to the extent that it admits non-equivalent encodings; encodings which cannot be reduced to one another). Moreover, because complex systems cannot be fully understood, our descriptions of complex systems cause further distortions. In other words, we model complex systems in order to better understand them, but since our models are imperfect renditions of complex systems, they introduce further uncertainties. Luhmann (2000: 46) describes this consequence of modelling as a paradox:
The self-description of the self-transparent system has to use the form of a paradox, a form with infinite burdens of information and it has to look for one or more distinguishable identities that “unfold” the paradox, reduce the amount of needed information, construct redundancies, and transform unconditioned into conditioned knowledge… [but] the question of the unity of the distinction always leads back to the paradox – and one can show this to others and accept it for oneself.
The issue at stake here is not so much the paradox itself: if we concede to the fact that the world is complex, then the paradox of framing or modelling is part of the complexity with which we have to grapple. In other words, we have to frame. Rather, the issue is whether we accept the paradoxical status of frames (or, in Luhmann’s words, whether ‘one can show this to others and accept it for oneself.’)
It is clear from the preceding analysis that those wishing to create unified theories of complexity are of the opinion that complex systems are merely complicated systems. In other words, complexity – in their eyes – is a function of our knowledge (epistemology) rather than an inherent characteristic of certain systems (ontology). It is believed that, with enough computing power, we will be able to establish the laws of complexity. Admittedly, the distinction between complicated and complex systems is often undermined in practice by powerful new technologies, where complex phenomena
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turns out (on further inspection) to be merely complicated (Cilliers, 1998: 3). However, despite the fact that the distinction between complicated and complex systems cannot be drawn in any unproblematic manner, the distinction, nevertheless, remains a useful analytical tool as it determines whether the study of complexity constitutes a search for underlying mathematical rules and formulae, or whether the study of complexity constitutes a considered engagement with complexity (Cilliers, 1998; Morin, 2007).
Whereas the former group of complexity theorists implicitly accepts the scientific ideals of explanation, prediction, and facilitation of control (Chu et al., 2003), the latter group tries to develop strategies and models to help us better deal with the complexity that characterises not only living systems, but also social systems. In terms of ethics, one can state that those who seek meta- frames (constituted by categorically-binding moral laws) to map out the moral world follow a restricted approach to ethical complexity, whereas those who attempt to engage with the contingencies and provisionality that characterise our moral experiences treat ethical complexity as an instance of critical complexity.
5.2. Complex systems display emergent behaviour due to dynamic self-organisation 5.2.1. Interactions in complex systems
In sections 4.3., the complex nature of the interactions (or more generally, the interrelations) between systems and their components was elaborated upon. The main insight derived from this section is that systemic relations are complex relations, meaning that they cannot be described by the principle of reduction (i.e. explaining the whole in terms of the parts), or by the principle of holism (i.e. explaining the parts in terms of the whole). This is, in part, due to the diversity and entropy which characterises open systems.
Equally important, however, is the non-linear dynamic organisation or emergence that takes place in systems where local interactions allow for global structures. As such, the interaction between systemic components can be described in terms of the following additional features (Cilliers, 1998: 3-4): the interactions between the components of a system can be physical or informational; the interactions are fairly rich ‘i.e. any element in the system influences and is influenced by quite a few other ones’ (3); the interactions have a short range, but these local interactions can have large systemic effects, which implies that systems-level order emerges because of interactions amongst
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components at lower levels of the system (Andersen 1999); and, there are positive (stimulating) and negative (inhibiting) feedback loops in the interactions.
This last feature is also referred to as ‘organizational recursion’ (Morin, 2008: 49), where a recursive process is defined as ‘a process where the products and the effects are at the same time causes and producers of what produces them’ (49). Therefore, just like the interactions between components create systemic structures and constraints, so too feedback loops allow for the system itself to constrain the behaviour of the parts by means of framing the identity of the components in a systemic context. Morin (50) uses the idea of the hologram to explain this last point: ‘[i]n a physical hologram, the smallest point of the hologram image contains the quasi-totality of information of the represented object. [Therefore,] not only is the part in the whole, but the whole is also in the part.’ Morin (50) offers the example of society to illustrate this point: not only do individuals produce society through their interactions, but from as early as childhood, society enters us through a process of socialisation, which supplies us with language and culture (50).
5.2.2. Structure, self-organisation and emergence
When the components of systems interact, dynamic structures emerge over time due to self- organisation. Self-organisation can be defined as ‘a process whereby a system can develop a complex structure from fairly unstructured beginnings’ (Cilliers 1998: 12). Contrary to popular opinion, complex systems are not flat systems. In other word, ‘[c]omplex systems are neither homogenous nor chaotic’ (Cilliers, 2001: 139). Instead, the interactions between components create systemic structures (including nested systems within the larger system (Ashmos & Huber 1987)). Whereas some structures are more durable, others are more volatile and ephemeral (Cilliers, 2001: 140). Cilliers (1998: 89) defines the notion of structure as:
the internal mechanism developed by the system to receive, encode, transform and store information on the one hand, and to react to such information by some form of output on the other.
In order to make the case for self-organisation, it is necessary to show that ‘internal structure can evolve without the intervention of an external designer or the presence of some centralised form of internal control’ (89). In other words, one must show that complex, self-organising systems are emergent, where emergence is defined as a quality that is ‘indeductible from the qualities of the parts, and thus irreducible’ (Morin, 2007: 12). This is only possible if we treat the concept of self-
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organisation not merely as a structural concept, but also as a temporal concept: a self-organising system must not only have structure, but also a history. Complex systems must be able to ‘learn’ from experience, and ‘remember’ past encounters (Cilliers, 1998: 92). Cilliers (92) explains that ‘[i]f more ‘previous information’ can be stored, the system will be able to make better comparisons. This increase in complexity implies a local reversal of entropy, which necessitates a flow of information through the system.’ It is, therefore, only possible for systems to develop complex structures by processing information, and developing ‘memory’. The example of neural networks offers a good explanation of this principle: neural networks are chemically-connected or functionally-associated neurons. The interconnections between these neurons are called synapses. Over time, certain pathways are established in the brain, meaning that some of the synapses are reinforced through impulses, whereas others die off. In this way, structure develops as ‘groups [of neurons] are selected, altered and maintained in a dynamic way through interaction with the environment’ (105). This implies that a fairly undifferentiated brain develops structure or consciousness over time.
What should be clear from the above description is that the structural and temporal dimensions of self-organisation (as an emergent process) do not allow for an understanding of complexity in terms of absolute thresholds (as implied by von Neuman’s use of the term ‘complexity barrier’). It is not the case that simple systems suddenly start showing emergent behaviour. As soon as dynamic and complex interactions between systemic components exist, systems start developing structures. However, complexity is also not an additive process, since the interactions between components are non-linear and allow for surprising reconfigurations of systemic structures. As such, trying to pinpoint optimal levels of organisation, through recourse to terms such as ‘self-organised criticality’, again denies a measure of complexity.
5.3. Complex systems interact with their environment in ways that constitute the system itself The principle of homeostasis that underlies both the cybernetics paradigm and autopoietic systems is grounded in the supposition that systems are operationally-closed (that is, systems facilitate their own production and maintenance through feedback loops). Over and against this view, Prigogine’s work on dissipative structures shows that living systems are open systems. Without a constant exchange of energy with the environment, systems are likely to reach an equilibrium point and die. To reiterate: disequilibrium is an essential feature of complex systems, because it is a precondition for self-organisation and system’s survival (Cilliers, 1998: 4).
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Morin (2008: 49) calls this feature of complex systems the ‘dialogic principle’. The dialogic principle combines the idea of structure, order, and stability with disorder, degradation, and change. Morin (49) explains this term with reference to the two types of chemico-physical entities with which we are born, namely DNA and amino-acids: on the one hand, DNA represents a stable entity, which carries memory and is hereditary; on the other hand, amino-acids – which live in contact with the environment – are extremely unstable and are constantly degrading, in order to recreate themselves from messages that emanate from the DNA. Therefore, counter-intuitively, we would not be alive if it was not for the fact that our organism constantly degrades its energy, in order to produce new cells (Morin, 2007: 16). In this regard, Morin (16) recalls the illuminating phrase spoken by Heraclitus in the 6th century BC: ‘live of death, die of life’. Morin (2008: 49) further
writes that we should understand these two logics, ‘that of transindividual reproduction and that of individual existence here and now’, as both complementary and antagonistic. In other words, we must think in terms of a double logic.
With regard to living organisms, the idea of open systems can be adequately explained with reference to the second law of thermodynamics. However, applying this insight to social systems creates a problem: according to Luhmann, social systems are necessarily operationally-closed, in that the system and the environment can only be known from the observer’s perspective. In defining a social system, the observer not only constitutes the system, but also defines herself and the environment in terms of the given system. Therefore, the observer constitutes the observation in as much as the observation constitutes the observer (Arnoldi, 2001: 5; see also: Luhmann, 1996: 24; 1995: 76). Additionally, there can be no external point from which to talk about a system, and new operations can only be built upon the system’s own previous operations. In other words, observation is a reflexive process, which never stabilises, and which generates additional complexity precisely because ‘[a]n observing system observes itself failing to observe itself fully’ (Rasch, 1991: 77; see also: Luhmann, 1990: 83). For Luhmann, there can, in other words, be no breach in a system. Over-and-against this perspective, Derrida’s work on deconstruction and the double-movement, as well as his use of terms such as différance and trace all present attempts to find a breach or gap in the system. As described in the next chapter (see sec. 3.2.), this breach constitutes the event as an experience of the impossible. Caputo (1997a: 51) writes that the impossible should not be understood as the modal opposite of the possible, but as a rupture or a passage to the limits. In this regard, he states that ‘[t]he desire of deconstruction for the more-than-possible impossible is a passion that it shares with apophatic theology’ (51). This breach also functions according to the dialogic principle: the impossible is mediated through the possible. The moment of the event is both
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an experience of the possible (that is, of calculation, stability, and order), and a momentary transgression of the possible. As such, Derrida’s philosophy provides an opening for thinking of social systems as open systems. However, this opening cannot be thought of logically, but demands a complex thinking. With regard to différance, Teubner (2001: 41) writes:
The open dance of heterogeneous operations, the infinite network of relations, the interplay of various aspects which occurs continuously without transferring them to a closed system – these are dangerous supplements to autopoietic closure. This understanding of différance cannot be systematically integrated into autopoiesis, it comes from outside as a threatening affliction of closed systems.
This point can only be fully grasped once the theory of deconstruction is adequately explained (chapter 4), and the understanding of deconstruction as a complex position is clearly elucidated (chapter 5 and 6). However, it is important to introduce the reader to the two main camps with regard to system-environment interactions, as this distinction holds implications for how we view the ethics of complexity48. Although Luhmann’s work undoubtedly holds important insights for
conceptualising social systems as radically immanent systems, the problem with endorsing a strong view of recursivity (where a system maintains itself in terms of its own operationally-closed systems) is that it leads to solipsistic or relativist implications. As such, Derrida’s work presents an important step in thinking about the relation between systems and their environments in more open terms. This study, therefore, simultaneously takes seriously the implications that our embedded, complex, and immanent perspectives hold for the status of our knowledge claims (Luhmann’s position), and the implications that arise from attempting to think beyond this conceptual, logical level (Derrida’s position).
Although the purpose of this chapter was to provide an overview of the paradigm of complexity, the ethics of complexity is briefly introduced in the concluding section. In this regard, specific attention is accorded to the importance of choice in modelling complex systems, the embeddedness of ethical practices, and the provisional nature of ethical knowledge49 – in order to summarise the implications that the above analysis holds for understanding the ethics of complexity.
48 This issue is revisted and explored in more detail in chapter six, section 2.
49 These three categories correspond closely to the three postmodern tenets introduced in chapter two, section 6.
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