Paul Cilliers - Complexity and Postmodernism (2022)

Paul Cilliers - Complexity and Postmodernism (1)

I have now read through this book three times. Every reading wasenjoyable and informative. I make these comments at the beginning of thisreview because when I saw this book announced, I dreaded reading it. Thetitle suggested that the reader might be in for a trudge through a turgidand unintelligible assertion of the absolute relativism of knowledge withthe general postmodernist programme reinforced by a turn to chaos andcomplexity. That is pretty well exactly what the text is not. It isclearly, indeed beautifully, written and although it seeks to reconcilepoststructuralist perspectives and complexity, Cilliers is adamant indismissing the notion that such a reconciliation provides a license forabsolute relativism. This is an important book with a substantial argumentto make. It is full of good things. At the same time there are importantand suggestive absences in it, absences which are of very considerablesignificance for the general project of 'simulating society'.

In this review I propose to go through the text section by section,identifying Cilliers' themes and beginning to argue and/or agree with himas the exposition unfolds. At the end of this process I want to make somegeneral remarks about the significance of the book in relation tosimulation of the social. There I will note the resonance of much thatCilliers says with recent claims about the desirability of a relationalsociology.

Cilliers begins in his preface, an important part of this text, and inhis first chapter by defining complexity and locating our capacity toapproach complex systems. His definition is quite usual in that hedescribes complexity in terms of emergence. However, he goes further. Firsthe places very considerable emphasis on the specificity of complex systems,whilst still allowing for the possibility of a general programme ofunderstanding and for the practice of modelling:

'The most obvious conclusion drawn from this perspective isthat there is no over-arching theory of complexity that allows us to ignorethe contingent aspects of complex systems. If something really is complex,it cannot by adequately described by means of a simple theory. Engagingwith complexity entails engaging with specific complex systems. Despitethis we can, at a very basic level, make general remarks concerning theconditions for complex behaviour and the dynamics of complex systems.Furthermore, I suggest that complex systems can be modelled.' (p.ix)

It is important to recognize immediately that this insistence on thesignificance of contingency is radically different from the approach ofthose such as Holland (1998) who want tounderstand emergence in terms of the potential of formulated rules andspecific dynamic equations. For Cilliers, emergence arises both from thecomplexity of internal interactions in systems and from the interactions ofsystems with their external environment. Indeed, given the open characterof complex systems, the construction of boundaries between the system andits environment is essentially a product of the process of observation. (p.5)

(Video) Paul Cilliers - Do modest positions have to be weak? Complexity, Knowledge and Responsibility

Second, whilst not entirely dismissing any relationship between chaosand complexity, Cilliers is sceptical about this as a fundamentalassumption. He considers that the complexity programme is in no waydependent on chaotic dynamics as a source, making the point that themathematical programme of chaos deals with the non-linear interaction ofrelatively small numbers of equations in contrast to the huge number ofinteracting components in any real complex system. Cilliers claims that wecan manage our understanding of phase shifts, sharp transitions betweendifferent states of a system, by using the idea of self-organisedcriticality rather than the metaphorical apparatus of chaos. This is animportant point and one which has very much clarified my thinking but Iwould be very reluctant to lose the ideas of bifurcation and attractorstates which derive from the chaos account. I will return to this pointlater with particular reference to the idea of control parameters.

The basis of our capacity to address complex systems, says Cilliers,lies in the capacities of electronic computers which essentially extend thecognitive range of science. However, they do so in a way which isabsolutely antithetical to the reductionist programme which has underpinnedscience as a practice since Newton. He puts it like this:

'At the heart of the matter ... our technologies have becomemore powerful than our theories. ... We can do with technology what wecannot do with science.' (pp. 1-2)

Here plainly Cilliers is using science in the sense of constructedgeneral theory, although he does not always stick to this usage. Later inChapter One, the object of science is defined in terms of prediction incontrast to the object of philosophy which is cast in terms ofunderstanding. In the latter usage, Cilliers is treating science as theequivalent of engineering as defined by Crutchfield:

' ... the epistemological problem of nonlinear modelling can becrudely summarized as the dichotomy between engineering and science. Aslong as a representation is effective for a task, an engineer does not carewhat it implies about underlying mechanisms; to the scientist though theimplication makes all the difference in the world. The engineer iscertainly concerned with minimizing implementation cost ... but thescientist presumes, at least, to be focused on what the model meansvis--vis natural laws. The engineering view of science is that it is meredata compression; scientists seem to be motivated by more than this.'(Crutchfield 1992, p. 68)

This approach to representation is not merely something Cilliersaccepts. He argues that for complex systems, it is all we can achieve,although he does not necessarily think we aspire to predictive power.

(Video) Complexity and the Social World: building on the legacy of Allen, Byrne, Stacey and Cilliers

For Cilliers, representation is always distributed. We cannot generatealgorithms to describe significant natural complex systems. We mighthowever be able to create models which work in the same way. His theory ofrepresentation is, I think, essentially one of heterologous analogy as thatterm is defined by Khalil (1996). It is not atheory of strong representation, a term which I take to be equivalent toKhalil's conception of unificational metaphor, similarities arising fromthe operation of the same basic law. Representation is distributed andbased on connectionism - the core argument of the second chapter of thisbook. Cilliers' distributed representation using primarily (but notexclusively) neural networks, can represent because such network systemsare to be understood in terms of the interactions among elements and notthrough an underlying generative law. They are themselves part of thegeneral class of complex systems. I will note here that Cilliers'identifies evolutionary algorithms as another component of the distributedrepresentational armoury, which is interesting given that Holland (whodeveloped this approach) is insisting on rule bases for emergence.

Throughout the text Cilliers has two referent systems for his account ofcomplexity. These are the mammalian brain and natural language. Theconnectionist account of representation, presented against the rule basedapproach of strong representation, is primarily developed in relation tothe debates surrounding the possibility and nature of artificialintelligence. The arguments about language begin in earnest in ChapterThree where Cilliers presents an exposition of Saussure's model of languagewith its absolute insistence on the significance of meaning (as opposed tostructure) and continues with a review of Derrida's 'deconstruction' ofSassure's account. The point of this exercise is that Cilliers caninterpret Derrida's approach as one which identifies language itself as anopen system far from equilibrium, with (and he cites Luhmann here) 'morepossibilities than can be actualized'. (p. 42) Derrida's conception of thesignificance of différance is equated in systemic terms with Freud'saccount of the relationships among neurones as one of differences.

This is an interesting and persuasive approach. What Cilliers does istake two fundamental areas where scientific and philosophical debateintersect, the nature of language and the nature of mind, and argue for theobjects of both intellectual projects as being far from equilibrium systemsfounded on distributed connections and working through the differencesamong the components of the systems. In other words, the account isabsolutely relational. Explicitly this is a programme of analogy, but Ithink there is more to it than that. Certainly in implicit terms, andperhaps explicitly as well, Cilliers is saying that the two great basics ofunderstanding, mind and language, can only be comprehended as complex farfrom equilibrium systems and that this is fundamental (a word with adifferent content from foundational) to our capacities for engaging withthe world.

Following arguments seeing off John Searle in Chapter Four, Cilliersturns to the basic question of representation. There is a lot in thischapter, but what is most interesting for a review to be published in JASSSis Cilliers' use of Baudrillard's notion of the hyperreal and simulation.For Baudrillard, and it seems Cilliers (although I am less sure aboutthis), the real is something that can be copied. Here, we come to one ofthe significant absences in this book. Cilliers does not engage at all withBhaskar's critical realism in which the domain of the real is not somethingwhich can be copied but something which contingently generates the actual.Instead, he seems to endorse Baudrillard's dismissal of essentialabstraction of the real - certainly Bhaskar's generative mechanisms wouldseem to have an essential character in this sense - turning in consequenceto the idea of simulation as something which attempts to repeat the real.(p. 84) This point is illustrated by reference to the way in which a neuralnetwork can be simulated on a digital computer, but I wonder if theillustration does in fact support the argument. Certainly we can consider asimulated neural network as being the same kind of thing in terms of systemcharacter as a constructed neural network, but both do share a realityexactly founded on their essential connectionism - an argument for anon-reductionist essentialism.

(Video) Paul Cilliers - Discussion

This turn to hyperreal simulation has its problems because of theemphasis on meaning itself as an emergent property in the process ofinterpretation, instead of some causal process inherent in the system beingobserved. For example, following Derrida, Cilliers considers that thesignificance of images in our culture is the source of the general tendencyin science '... to fall prey to a metaphysics of presence.' (p. 82) Thiswould seem to imply a rejection of the iconological approach (see Reed andHarvey 1996) and yet the turn to interpretationthrough images, and moving images at that, seems a fundamental part ofsimulation's project for dealing with our inability to reduce complexsystems to sets of differential equations. Simulation is to a considerableextent an exercise in representing the real, again in a way which isexplicitly not reductionist/analytical but which nonetheless asserts thatthe representation is an analogue of the thing being represented. Reallythis is the crucial ambiguity in Cillier's text, and one which he himselfkeeps coming back to. At one level, the postmodernist account seems to denythe possibility of any representation but Cilliers is always arguing thata distributed holistic analogy is possible.

'It bears repetition that an argument against representation isnot anti-scientific at all. It is merely an argument against a particularscientific strategy that assumes complexity can be reduced to specificfeatures and then represented in a machine. Instead it is an argument forthe appreciation of the nature of complexity, something that can perhaps be'repeated' in a machine, should the machine itself be complex enough tocope with the distributed character of complexity.' (p. 86)

And yet as Cilliers puts it, in a way which any sociologist must agreewith:

'... a certain theory of representation implies a certaintheory of meaning - and meaning is what we live by ...' (p. 88)

Chapter Six deals with 'self organisation in complex systems'. Againthere is much that is interesting and important here, but this discussioncontains (an appropriately postmodern mode of expression) two of thecrucial absences in the text. The first is that there is no sense of thenested character of complex systems. Words and neurones can perhaps beproperly considered as atomic nodes. In other words, we don't have to thinkabout them as complex systems in their own right, although a neurone iscertainly internally a complex system with potential implications ifcancerous for the whole distributed system in which it is a node. However,the social is always nested. Individual selves, which may not be much afterDerrida, but they are something, are themselves complex systems with theirown evolutionary trajectories. Even the individual, the macro-actor, mayhave effects on social systems if powerful enough. Certainly collectivitiesof individuals, collective actors with their own emergent properties, formpart of a social order. If we think about a purely structuralrepresentation of this nesting, the discussions of localities withinregions within a global economic and cultural system, then we can see theissue. The urban is one of the few domains of significant social simulationand work on simulating urban processes demonstrates absolutely the need toconceptualise systems as nested (Batty 1995, Batty and Xie 1997).

The other absence, essentially a deliberate deletion by Cilliers, is theidea of control parameters. He says:

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'In our analysis of complex systems ... we must avoid the trapof trying to find master keys. Because of the mechanisms by which complexsystems structure themselves, single principles provide inadequatedescriptions. We should rather be sensitive to complex and self-organizinginteractions and appreciate the play of patterns that perpetuallytransforms the system itself as well as the environment in which itoperates.' (p. 107)

Yet complex systems do change state on the basis of the transformationof key parameter values. I have argued (Byrne1997) that the polarised form of cities is something that can beunderstood in terms of the transformation of parameters to do with economicengagement and power. It seems to me that abandoning the idea of controlparameters, however complex may be the interactive processes through whichthe control of these parameters are expressed, is also an abandonment ofthe possibility of effective agency based on understanding.

This tension runs through Cilliers' final chapter which deals with'Complexity and Postmodernism'. I think this is fascinating. Cilliersargues that the postmodern programme cannot be understood in purelyrelativist terms but is instead to be understood as an assertion of thelocal validity of accounts rather than their universal validity. I haveabsolutely no problem with this but it is an unusual version of thepostmodernist position, although Cilliers marshals a good number ofcitations, particularly from Derrida, in support of this positions. Thechapter includes some interesting ideas on understanding society inconnectionist and complex terms (although here the absence of a sense ofnested systems again is a weakness), a fascinating discussion of languageand some important preliminary statements about the ethical implications ofthe general argument. The element which I want to focus on is Cilliersdiscussion of the nature of the scientific project. Again, there is a greatdeal to agree with and some things I would want to argue about. Certainlythe notion that the complexity programme is fundamentally antithetical todisciplinary boundaries in science is absolutely correct. However, I thinkCilliers is hung up on, and moreover knows that he is hung up on, thedistinction between the postmodernist programme as a general deconstructionof science and the possibility of using complexity as the basis of acoherent and acceptable postmodern science. The first is the usual story.The second is the interesting one. There is a lot in this book to help usget going with that project. In an afterword called 'UnderstandingComplexity', Cilliers puts his finger right on the fundamental issue. As hesays, his work could be accused of the performative fallacy - attempting todo what he claims is not possible: generating a theory which insists onradical contingency but claims to be generally valid. Well yes, that iswhat he is doing but it is not fallacious. If we think of complexity, ofhis distributed connectionism, not as a law but as a metalaw, hereunderstood as a general statement about the complex character ofdissipative far from equilibrium systems, then we have something whichallows for the local but is general. This is entirely compatible with therealist account. Postmodernists in general don't like realism, but for mymoney Cilliers ends up as an implicit realist.

So what does this all have to say to those interested in simulatingsocieties? Well let me try to explain that by citation of resonances.Emirbayer argues (in his 'Manifesto for a Relational Sociology') that:

The imageries most often employed in speaking of transactionsare accordingly those of complex joint activity in which it makes no senseto envision constituent elements apart from the flows within which they areinvolved (and vice versa).' (Emirbayer 1997,p. 289)

The implication for me of Cilliers' arguments is that simulation isindeed a necessary part of the sociological project - he resonatesabsolutely with Abbott who remarks:

(Video) Complexity within Competition Cheer

'If I may use another forbidden word, we will have to employsimulation. Game theory will not get us very far because it isignorant, except in the most general terms, of a serious concern withstructure and with complex temporal effects. But simulations may help usunderstand the limits and possibilities of certain kinds of interactionalfields, and that would be profoundly sociological knowledge.' (Abbott 1998, pp. 176-177, with originalemphasis)

This is what the social world is like - it has to be understood andexplored in relational terms. Of course Cilliers proscribes some approachesto simulation, essentially all hard representations, and prescribes others,those based on distributed connectionism. He has persuaded me, not that Ineeded much persuading. Let me conclude. I liked this book and learned fromit. In this long review I have tried to give a reasonable representation ofthe book, based on a sampling strategy. Of course in the nature of thething, appropriately enough, it needs to be taken as a whole.

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