LA TEORÍA DEL CAOS Y LA GESTIÓN DE LAS ORGANIZACIONES ANDRES UBIERNA • •
Septiembre 2009
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Contacto: ubierna.andres@gmail.com http://andresubierna.com
Letting Go : Chaos theory and the management of organisations By Richard Tiplady “This organisation runs like clockwork; it’s always going round in circles” In our attempts to understand how to manage organisations, we are often more affected by scientific thinking than we realise. The idea of the world as a great machine, operating in an orderly fashion according to it’s maker’s instructions, leads us easily to the idea that the best human organisations also operate in the same manner. Organisations will run smoothly, we assume, if each part is in its correct place, operating to its optimum efficiency, well oiled and ticking over nicely. The scientific paradigm created by Newton and Descartes told us that the natural state for any system is equilibrium. Any departures from this would be damped out. If you try to interrupt a pendulum swing by giving it a push, it will soon revert to its natural rhythm. We also learned that the best way to understand any system was to look for cause and effect, and to reduce it to its component parts (which is why now have huge particle accelerators looking for ever smaller particles in the hope that this will tell us how the world works). Management theory in the early twentieth century took the principles of equilibrium, reductionism and cause and effect for granted. Henry Ford is credited with applying them very successfully to the manufacture of cars. Management theorists used the metaphor of “organisation as machine” to develop control systems of planning, budgeting and management-by-objectives. Division of labour, interchangeability of parts, standard procedures and quality control are all the fruit of the scientific rational approach to management, and it has delivered some very good results. As Chinese writer Lin Yutang wrote in 1938, “I always rely on American water taps, rather than on those made in China, because American water taps do not leak”. There’s a joke that illustrates this way of thinking. A vicar is walking through his parish, and stops to chat to one of his parishioners who is working in his garden. The garden is a masterpiece, obviously the result of many years of hard work and loving care. “Ah”, say the vicar, “it’s amazing what can be achieved when God and man work together in harmony”. “That’s right”, replied the gardener. “You should have seen the mess this garden was in when God looked after it all by himself”. Slightly less funny is the world envisaged by writer Aldous Huxley in his 1932 novel “Brave New World”. The novel is set in the year 632 AF (After Ford), in a world in which all of humanity has been engineered into different social classes, ranging from the elite Alpha-Plus to the semi-moronic Epsilon-Minus. All of society’s activities are directed towards the maintainance of stability and equilibrium. As World Controller Mustapha Mond puts it in the book, “We have our stability to think of. We don’t want to change. Every change is a menace to stability”1. The problem is, the real world isn’t like a carefully-tended garden or a well-maintained machine. It is messy, turbulent and chaotic. We can’t predict the weather a week ahead. Rainforests are incredible systems of complexity that allow life to thrive in far greater abundance and diversity than any carefully-tended garden. As James Gleick puts it, we see “pattern born amid formlessness : that is biology’s beauty and its basic mystery. Life sucks order from a sea of disorder”2. Arthur Battram describes the “real world” as the place where ordered systems do not collapse into chaos if left alone, and biological metaphors, not machine metaphors, are the starting point for engaging with the real world3. Chaos theory4 was developed first in mathematics, physics and biology in an attempt to understand and explain the patterns of order and disorder that we encounter in the world.
People familiar with Newton’s Second Law of Thermodynamics, and the law of entropy, can sometimes struggle with the concept of chaos. The principle that everything tends towards disorder is firmly established in nonscientific culture, and feels intuitively right (have you looked in your child’s bedroom lately?). In thermodynamics, this is a rule that is always true, People familiar with Newton’s Second Law of Thermodynamics, and the law of entropy, can but it has taken onwith a life removed fromThe itsprinciple oringinal context. Entropy, or the tendency sometimes struggle the far concept of chaos. that everything tends towards towards disorder, has been blamed for the disintegration of society and family disorder is firmly established in nonscientific culture, and feels intuitively right (have you life, and reflects illusion that ‘the people’ strong leaders impose order to looked inthe yourfascist/communist child’s bedroom lately?). In thermodynamics, thisneed is a rule that is always to true, protect themselves. inits our world,context. both inEntropy, nature or and human behaviour, but it hasthem takenfrom on a life far removedBut from oringinal theintendency towards disorder, hasflourishes. been blamed for the disintegration of society family forms life, andpatterns. chaos/complexity Self-organisation is the norm.and Natures reflects the fascist/communist illusion that ‘the people’ need strong leaders to impose order to
protectI them themselves. But in our world, in nature and inelements human behaviour, What hopefrom to do in this short paper is to both identify the main of chaos theory, and to chaos/complexity flourishes. Self-organisation is the norm. Natures forms patterns. consider how they apply to human behaviour, so that we can draw out lessons for the management of organisations that reflect the “real world” of complexity and chaos, not the What I hope to do in this short paper is to identify the main elements of chaos theory, and to ideal (and terrifying) one of complete order and social control. consider how they apply to human behaviour, so that we can draw out lessons for the management of organisations that reflect the “real world” of complexity and chaos, not the
Stable chaos ideal (and terrifying) one of complete order and social control.
Chaos theory is based on the recognition that real world systems never settle down into a Stable chaos steady state. The Newtonian goal of equilibrium is a fallacy. The weather never settles down Chaos theory cyclical is based patterns. on the recognition real world fluctuate systems never settle down into a into steady, Animalthat populations constantly. All systems go through steady state. The Newtonian equilibriumalways is a fallacy. The weather settles down continual patterns of ordergoal andof disorder, changing, nevernever repeating. And yet this is not into steady, cyclicalutter patterns. Animal constantly. Allinsystems go through simple disorder, chaos withpopulations no patternfluctuate whatever. ‘Chaos’ its technical meaning continual patterns of order and disorder, always changing, never repeating. And yet this is not exhibits a kind of stability within instability. simple disorder, utter chaos with no pattern whatever. ‘Chaos’ in its technical meaning exhibits a kind of stability within instability.
In 1960, meteorologist Edward Lorenz, one of the first scientists to explore the emergence of patterns in chaos, found that complex systems “stayed within certain bounds, never ‘running In 1960, meteorologist Edward Lorenz, one of the first scientists to explore the emergence of off the page’ but never repeating either”. His findings “signalled pure disorder, since no point patterns in chaos, found that complex systems “stayed within certain bounds, never ‘running or ever recurred. it also signalled a new kind ofsince order” . Mathematician off patterns the page’ of butpoints never repeating either”. Yet His findings “signalled pure disorder, no 5point 5 or patternsSmale, of pointsworking ever recurred. Yet it the alsosame signalled a new kindthat of order” . Mathematician Stephen at around time, noted chaos and instability were not Stephen Smale, working around the same time, noted that chaos and instability were not brand of the same things at all.atChaotic systems could be stable, with their own particular the same things at all. Chaotic stable, with their ownas particular brand of irregularity persisting in thesystems face ofcould smallbedisturbances. Just a regular pendulum returns to irregularity persisting in the face of small disturbances. Just as a regular pendulum returns to returned to its ordered beat after being given an extra push, so ‘chaotic’ patterns of behaviour its orderedafter beatbeing after being given an extra push, so ‘chaotic’ patterns of behaviour returned to ‘normal’ interrupted. ‘normal’ after being interrupted.
Philip Marcus, a NASA astronomer, did some computer modelling of
Philip Marcus, a NASA astronomer, didEdward someLorenz’s computer modelling of Jupiter’s Red Spot in the early 1980s using work, and noted “youRed see this spot,1980s happyusing as a clam amid the small-scale Jupiter’s Spotlarge-scale in the early Edward Lorenz’s work, and chaotic“you flow. see The this spot large-scale is a self-organising created andamid regulated by noted spot, system, happy as a clam the small-scale the sameflow. nonlinear twistsisthat create the unpredictable turmoil around it.regulated It chaotic The spot a self-organising system, created and by 1 is stable . the samechaos” nonlinear twists that create the unpredictable turmoil around it. It is stable chaos”1. Strange attractors – the channelling and constraining of disorder
Strange attractors – the channelling and constraining of disorder
So where does this self-generated order come from? How does disorder get channeled or constrained to develop its own oder, without any kind of imposition from outside? In chaos So where does this self-generated order come from? How does disorder get channeled or theory, disorder turns into chaos because of ‘strange attractors’. All systems have attractors – constrained toadevelop own oder, kind of imposition from outside? In chaos the attractor for swinging its pendulum is thewithout point atany which is hangs down, completely still, theory, disorder turns chaos because ‘strange attractors’. systems with no motion. This is theinto point toward which theofsystem is moving, and atAll which, if no have attractors – the attractor for aforce swinging pendulum is the point which attractors is hangsare down, completely still, additional external is applied, it will eventually arrive.atStrange similar and with no –motion. This isunstable the point toward within whichcertain the system different they constrain behaviour limits. is moving, and at which, if no
additional external force is applied, it will eventually arrive. Strange attractors are similar and Strange attractors are characterised by two featureswithin – they are stable (they represent the final different – they constrain unstable behaviour certain limits. state of any dynamical system in a noisy world), and they are non-periodic (they never repeat
themselves, and don’tare fall characterised into any kind of steady Otto Strange attractors by twograndfather-clock features – theytype areequilibrium). stable (they represent the final Rössler commented that “nature does something against its own will, and produces beauty”. state of any dynamical system in a noisy world), and they are non-periodic (they never repeat themselves, and don’t fall into any kind of steady grandfather-clock type equilibrium). Otto 5 Gleick, ibid, p30 Rössler commented that “nature does something against its own will, and produces beauty”.
Through strange attractors, nature is constrained. Disorder is channeled into patterns which, while never self-repeating, represent some underlying theme. We need to think of strange attractors as more like a boat drifting in a slow current on a wide
Through strange attractors, nature is constrained. Disorder is channeled into patterns which, while never self-repeating, represent some underlying theme.
river, rathertothan a of magnet drawing iron as filings towards itself.drifting The source of the pattern not We need think strange attractors more like a boat in a slow current on is a wide easy to discern, but it is clearly evident in its results. river, rather than a magnet drawing iron filings towards itself. The source of the pattern is not easy to discern, but it is clearly evident in its results. Strange attractors in organisations include the values, goals and leadership styles Strange attractors other in organisations include values, goals and leadership adopted. Sometimes attractors, such as the informal leadership provided by styles adopted. Sometimes otherany attractors, such role, as informal leadership provided by the someone who does not have such formal can override and out-influence someone who both doesinnot have any formal role,We canneed override and to out-influence formal attractors, positive andsuch negative ways. to learn look for the the formal attractors, both in positive and negative ways. We need to learn to look for the strange attractors at work in our organisations, recognising thatthat they willwill often be be hard strange attractors at work in our organisations, recognising they often hard to isolate but but have a real influence nonetheless. to isolate have a real influence nonetheless. Similarly, the the rolerole of managers should not not be to but but to function as the team Similarly, of managers should bedirect, to direct, to function as the team attractor, setting ‘rules’ or conditions allow appropriate behaviours attractor, setting the the ‘rules’ or conditions thatthat allow the the appropriate behaviours andand outcomes to emerge. This would offer a real world example of self-organisation, outcomes to emerge. This would offer a real world example of self-organisation, providing the opportunity for an open and adaptable form of teamwork, where people providing thethemselves opportunitywithin for anclear openboundaries and adaptable form of teamwork, where people manage or according to clear terms or attractors. manage themselves within clear boundaries or according to clear terms or attractors. Bifurcations – stability and instability Bifurcations – stability and instability In the 1970s, ecologists began to see patterns that other scientists had not been able to. They Inused the 1970s, ecologists began seeand patterns that scientists not beeninable to. They mathematical models to to track predict theother ebb and flow ofhad population different used mathematical models to track and predict the ebb and flow of population in different species, but they were aware that the models were poor approximations of the seething mass species, but Their they were aware of that the models were poor approximations ofthe theimportance seething mass of real life. awareness these limitations predisposed them to see of ofirregularities real life. Their awareness of these limitations predisposed them to see the importance of that others saw as mere oddities. So while initial assumptions that populations irregularities that others saw as mere oddities. So while initial assumptions that populations were aiming for some kind of equilibrium level meant that, if numbers bounced back and forth, were aiming for some kind of equilibrium level meant that, if numbers bounced back and forth, it was assumed that the population level was oscillating around some underlying equilibrium, it was assumed that the population level was oscillating around some underlying equilibrium, peoplesoon soonbegan begantotothink thinkthat thatthere theremight mightbebenonounderlying underlyingequilibrium. equilibrium. people bothmodels modelsand andininreality, reality,species speciespopulations populationswere werefound foundtotocycle cycle between‘boom’ ‘boom’and and InInboth between ‘bust’ over different periods, sometimes without pattern, and sometimes with new patterns ‘bust’ over different periods, sometimes without pattern, and sometimes with new patterns ofof ‘boomand andbust’ bust’emerging. emerging.Similarly, Similarly,it itwas waswell wellknown knownthat thatepidemics epidemicstended tendedtotocome comeinin ‘boom cycles,and andwhile whileintuition intuitionsuggests suggeststhat thata aprogramme programmeofofinoculation inoculationwould wouldchange changethe thepattern pattern cycles, infectionininthe thedesired desireddirection direction(downwards), (downwards),reality realityshowed showedthat thathuge hugeoscillations oscillationswere were ofofinfection justasaslikely likelytotobe bethe theresult. result.The Thelong-term long-termtrend trendfor forinfection infectionmight mightbebedownwards, downwards,but butit it just wouldbebeinterrupted interruptedbybysurprising surprisingpeaks. peaks.This Thiswas wasexactly exactlythe theexperience experienceduring duringthe the would inoculation inoculationcampaign campaigntotowipe wipeout outrubella rubellaininBritain. Britain.
So Sowhat whatwas washappening happeninghere? here?Where Wheredoes doesthis this‘boom ‘boomand andbust’ bust’cycle cyclecome comefrom, from,and andshould should some someinterventions interventionsdesigned designedtotomove movea apattern patternininone onedirection directionsometimes sometimeslead leadtoto contradictory contradictoryoutcomes? outcomes?
The above diagram shows a classic birfucating image of chaos, where outcomes oscillate The above diagram shows a classic birfucating image of chaos, where outcomes oscillate between widely-varying values, and then dissolve into disorder, which in itself contains both between widely-varying values, and then dissolve into disorder, which in itself contains both instability and pattern. It has been shown that many chaotic systems behave in such ways, instability pattern.ofIt actions has been shown that many chaotic systems behave in such ways, and that theand outcomes in such a system cannot be predicted. and that the outcomes of actions in such a system cannot be predicted. What does this mean for organisational behaviour? It basically means that if you want to predict and plan for the outcomes of any given action, forget it. Any dynamic,
What does this mean for organisational behaviour? It basically means that if you want complex or chaotic system has so many factors bearing upon it, all of which react to to predict and plan for the outcomes of any given action, forget it. Any dynamic, one another’s actions infor an ongoing process, all outcomes will complex ormean chaotic system hasiterative so many factorsIt that bearing upon it, all of be react to What does this organisational behaviour? basically means that ifwhich you want ‘chaotic’, that is, will include elements of stability and instability. Enjoy the ride. one another’s actions in an ongoing iterative process, that all outcomes will be to predict and plan for the outcomes of any given action, forget it. Any dynamic, ‘chaotic’, that is, will include of stability and instability. ride. complex or chaotic system has soelements many factors bearing upon it, all ofEnjoy whichthe react to The butterfly effect actions in an ongoing iterative process, that all outcomes will be one another’s The‘chaotic’, butterflythat effect is, will include elements of stability and instability. Enjoy the ride. One of the other key features of chaos theory is the ‘butterfly effect’, and this links closely to the above of key bifurcation, that it is impossible to predicteffect’, the outcomes anyclosely given to One ofconcept the other featuresi.e. of chaos theory is the ‘butterfly and thisoflinks The butterfly effect action. The butterfly effect, or to give its technical name, “sensitive dependence on initial the above concept of bifurcation, i.e. that it is impossible to predict the outcomes of any given conditions”, implies outcomes can vary with effect’, just a small variation in on theinitial initial action. The butterfly effect, to give itssignificantly technical name, “sensitive One of the other keythat features of or chaos theory is the ‘butterfly anddependence this links closely to inputs. This is embodied in the idea that a butterfly can flutter its wings in China, and two conditions”, implies that outcomes significantly with just small variation the initial the above concept of bifurcation, i.e. thatcan it isvary impossible to predict the aoutcomes of any in given months laterThis youisget a tornado over the USA. inputs. embodied that a butterfly flutter its wings in China, and two action. The butterfly effect, or in to the giveidea its technical name, can “sensitive dependence on initial monthsimplies later you get a tornadocan over thesignificantly USA. conditions”, that outcomes vary with just a small variation in the initial inputs. This is embodied in the idea that a butterfly can flutter its wings in China, and two months later you get a tornado over the USA.
In organisational this means that actions small actions canbig have big outcomes. In organisational terms,terms, this means that small can have outcomes. In anyIn any non-linear, chaotic system, effects do not vary smoothly with input. non-linear, chaotic system, effects do not vary smoothly with input. In organisational terms, this means that small actions can have big outcomes. In any In suchchaotic contexts, change is best seen as an iterative process non-linear, system, effects do not vary smoothly with input. of small steps, not (for In such contexts,the change is bestofseen asscale an iterative steps, the not inertia (for of example) marshalling large changeprocess ‘muscle’oftosmall overcome example) the marshalling ofislarge change ‘muscle’ to overcome the inertia of(for and organisational culture. Itbest isn’tscale a case thatiterative small changes come from small actions, In such contexts, change seen as an process of small steps, not largethe changes need large actions major leverage. Who small example) marshalling of alarge scale change ‘muscle’ to overcome thewhat inertia of organisational culture. It isn’t case thatand small changes come fromknows small actions, andseeds we sow in people’s minds that will bear considerable (and unforeseen) fruit in the organisational culture. It isn’t a case that small changes come from small actions, and large changes need large actions and major leverage. Who knows what small seeds future? large changes need large actions and major leverage. Who knows what small seeds we sow in people’s minds that will bear considerable (and unforeseen) fruit in the we sow in people’s minds that will bear considerable (and unforeseen) fruit in the future? Since the setting of initial conditions is so significant in terms of final outcome, it future? highlights this as a key aspect of the leadership or management role. But be aware Since the ofofinitial conditions isisso ininwhat terms ofoffinal the outcomes could vary considerably from you anticipated. Sincethat thesetting setting initial conditions sosignificant significant terms finaloutcome, outcome,itit highlights this as a key aspect of the leadership or management role. But highlights this as a key aspect of the leadership or management role. Butbe beaware aware that the outcomes could vary considerably from what you anticipated. that the outcomes could vary considerably from what you anticipated. Fractals – self-similarity across scale
Fractals Fractals––self-similarity self-similarityacross across scale scale
Take a closer look at the above diagram. It shows the bifurcations mentioned earlier. Then note the highlighted area. When magnified, it shows that the pattern is repeated (albeit at a at farthe smaller Takeinversely) a closer look abovescale. diagram. It shows the bifurcations mentioned earlier. Then Takethe a closer look atarea. the above It itshows bifurcations earlier. note highlighted When diagram. magnified, showsthe that the patternmentioned is repeated (albeitThen note the highlighted area. When magnified, it shows that the pattern is repeated (albeit inversely) at a far smaller scale. inversely) at a far smaller scale.
This is know as ‘self-similarity across scale’, or as fractal behaviour. The patterns exhibited by a chaotic system are the same at a macro and a micro level. IBM mathematician Benoit Mandelbrot, who coined the term ‘fractal’, first identified this behaviour when analysing the fluctuations in the price of cotton in the USA over the course of a century. He was trying to find the broad swings in the price of cotton, those that were unaffected by daily fluctuations and speculation, which were just ‘noise’, unpredictable and uninteresting. To his surprise, he found that while price changes were random and unpredictable, the curves for daily price changes matched those of monthly price changes exactly, The degree of variation in prices remained constant even during the first 60 years of the twentieth century, which included two world wars and a major depression. Fractals belong to chaos theory because they represent the emergence of order and beauty in chaotic systems. “In the end, the word fractal came to stand for a way of describing, calculating, and thinking about shapes that are irregular and fragmented, jagged and brokenup – shapes from the crystalline curves of snowflakes to the discontinuous dusts of galaxies. A fractal curve implies an organising structure that lies hidden among the hideous complication of such shapes”6. They also show that chaotic systems develop similar patterns of order at different scales. Applying fractal thinking to organisations is potentially fruitful in helping to think about how they should be structured, and also for analysing organisation-wide behaviour by identifying the self-similar patterns that exist at lower levels or at a smaller scale. The following comment illustrates this approach : “We have found success in using the tools of family therapy in larger systems by helping groups turn their attention to the way their system functions—to basic processes. We have often found that the problems in the group are structurally similar to problems in the external environment or a larger system of which the group is a part, but they occur a smaller scale. They are fractals. In mathematics, a fractal is an intricate design that appears when a series of nonlinear equations are solved by a computer and the results are repeatedly fed back into the equations. The design that results from this iterative process is composed of parts that are self-similar on different scales. In some fractals, you will see the same pattern when looking at the initial design as you will when you magnify a portion of it 200 times, or a million times, or a billion times. It is an expression of self-similarity across scales. Fractals are one way in which nature organizes itself, and, therefore, it is not surprising that human problems can also express this type of self-similarity across scales”7. An unapologetic footnote If this short paper seems rather abstract and theoretical, it is because it is intentionally so. I have tried to give some pointers to where I think chaos theory can be applied to organisational behaviour, but I do so in much more practical detail in my book World of Difference. But, to keep the book a bit more readable, I used some of the concepts and assumptions of chaos/complexity theory to explore how organisations might behave in an increasingly complex and diverse world, without necessarily spelling out the theoretical basis of the ideas. I have found that people misunderstand what is meant by chaos, and that is why I have put together this short paper. Perhaps the most important principle to remember about chaos theory is that of ‘self-organisation’. Now go read my book8. © Richard Tiplady 15th December 2003 http://www.tiplady.org.uk
1
Aldous Huxley, Brave New World, London:Flamingo, 1994, p205 2
James Gleick, Chaos, London:Vintage, 1998, p299 3
Arthur Battram, Navigating Complexity, London : The Industrial Society, 1999, vi 4
Sometimes called complexity theory, or dynamical systems theory 5
Gleick, ibid, p30 6
Gleick, ibid, p114 7
Judy Kirmmse and Jo Vanderkloot, http://www.afta.org/newsletter/83/kirmmse.html (accessed 15th December 2003) 8
Copies of World of Difference can be ordered from http://www.paternoster-publishing.com/Merchandiser/catalog/Product.jhtml?PRODID=225181&CATID=77 or http://www.amazon.co.uk/exec/obidos/ASIN/1842272446/ref=ord_1cl_log_ydet/026-3709373-0788410