The Complexity Revolution
Authors: Hodge Bob 
Published: October 2020

‘Complex(ity)’ is currently fashionable in the humanities. Fashions come and go, but in this article I argue that the interest in complexity connects with something deeper, an intellectual revolution that began before complexity became trendy, and will continue after the spotlight passes on. Yet to make this case, and understand and advance this revolution, we need a better take on ‘complexity’.

‘Complex’ is of course complex. In common use it refers to something ‘composed of many interrelated parts’, or problems ‘so complicated or intricate as to be hard to deal with’. I will call this popular meaning, with its positive and negative values, complexity-1. In science it has a more negative sense, complexity-2, referring to the presenting complexity of problems, which science will strip down to underlying simplicity. But recently it has developed positive meanings in both science and humanities. Complexity-3 marks a revolutionarily more positive attitude to complexity in science that does seek to be reductive. Humanities-style complexity-4, which acknowledges and celebrates the inherent complexity of texts and meanings, is basic in contemporary Media and Cultural studies (MaC for short).

The underlying root of complex is plico bend or fold, plus con- together, via complector grasp (something), encompass an idea, or person. The double of ‘complex’ is ‘simple’, from Latin simplex, which less obviously also comes from plico, plus semel once, at the same time. ‘Simple’ and ‘complex’ are closer than people think: only a fold or two apart.

A key idea is that these elements are interdependent, parts of a single underlying form. ‘Simple(x)’ is another modality of ‘complex’, dialectically related, different in degree not kind, not absolutely opposite. The idea of ‘holding together’ is stronger in Latin complex, the idea of difficulty more prominent in modern usage, yet the term still includes both. The concept ‘complex’ is untenable apart from ‘simple’.

This figure maps the basic structures in ‘complexity’. This complexity contains both positive and negative values, science and non-science, academic and popular meanings, with folds/differences and relationships so dynamically related that no aspect is totally independent. This complex field is the minimum context in which to explore claims about a ‘complexity revolution’.

Complexity in Science and Humanities

In spite of the apparent similarities between Complexity-3 (sciences) and 4 (humanities), in practice a gulf separates them, policed from both sides. If these sides do not talk to each other, as they often do not, the result is not a complex meaning for ‘complex’, but a semantic war-zone.

These two forms of complexity connect and collide because they reach into a new space where discourses of science and non-science are interacting more than they have for many years. For many, in both academic communities, a strong, taken-for-granted mindset declares the difference between them is absolute. They assume that if ‘complexity’ exists in science, it must mean something completely different from what it means in humanities or everyday discourse, so different as to be incomprehensible or unusable by humanists. This terrified defence of the traditional gulf between sciences and humanities is not the clinching argument these critics think. On the contrary, it symptomises what needs to be challenged, via the concept complex.

One influential critic of this split was Lord Snow, who talked of ‘two cultures’. Writing in class-conscious post-war Britain he regretted the ignorance of humanities-trained ruling elites about basic science, and scientists’ ignorance of humanities. No-one then or now doubts there is a problem. Most MaC students have a science-light education, and feel vulnerable to critiques which say they do not need to know any science or maths, including complexity science, and could not understand it anyway.

To understand how this has happened I go back to the 17th century rise of ‘modern science’. The Royal Society then included the poet Dryden as well as the scientist Newton, but already the fissure between science and humanities was emerging in the elite, re-enforcing existing gaps between both these and technology. The three forms of knowledge and their communities continued to develop over the next 400 years, producing the education system which formed most of us, the structure of academic knowledges in which culture, technology and science form distinct fields.

Complexity has been implicated in this three-way split. Influenced by Newton’s wonderful achievement, explaining so much (movements of earthly and heavenly bodies) with so little (three elegant laws of motion, one brief formula), science defined itself as a reductive practice, in which complexity was a challenge. Simplicity was the sign of a successful solution, altering the older reciprocity between simplicity and complexity. The paradox was ignored that proof involved highly complex mathematics, as anyone who reads Newton knows. What science held onto was the outcome, a simplicity then retrospectively attributed to the universe itself, as its true nature. Simplicity became a core quality in the ontology of science, with complexity-2 the imperfection which challenged and provoked science to eliminate it.

Humanities remained a refuge for a complexity ontology, in which both problems and solutions were irreducibly complex. Because of the dominance of science as a form of knowing, the social sciences developed a reductivist approach opposing traditional humanities. They also waged bitter struggles against anti-reductionists who emerged in what was called ‘social theory’.

Complexity-4 in humanities is often associated with ‘post-structuralism’, as in Derrida, who emphasises the irreducible complexity of every text and process of meaning, or ‘postmodernism’, as in Lyotard’s controversial, influential polemic. Lyotard attempted to take the pulse of contemporary Western thought. Among trends he noted were new forms of science, new relationships between science and humanities, and a new kind of logic pervading all branches of knowledge.

Not all Lyotard’s claims have worn well, but his claim that something really important is happening in the relationship between kinds and institutions of knowledge, especially between sciences and humanities, is worth serious attention. Even classic sociologists like Durkheim recognised that the modern world is highly complex. Contemporary sociologists agree that ‘globalisation’ introduces new levels of complexity in its root sense, interconnections on a scale never seen before. Urry argues that the hyper-complexity of the global world requires a complexity approach, combining complexity-3 and 4. Lyotard’s ‘postmodernism’ has too much baggage, including dogmatic hostility to science. Humanities complexity-4 has lost touch with the sceptical side of popular complexity-1, and lacks a dialectic relationship with simplicity. ‘Complexity’, incorporating Complexity-1 and 3, popular and scientific, made more complex by incorporating humanities complexity-4, may prove a better concept for thinking creatively and productively about these momentous changes. Only complex complexity in the approach, flexible and interdisciplinary, can comprehend these highly complex new objects of knowledge.

Complexity and the New Condition of Science

Some important changes in the way science is done are driven not from above, by new theories or discoveries, but by new developments in social contexts. Gibbons and Nowottny identify new forms of knowledge and practice, which they call ‘mode-2 knowledge’, emerging alongside older forms. Mode-1 is traditional academic knowledge, based in universities, organised in disciplines, relating to real-life problems at one remove, as experts to clients or consultants to employers. Mode-2 is orientated to real life problems, interdisciplinary and collaborative, producing provisional, emergent knowledge.

Gibbons and Nowottny do not reference postmodernism but are looking at Lyotard’s trends as they were emerging in practice 10 years later. They do not emphasise complexity, but the new objects of knowledge they address are fluid, dynamic and highly complex. They emphasise a new scale of interdisciplinarity, in collaborations between academics across all disciplines, in science, technology, social sciences and humanities, though they do not see a strong role for humanities.

This approach confronts and welcomes irreducible complexity in object and methods. It takes for granted that real-life problems will always be too complex (with too many factors, interrelated in too many ways) to be reduced to the sort of problem that isolated disciplines could handle. The complexity of objects requires equivalent complexity in responses; teamwork, using networks, drawing on relevant knowledge wherever it is to be found.

Lyotard famously and foolishly predicted the death of the ‘grand narrative’ of science, but Gibbons and Nowottny offer a more complex picture in which modes-1 and 2 will continue alongside each other in productive dialectic. The linear form of science Lyotard attacked is stronger than ever in some ways, as ‘Big Science’, which delivers wealth and prestige to disciplinary scientists, accessing huge funds to solve highly complex problems with a reductionist mindset. But governments also like the idea of mode-2 knowledge, under whatever name, and try to fund it despite resistance from powerful mode-1 academics.

Moreover, non-reductionist science in practice has always been more common than the dominant ideology allowed, whether or not its exponents, some of them eminent scientists, chose to call it ‘complexity’ science. Quantum physics, called ‘the new physics’, consciously departed from the linear, reductionist assumptions of Newtonian physics to project an irreducibly complex picture of the quantum world. Different movements, labelled ‘catastrophe theory’, ‘chaos theory’ and ‘complexity science’, emerged, not a single coherent movement replacing the older reductionist model, but loosely linked by new attitudes to complexity. Instead of seeing chaos and complexity as problems to be removed by analysis, chaos and complexity play a more ambiguous role, as ontologically primary. Disorder and complexity are not later regrettable lapses from underlying essential simplicity and order, but potentially creative resources, to be understood and harnessed, not feared, controlled, eliminated.

As a taste of exciting ideas on complexity, barred from humanities MaC students by the general prohibition on ‘consorting with the enemy’ (science), I will outline three ideas, originally developed in complexity-3, which can be described in ways requiring no specialist knowledge or vocabulary, beyond a Mode-2 openness to dynamic, interdisciplinary engagement.

Fractals, a term coined by mathematician Benoit Mandelbrot, are so popular as striking shapes produced by computer-graphics, circulated on T-shirts, that they may seem superficial, unscientific, trendy. They exist at an intersection between science, media and culture, and their complexity includes transactions across that folded space. The name comes from Latin fractus, broken: irregular shapes like broken shards, which however have their own pattern. Mandelbrot claims that in nature, many such patterns partly repeat on different scales. When this happens, he says, objects on any one scale will have equivalent complexity.

Part of this idea is contained in Blake’s famous line: ‘To see the world in a grain of sand’. The importance of the principle is that it fundamentally challenges reductiveness. Nor is it as unscientific as it may sound. Geologists indeed see grains of sand under a microscope as highly complex. In sociology, instead of individuals (literal meaning ‘cannot be divided’) being the minimally simple unit of analysis, individuals can be understood to be as complex (e.g. with multiple identities, linked with many other social beings) as groups, classes or nations. There is no level where complexity disappears.

A second concept is ‘fuzzy logic’, invented by an engineer, Zadeh. The basic idea is not unlike the literary critic Empson’s ‘ambiguity’, the sometimes inexhaustible complexity of meanings in great literature. Zadeh’s contribution was to praise the inherent ambiguity and ambiguity of natural languages as a resource for scientists and engineers, making them better, not worse, for programming control systems. Across this apparently simple bridge have flowed many fuzzy machines, more effective than their over-precise brothers. Zadeh crystallised this wisdom in his ‘Principle of incompatibility’:

As the complexity of a system increases, our ability to make precise and yet significant statements about its behaviour decreases until a threshold is reached beyond which precision and significance (or relevance) become almost mutually exclusive characteristics (28)

Something along these lines is common wisdom in complexity-1. For instance, under the headline “Law is too complex for juries to understand, says judge” (Dick 4), the Chief Justice of Australia, Murray Gleeson, noted a paradox of complexity, that attempts to improve a system by increasing its complexity make it worse (meaningless or irrelevant, as Zadeh said). The system loses its complexity in another sense, that it no longer holds together.

My third concept is the ‘Butterfly Effect’, a name coined by Lorenz. The butterfly was this scientist’s poetic fantasy, an imagined butterfly that flaps its wings somewhere on the Andes, and introduces a small change in the weather system that triggers a hurricane in Montana, or Beijing. This idea is another riff on the idea that complex situations are not reducible to component elements. Every cause is so complex that we can never know in advance just what factor will operate in a given situation, or what its effects might be across a highly complex system.

Travels in Complexity

I will now explore these issues with reference to a single example, or rather, a nested set of examples, each (as in fractal theory) equivalently complex, yet none identical at any scale. I was travelling in a train from Penrith to Sydney in New South Wales in early 2006 when I read a publicity text from NSW State Rail which asked me: ‘Did you know that delays at Sydenham affect trains to Parramatta? Or that a sick passenger on a train at Berowra can affect trains to Penrith?’

No, I did not know that. As a typical commuter I was impressed, and even more so as an untypical commuter who knows about complexity science. Without ostentatious reference to sources in popular science, NSW Rail was illustrating Lorenz’s ‘butterfly effect’. A sick passenger is prosaic, a realistic illustration of the basic point, that in a highly complex system, a small change in one part, so small that no-one could predict it would matter, can produce a massive, apparently unrelated change in another part.

This text was part of a publicity campaign with a scientific complexity-3 subtext, which ran in a variety of forms, in their website, in notices in carriages, on the back of tickets. I will use a complexity framework to suggest different kinds of analysis and project which might interest MaC students, applicable to objects that may not refer to be complexity-3.

The text does two distinct things. It describes a planning process, and is part of a publicity program. The first, simplifying movement of Mode-1 analysis would see this difference as projecting two separate objects for two different specialists: a transport expert for the planning, a MaC analyst for the publicity, including the image. Unfortunately, as Zadeh warned, in complex conditions simplification carries an explanatory cost, producing descriptions that are meaningless or irrelevant, even though common sense (complexity-1) says otherwise.

What do MaC specialists know about rail systems? What do engineers know about publicity? But collaboration in a mode-2 framework does not need extensive specialist knowledge, only enough to communicate with others. MaC specialists have a fuzzy knowledge of their own and other areas of knowledge, attuned by Humanities complexity-4 to tolerate uncertainty. According to the butterfly principle it would be foolish to wish our University education had equipped us with the necessary other knowledges. We could never predict what precise items of knowledge would be handy from our formal and informal education. The complexity of most mode-2 problems is so great that we cannot predict in advance what we will need to know.

MaC is already a complex field, in which ‘Media’ and ‘Culture’ are fuzzy terms which interact in different ways. Media and other organisations we might work with are often imbued with linear forms of thought (complexity-2), and want simple answers to simple questions about complex systems. For instance, MaC researchers might be asked as consultants to determine the effect of this message on typical commuters. That form of analysis is no longer respectable in complexity-4 MaC studies. Old-style (complexity-2) effects-research modelled Senders, Messages and Receivers to measure effects. Standard research methods of complexity-2 social sciences might test effects of the message by a survey instrument, with a large sample to allow statistically significant results. Using this, researchers could claim to know whether the publicity campaign had its desired effect on its targeted demographic: presumably inspiring confidence in NSW Rail.

However, each of these elements is complex, and interactions between them, and others that don’t enter into the analysis, create further levels of complexity. To manage this complexity, MaC analysts often draw on Foucault’s authority to use ‘discourse’ to simplify analysis. This does not betray the principle of complexity. Complexity-4 needs a simplicity-complexity dialectic. In this case I propose a ‘complexity discourse’ to encapsulate the complex relations between Senders, Receivers and Messages into a single word, which can then be related to other such elements (e.g. ‘publicity discourse’).

In this case complexity-3 can also be produced by attending to details of elements in the S-M-R chain, combining Derridean ‘deconstruction’ with expert knowledge of the situation. This Sender may be some combination of engineers and planners, managers who commissioned the advertisement, media professionals who carried it out. The message likewise loses its unity as its different parts decompose into separate messages, leaving the transaction a fraught, unpredictable encounter between multiple messages and many kinds of reader and sender.

Alongside its celebration of complexity-3, this short text runs another message: ‘untangling our complex rail network’. This is complexity-2 from science and engineering, where complexity is only a problem to be removed. A fuller text on the web-site expands this second strand, using bullet points and other signals of a linear approach. In this text, there are 5 uses of ‘reliable’, 6 uses of words for problems of complexity (‘bottlenecks’, ‘delays’, ‘congestion’), and 6 uses of words for the new system (‘simpler’, ‘independent’). ‘Complex’ is used twice, both times negatively. In spite of the impression given by references to complexity-3, this text mostly has a reductionist attitude to complexity. Complexity is the enemy.

Then there is the image. Each line is a different colour, and they loop in an attractive way, seeming to celebrate graceful complexity-2. Yet this part of the image is what is going to be eliminated by the new program’s complexity-2. The interesting complexity of the upper part of the image is what the text declares is the problem. What are commuters meant to think? And Railcorp?

This media analysis identifies a fissure in the message, which reflects a fissure in the Sender-complex. It also throws up a problem in the culture that produced such interesting allusions to complexity science, but has linear, reductionist attitudes to complexity in its practice. We can ask: where does this cultural problem go, in the organisation, in the interconnected system and bureaucracy it manages? Is this culture implicated in the problems the program is meant to address? These questions are more productive if asked in a collaborative mode-2 framework, with an organisation open to such questions, with complex researchers able to move between different identities, as media analyst, cultural analyst, and commuter, interested in issues of organisation and logistics, engaged with complexity in all senses.

I will continue my imaginary mode-2 collaboration with Railcorp by offering them another example of fractal analysis, looking at another instant, captured in a brief media text. On Wednesday 14 March, 2007, two weeks before a State government election, a very small cause triggered a systems failure in the Sydney network.

A small carbon strip worth $44 which was not properly attached properly threw Sydney’s transport network into chaos on Wednesday night, causing thousands of commuters to be trapped in trains for hours. (Baker and Davies 7)

This is an excellent example of a butterfly effect, but it is not labelled as such, nor regarded positively in this complexity-1 framework. ‘Chaos’ signifies something no-one wants in a transport system. This is popular not scientific reductionism.

The article goes on to tell the story of one passenger, Mark MacCauley, a quadriplegic left without power or electricity in a train because the lift was not working. He rang City Rail, and was told that “someone would be in touch in 3 to 5 days” (Baker and Davies 7). He then rang emergency OOO, and was finally rescued by contractors “who happened to be installing a lift at North Sydney” (Baker and Davies 7).

My new friends at NSW Rail would be very unhappy with this story. It would not help much to tell them that this is a standard ‘human interest’ article, nor that it is more complex than it looks. For instance, MacCauley is not typical of standard passengers who usually concern complexity-2 planners of rail networks. He is another butterfly, whose specific needs would be hard to predict or cater for. His rescue is similarly unpredictable. Who would have predicted that these contractors, with their specialist equipment, would be in the right place at the right time to rescue him? Complexity provided both problem and solution. The media’s double attitude to complexity, positive and negative, complexity-1 with a touch of complexity-3, is a resource which NSW Rail might learn to use, even though it is presented with such hostility here.

One lesson of the complexity is that a tight, linear framing of systems and problems creates or exacerbates problems, and closes off possible solutions. In the problem, different systems didn’t connect: social and material systems, road and rail, which are all ‘media’ in McLuhan’s highly fuzzy sense. NSW Rail communication systems were cumbrously linear, slow (3 to 5 days) and narrow. In the solution, communication cut across institutional divisions, mediated by responsive, fuzzy complex humans. If the problem came from a highly complex system, the solution is a complex response on many fronts: planning, engineering, social and communication systems open to unpredictable input from other surrounding systems.

As NSW Rail would have been well aware, the story responded to another context. The page was headed ‘Battle for NSW’, referring to an election in 2 weeks, in which this newspaper editorialised that the incumbent government should be thrown out. This political context is clearly part of the complexity of the newspaper message, which tries to link not just the carbon strip and ‘chaos’, but science and politics, this strip and the government’s credibility.

Yet the government was returned with a substantial though reduced majority, not the swingeing defeat that might have been predicted by linear logic (rail chaos = electoral defeat) or by some interpretations of the butterfly effect. But complexity-3 does not say that every small cause produces catastrophic effects. On the contrary, it says that causal situations can be so complex that we can never be entirely sure what effects will follow from any given case. The political situation in all its complexity is an inseparable part of the minimal complex situation which NSW Rail must take into account as it considers how to reform its operations. It must make complexity in all its senses a friend and ally, not just a source of nasty surprises.

My relationship with NSW Rail at the moment is purely imaginary, but illustrates positive and negative aspects of complexity as an organising principle for MaC researchers today. The unlimited complexity of Humanities’ complexity-4, Derridean and Foucauldian, can be liberating alongside the sometimes excessive scepticism of Complexity-2, but needs to keep in touch with the ambivalence of popular complexity-1. Complexity-3 connects with complexity-2 and 4 to hold the bundle together, in a more complex, cohesive, yet still unstable dynamic structure. It is this total sprawling, inchoate, contradictory (‘complex’) brand of complexity that I believe will play a key role in the up-coming intellectual revolution. But only time will tell.

References

Baker, Jordan, and Anne Davies. “Carbon Strip Caused Train Chaos.” Sydney Morning Herald 17 Mar. 2007: 7.
Derrida, Jacques. Of Grammatology. Baltimore: Johns Hopkins, 1976.
Dick, Tim. “Law Is Now Too Complex for Juries to Understand, Says Judge.” Sydney Morning Herald 26 Mar. 2007: 4.
Empson, William. Seven Types of Ambiguity. London: Chatto and Windus, 1930.
Foucault, Michel. “The Order of Discourse.” In Archaeology of Knowledge, trans. A.M Sheridan Smith. London: Tavistock, 1972.
Gibbons, Michael. The New Production of Knowledge. London: Sage, 1994.
Lorenz, Edward. The Essence of Chaos. London: University College, 1993.
Lyotard, Jean-Francois. The Postmodern Condition. Manchester: Manchester UP, 1984.
McLuhan, Marshall. Understanding Media. London: Routledge, 1964.
Mandelbrot, Benoit. “The Fractal Geometry of Nature.” In Nina Hall, ed. The New Scientist Guide to Chaos. Harmondsworth: Penguin, 1963.
Nowottny, Henry. Rethinking Science. London: Polity, 2001.
Snow, Charles Percy. The Two Cultures and the Scientific Revolution. London: Faber 1959.
Urry, John. Global Complexity. London: Sage, 2003.
Zadeh, Lotfi Asker. “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes.” ILEE Transactions on Systems, Man, and Cybernetics 3.1 (1973): 28-44.

Citation reference for this article

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