the new scienceOverview

Paradoxically, now that literary criticism is adopting many of the previous methods and outlooks of science, science itself is moving on. The newer sciences recognize the role of scientists in their experiments, the pervasiveness of chaotic systems, and the complex nature of brain functioning. Science is an abstraction, and for all its astonishing success, can only make models that leave out much that is important to human beings.


What does the word "science" conjure up? Slow advances by an established routine of observation and experimentation, the careful testing of hypotheses, publication of results in respectable journals, the findings validated by other workers? Certainly a good deal of science does progress by agreed procedures. Objectivity is stressed and rigorous procedures are adopted to remove experimenter bias. Whatever the field, the experiment or observations may be repeated and the same results obtained by anyone with the correct equipment and training. {1}

The end result is theories, which are independent of context. In the most compressed form, often mathematical, the theories state the relationships holding between external realities: temperature, mass, plant type, age, location, etc. {2} Much depends on the science concerned — the descriptive sciences of botany, geology and palaeontology are obviously different from chemistry, astronomy and physics, but there is general feeling that the more abstract and reductive the theories the better: the greatest number of phenomena are covered in the most elegant and fundamental fashion.

And whatever the discipline, the method certainly works. Progress in the last hundred years has been staggering, and now even psychology, medicine, sociology and anthropology strive to emulate the hard sciences, even if at long removes. Sometimes the context cannot be entirely squeezed out of the algorithm: it is important which illness, where and affecting whom is being studied. And though physics is pushing on to theories of everything, {3} — which can only be mathematical notations impossible to conceive — this option is clearly not open to the human or descriptive sciences. Indeed the human sciences are particularly resistant to a reductive approach (or, to put it another way, fail to be fully science). Anthropology employed a mathematical notation during its Structuralist phase, but the equations were soon recognized as empty window-dressing. And mundane psychology experiments are still notorious for "proving" the obvious. {4}

But even in the hard sciences, the methodology has its problems. What exactly are electrons? They behave both as particles and a wave action. Perplexingly, they disappear when they meet their opposite number, the positron. Worse still, they obey statistical laws, the Shrödinger wave equations only indicating the percentage likelihood of an electron being in a certain position with a certain speed. Of course we can rationalize the situation, say that an electron is like nothing else but an electron, and that the very act of observing upsets its speed and position. But that is not the orthodox view, or very comforting. The electron is a lepton, one of the fundamental building blocks of matter, and if these blocks do not have solid objective existence, what does? {5} The building blocks seem inter-linked in a way they should not be, moreover, seeming to communicate instantaneously — faster than the speed of light, which the General Theory of Relativity declares impossible. {6}

And matters at the other end of the scale, in astrophysics, are equally baffling. The universe may have originated out of nothing, a false vacuum collapse which co-created other universes which will always remain outside our detection. And the fabric of the universe may be constituted by superstrings, loops of incredibly small size. Originally these superstrings had 26 dimensions, but 6 have compacted to invisibility and 16 have internal dimensions to account for fundamental forces. {7} Is this credible? The theory is contested, and may indeed turn out to be pure mathematics — which is shaky in places, not only in superstrings, but generally. {8}

But if the world is stranger than we can conceive it, it is no longer in areas we cannot enter anyway, the very small or the very large. Science has traditionally dealt with reversible, linear situations: small causes which have small effects, and are totally predictable. But most of the world is not that way at all. The cup slips from our grasp at breakfast, we have a row with our partner for spoiling the new carpet, go late to the office in a foul temper, fall out with the boss, are fired, lose the home and partner and indeed everything from the most insignificant incident. And that is by no means an exceptional, one-off situation. Nonlinear situations are common enough in scientific investigations but were blithely ignored. Scientists only reported the experiments that worked, that provided the simple relationships they were looking for. {9}

The False Picture of Science

That does not imply that the investigations were cooked, though certainly the scientific paper does give a strange notion of how experiments are conducted, and perhaps even a false one. {10} But the main objection is that the idealizations represented by cause and effect models become abstract, remote and artificial. Life simply isn't as science depicts. {11} Science works very effectively in some ways, but these ways are not the natural habitat of human beings. Indeed, as we move from physics to the life sciences and then to psychology, sociology and economics, the reductionism of science increasingly fails to adequately represent matters. The current models of economics and psychology are not only much too simplistic, but tend to illustrate and take for granted what at base are only assumptions and shared procedures. A notion like "marginal utility" can never have the objective existence of the volt or carbonate diagenesis. The notion is largely an artifact of the conceptual system, and attempts to prove the notion soon dissolve into arguments about the foundations of economics itself. {12} {19}

Are the hard sciences that much better? Certainly their predictions are clearer-cut and more successful. Psychological theories are notoriously ambiguous, and economics is more trotted out for justification in business and politics than rigorously applied. But at base (though a good deal deeper down and more secure) the hard sciences themselves rest on the assumptions and procedures that form the long apprenticeships of scientists. The very building blocks of nature are nebulous concepts, and there bristle immense philosophical problems with theories, objective reality, truth, logic and mathematics. Sometimes there seems to exist no unquestionable bedrock of experience or knowledge on which science or anything else can be ultimately grounded.

Suppose that is so. Instead of continually seeking what does not exist, can we not accept that we live in a web of mutually supporting beliefs, assumptions, ways of looking at and responding to things. Does that open the floodgates to wild irrationalism, or rule out objectivity? Not at all. Derrida's deconstruction may seem to argue so, but the world still has to make sense through language, and some things cannot be argued away. Whatever reality we may chose to accord it, the world "out there" — and the way others see and respond to that world — very much constrains our own beliefs and actions. Most of what we read we have to take on trust. Private languages are unworkable, and life is much too short to investigate everything. We are born into a web of understandings, make a few modifications as need dictates, and hand the web on much as we found it.

The New Science of Complexity

A new science accepts this web-like view of the world? Called by a variety of names — study of dissipative structures, complex systems, life systems {13} — it has grown from the unexpected fusion of two very different fields. One is computer simulation of complex systems that hover on the border between chaos and regularity. The other is the behaviour of living organisms.

Complex systems are now an immense field of study, difficult to summarize briefly, but their essential feature is non-linearity. The future behaviour of the system depends on its prior behaviour and through feed-backs has an inbuilt element of randomness. Such behaviour is seen in very simple systems (e.g. one represented by X' = k x(1 - x) where x is the value initially, and X the value at a later time) but real-life examples are usually much more complicated, often resulting from the interaction of several such systems. The system will exhibit areas of simple behaviour: movement towards a single point, or oscillation between two or more points, but there will also areas of chaotic behaviour where the smallest change in prior conditions causes wild fluctuations later on. But even more characteristic of these systems is what are termed strange attractors. The system revolves round certain points, continually tracing trajectories which are very similar but never exactly identical. {14}

What has this to do with life? Certain chemical reactions behave in a similar way, and their behaviour mimics those of living systems, even though the reactions involve non-organic compounds that would individually behave quite straightforwardly. Given feedback mechanisms — and many chemical reactions are reversible — there arise areas or islands of order on the very edge of chaos. Most importantly, the systems organize themselves, automatically, out of the web of interacting reactions. They have emergent properties where behaviour is different and not to be predicted from the behaviour at a lower level.

And the significance? Living creatures may owe their structures to such self-organization of their constituent chemicals: in the metabolism of cells, brain functioning, even the way the DNA code is interpreted to produce the right sequence of cells in the growing animal. On a broader field, that of ecosystems and natural selection, it may be that species themselves represent strange attractors, with parallel evolution in the likes of whales and marsupial wolves. {15} Indeed the theory of networks can be very generally extended. Life, according to the Santiago school of Maturana and Varela, is characterized by two features: cognition and the ability to reproduce. Cognition means making distinctions and is shown by all forms of life, even the lowliest. But only man, and possibly the higher primates to some extent, know that they know, i.e. have self-awareness and an inner world. Self awareness is closely tied to language, which is not a mental representation or a transfer of information, but a coordination of behaviour. Language is a communication about communication, by which we bring forth a world, weaving the linguistic network in which we live.

At a stroke, a good deal of philosophy's aims are thrown away. Mental states embody certain sensations. Cognitive experience involves resonance — technically phase-locking — between specific cell assembles in the brain: e.g. those dealing with perception, emotion, memory, bodily movement, and also involves the whole body's nervous systems. Attempts to define, or even to illuminate, such concepts as consciousness, being, truth and ethical value are no more than knottings in the web of understanding. Words lead back to physiology and bodily functioning, not to any abstract notions based on irrefutable logic. {16}

Poems as Strange Attractors

And the relevance to literature? It may be that poems themselves are strange attractors. There are many similarities. Poems organize themselves. The writer submits words to the embryonic arrangement of the poem — a phrase, a conjectured verse form, intellectual argument, controlling emotion — but thereafter the poem takes over, creating an arrangement of words that is not easily changed. Poets often produce cycle of poems, recognizable in theme and form, but differing slightly from poem to poem. Literary periods also see these cycles of creation: a common technique or subject matter or Zeitgeist. Strange attractors have exactly these properties: similarities but not repetitions, an independence, a reluctance to shift far from their previous shape and position.

Certainly these are conjectural matters. But consider the complex systems of brain functioning, the schemas that may operate to create our sense of reality, the part which metaphors and other tropes play in literature, and there arises a possible explanation of the enormous power of poetry: its ability to recreate experience with startling vividness, to evoke deep emotions, to condense large areas of thought in compelling arrangements of a few words. And note too how the features of artworks — pleasing shape, autonomy, emotional appeal and significance — arise out the materials themselves. The artist may guide and judge, but there are no stratagems or recipes, no foolproof procedures for success. Note also that strange attractors develop on the edge of chaos, as do artistic creations, with the artist is not wholly in control. None of these is conclusive, even when taken together, but the parallels are obvious and intriguing.

A Word of Caution

What is the scientific evidence for any of this? Only a little at present, but growing fast. {17} Neural nets already have important applications. Very large computer networks can be programmed with a few simple rules, told the "correct" answers to an input problem, and be expected to automatically solve future problems. How they amalgamate their simple rules into powerful problem-solving algorithms is not entirely understood, but the systems perform perfectly well in areas as diverse as predicting airline seat demand to screening cervical smears for cancer. {18}

But the importance of these approaches should not be overestimated. Science is an intensely conservative activity, and most science is and will be conducted along previous lines. Neural nets may give the right answer, but most scientists insist on knowing why. Many of these so-called approaches seem only vague analogies, and scientists will not abandon tried and tested methodologies for unquantifiable speculation. Philosophy itself will continue to probe the bases of science. Neural scientists may see meaning and truth and representation as artifacts of language, but they are nonetheless concepts we are accustomed to using. Many of the Santiago school and their popularizers seem philosophically naive, unaware of the problems met and unresolved by linguistic philosophy.

Some Concluding Thoughts

Nonetheless, there are now grounds for hoping that the three-century-old split between the arts and sciences may slowly be coming to an end. No fundamental divide separates reason and emotion, and poetry cannot be written off as emotive expression. The figurative nature of language, which the Royal Society and later science ignored, is once again emphasized by the new science and by metaphor theory. How we express something is part of its content, as surely in science as in literature. Quantitative methods will continue in literary and historical studies, but their "objectivity" may be no more than a local knotting of common beliefs and practices.

This and other pages in the theory section have been collected into a free pdf ebook entitled 'A Background to Literary Theory'. Click here for the download page.  


1. John Losee's Philosophy of Science (1993), and M.J. Mulkay's Science and the Sociology of Knowledge (1979).
2. Many popular introductions exist. See: Robert Matthews's Unravelling the Mind of God: Mysteries at the Frontiers of Science (1992.
3. John Barrow's Theories of Everything: The Quest for Ultimate Explanation (1991).
4. See any introduction to behavioural psychology.
5. Quantum physics is covered by many textbooks and popular accounts. See, for example, Chapter 5 of Andrew Scott's Basic Nature (1991) or P. Davies and J. Grubbing's The Matter Myth (1991).
6. pp. 139-143 in Matthews 1992.
7. Chapter 5 and 6 in Matthews 1992.
8. John Allen Paulos's Beyond Numeracy: An Uncommon Dictionary of Mathematics (1991), and Ian Stewart's The Problems of Mathematics (1987). Morris Kline's Mathematics and the Search for Knowledge (1986) is more advanced. See pp. 148-52 in Bryan Bunch's Mathematical Fallacies and Paradoxes for a kindergarten introduction to Gödel's theorem.
9. J. Briggs and F.D. Peat's The Turbulent Mirror (1989), and R. Lewis's Complexity (1993).
10. Alan Gross's The Rhetoric of Science. (1996).
11. Fritof Capra's The Web of Life: A New Synthesis of Mind and Matter (1996) and bibliography.
12. G. Routh's The Origin of Economic Ideas. (1977) and G. Brockway's The End of Economic Man. (1991).
13. Peter Coveney and Roger Highfield's Frontiers of Complexity: The Search for Order in a Chaotic World (1995).
14. Briggs and Peat 1991. More advanced texts are: F. Falconer's Fractal Geometry: Mathematical Foundations and Applications (1990) and Denny Gulick's Encounters with Chaos (1992).
15. Stuart Kauffman's At Home in the Universe: The Search for Laws of Complexity (1995), Roger Lewin's Complexity: Life at the Edge of Chaos (1993), and Chapter 7 of Coveney and Highfield 1995.
16. Chapters 11 and 12 of Capra 1996.
17. Coveney and Highfield 1995. Also Edgar E. Peter's Chaos and Order in the Capital Markets: A New View of Cycles, Prices and Market Volatility (1991) for a financial application.
18. Laurene Fausett's Fundamentals of Neural Networks (1994), and pp. 130-148 in Coveny and Highfield 1995.
19. Neoclassical Economics. C.J. Holcombe. Ecommerce Digest. October 2013.

Internet Resources

1. How Science Works. David Goldstein.
. Standard but cogent account.
2. Bell's Theorem. David M. Harrison. Feb. 1999. Harrison/BellsTheorem/BellsTheorem.html. More detailed account, with interpretations.
3. Identity and Individuality in Quantum Theory. Steven French. Feb. 2000. Metaphysical implications of quantum physics.
4. Holism and Nonseparability in Physics. Richard Healey. Jul. 1999. Technical entry on the properties of quantum systems.
5. Ilya Prigogine. Brief account, with in-text links.
6. Ilya Prigogine. Oct. 2002. notebooks/prigogine.html. Critical account of Prigogine's work
7. Chaos and Fractals. Larry Bradley. Introductory articles, references and links.
8. Strange Attractors: Creating Patterns in Chaos. Julien C. Sprott. Book chapters can be loaded down free as MS Word documents.
9. Complexity, Complex Systems & Chaos Theory Organizations as Self-Adaptive Complex Systems. Business applications of chaos theory.
10. Book of papers about Maturana's ideas. Lloyd Fell, David Russell & Alan Stewart (Eds.) Jan 1998. A good range of ideas discussed.
11. Strange & Complex. Glenn Elert . 2003. Excerpt from The Chaos Hypertextbook: straightforward but needs a little maths..
12. Human Values as Strange Attractors. Anthony Judge. Aug. 1993. Simple, non-mathematical treatment.
13. The Fuzziness of Communication A Catalyst for Seeking Consensus. Vladimir Dimitrov and David Russell. 1994. Metaphor, fuzzy logic and similarity to strange attractors.
14. Neural Networks Warehouse. Commercial site but has excellent tutorials, books, articles and a wide range of software, some free.

      C. John Holcombe   |  About the Author    | ©     2007 2012 2013 2015.   Material can be freely used for non-commercial purposes if cited in the usual way.