Chapter One - Additional Resources

What is complexity theory?

For Laplace and Newton, the universe was rationalistic, deterministic and of clockwork order; effects were functions of causes, small causes (minimal initial conditions) produced small effects (minimal and predictable) and large causes (multiple initial conditions) produced large (multiple) effects. Predictability, causality, patterning, universality and ‘grand’ overarching theories, linearity, continuity, stability, objectivity, all contributed to the view of the universe as an ordered and internally harmonistic mechanism in an albeit complex equilibrium, a rational, closed and deterministic system susceptible to comparatively straightforward scientific discovery and laws.

From the 1960s this view has been increasingly challenged with the rise of theories of chaos and complexity. Central to chaos theory are several principles (e.g. Gleick, 1987; Morrison, 1998):

• Small-scale changes in initial conditions can produce massive and unpredictable changes in outcome (e.g. a butterfly’s wing beat in the Caribbean can produce a hurricane in America);
• Very similar conditions can produce very dissimilar outcomes (e.g. using simple mathematical equations (Stewart, 1990);
• Regularity and conformity break down to irregularity and diversity;
• Even if differential equations are very simple, the behaviour of the system that they are modelling may not be simple;
• Effects are not straightforward continuous functions of causes;
• The universe is largely unpredictable;
• If something works once there is no guarantee that it will work in the same way a second time;
• Determinism is replaced by indeterminism; deterministic, linear and stable systems are replaced by ‘dynamical’, changing, evolving systems and non-linear explanations of phenomena;
• Continuity is replaced by discontinuity, turbulence and irreversible transformation;
• Grand, universal, all-encompassing theories and large-scale explanations provide inadequate accounts of localized and specific phenomena;
• Long-term prediction is impossible.
• Order is not predetermined and fixed;Social behaviour, education and learning are emergent, and are marked by recursion, feedback, evolution, autocatalysis, openness, connectedness and self-organization (e.g. Doll, 1993);
• Social life, education and learning take place through the interactions of participants with their environments (however defined, e.g. interpersonal, social, intrapersonal, physical, material, intellectual, emotional) in ways which cannot be controlled in an experiment;
• Local rules and behaviours generate diversity and heterogeneity of practice, undermining generalizability from experiments about ‘what works’.

Complexity theory has entered the world of social sciences and is providing not only a significant challenge to existing research methods, but is suggesting alternative ways of conceiving the world and, thereby, of researching it (c.f. Morrison, 2002; 2003). Here we outline some key features of complexity theory, and tease out its implications for educational research.

It is a truism to state that society is changing, and that the paradigms for understanding society, themselves, are changing. Change is ubiquitous, and stability and certainty are non-concepts in complexity theory. Educational research can be viewed through the lens of complexity theory, replacing positivism with complexity theory (Lewin, 1993; Morrison, 2002). The elements of complexity theory are set out in Figure 1.

Figure 1: Components of complexity theory

Complexity theory looks at the world in ways which break with simple cause-and-effect models, linear predictability, and a dissection approach to understanding phenomena, replacing them with organic, non-linear and holistic approaches (Santonus, 1998, p. 3) in which relations within interconnected networks are the order of the day (Youngblood, 1997, p. 27; Wheatley, 1999, p. 10).

Through feedback, recursion, perturbance, auto-catalysis, connectedness and self-organization, higher and greater levels of complexity and differentiated, new forms of life, behaviour, systems and organizations arise from lower levels of complexity and existing forms. These complex forms often derive from comparatively simple sets of rules – local rules and behaviours generating emergent complex global order and diversity (Waldrop, 1992, pp. 16-7; Lewin, 1993, p. 38; Åm, 1994). General laws of emergent order can govern adaptive, dynamical processes (Waldrop, 1992, p. 86; Kauffman, 1995, p. 27).

Figure 2: the rise of emergence in complexity theory

In Figure 2 the interaction of individuals feeds into the wider environment which, in turn influences back into the individual units of the network; they co-evolve, shaping each other (Stewart, 1991). Bar-Yam (1997) suggests that a complex system is formed from its several elements, and that the behaviour of the complex system as a whole is greater than the sum of the parts (see also Goodwin, 2000, p. 42).

Fixity in the environment and its components does not exist; stability is the stability of the mortuary. Stable systems, as Stacey (1992, p. 40) reminds us, ultimately fail. Indeed April (1997, p. 26) suggests that change and unpredictability are requirements if an organism is to survive: ‘a butterfly which flies in a straight line without zigzags will fall prey very fast’. A heartbeat is marked by regularity immediately prior to cardiac arrest. Change and movement are necessary for survival. Disequilibrium is vital for survival. The caterpillar must cede to the butterfly if the species is to survive.

Feedback must occur between the interacting elements of the system. Complexity theorists (Waldrop, 1992; Cilliers, 1998) have turned to Hebb’s (1949) views on learning in respect of feedback for development. Hebb’s operates on an associationist or connectedness principle (‘joined-up thinking’): if X and Y occur together then an association between the two is formed in the brain synapses; if there is recurrence of the association between X and Y then the strength of that connection is increased into strong ‘cell assemblies’; if recurrence is minimal or non-existent then the association decays and dies. If, each time I encounter mathematics, I experience pain, then, naturally, I will tend to associate pain with mathematics, and this will shape my reactions to it. I may avoid mathematics or take steps to reduce the pain etc., i.e. I have learned something and it affects my behaviour.

Negative feedback – for example learning that one has failed in a test – brings diminishing returns (Marion, 1999, p. 75); it is regulatory. Positive feedback brings increasing returns and uses information not merely to regulate but to change, grow and develop (Wheatley, 1999, p. 78). It amplifies small changes (Stacey, 1992, p. 53; Youngblood, 1997, p. 54). Senge et al. (2000, p. 84) cite the example of a baby animal whose eating is voracious. The more it eats, the faster it grows; its rate of growth accelerates. Once a child has begun to read she is gripped by reading, she reads more and learns at an exponential rate.

Not only can feedback be positive, it also needs to be rich. If I simply award a grade to a student’s work, she cannot learn much from it except that she is a success or a failure, or somewhere in between. If, on the other hand, I provide rich feedback she can learn more; if I only point out two matters in my feedback then the student might only learn those two matters; if I itemize ten points then the student might learn all ten of them. We have to recall that the root of ‘feedback’ is ‘food’, nourishment rather than simply information of low-level curriculum facts.

Connectedness , a key feature of complexity theory, exists everywhere. In schools, children are linked to families, teachers, peers, societies and groups; teachers are linked to other teachers, other providers of education, support agencies like psychological and social services, policy-making bodies, funding bodies, the legislature, and so on. The school is not an island, but is connected externally and internally in several ways. Indeed, many schools sink under internal communication and connectedness, through memoranda, meetings, paperwork, assessment data, inspection data, working parties, policy and curriculum development groups, e-mail, voice mail and a host of other forms. The price of communication is high in terms of teacher stress.

Connectedness is exemplified neatly in another setting. Take a rainforest; in it ants eat leaves, birds eat ants and leave droppings, which fertilize the soil for growing trees and leaves (Lewin, 1993, p. 86). As April et al. (2000, p. 34) remark, nature possesses many features that organizations crave: flexibility, diversity, adaptability, complexity, and connectedness. Connectedness is required if a system is to survive; disturb one element in the connections and either the species or system must adapt or die; the process is inexorable. Connectedness through communication is vital. This requires a distributed knowledge system, in which knowledge is not centrally located in a command and control centre or in a limited set of agents (e.g. a government); rather it is dispersed, shared and circulated throughout the organization and its members.  

If learning through feedback is to take place, if connectedness is to work successfully, and if knowledge is to be collected from a distributed, dispersed system, then an essential requirement is effective communication and collaborative learning. Communication and collaboration are key variables (c.f. Peters, 1989; Cilliers, 1998). Communication is central to complexity theory.

Emergence is the partner of self-organization. Systems possess the ability for self-organization, which is not according to an a priori grand design – a cosmological argument – nor to a deliberately chosen trajectory or set of purposes – a teleological argument. Rather, self-organization emerges of itself as the result of the interaction between the organism and its environment (Casti, 1997), and new structures emerge that could not have been envisioned initially (Merry, 1998). 

The movement towards greater degrees of complexity, change and adaptability for survival in changing environments is a movement towards ‘self-organized criticality’ (Bak and Chen, 1991; Bak, 1996), in which systems evolve, through self-organization, towards the ‘edge of chaos’ (Kauffman, 1995) (defined below). Take, for example, a pile of sand (Bak, 1996). If one drops one grain of sand at a time a pyramid of sand appears. Continue to drop another granule and a small cascade of sand runs down the pyramid; continue further and the sand pile build up again in a slightly different shape; continue further and the whole pyramid falls down like a house of cards. This is chaos, and complexity theory resides at the edge of chaos, at the point just before the pyramid of sand collapses, between mechanistic predictability and complete unpredictability (Karr, 1995, p. 3; Bak, 1996):

Stacey (2000, p. 395) suggests that a system can only evolve, and develop spontaneously, where there is diversity and deviance (ibid., p. 399) – a salutary message for command-and-control teachers who exact compliance from their pupils.

The exact movement, reconfiguration and subsequent catastrophic destruction of the sand pile mentioned above are largely unpredictable. At the point of ‘self-organized criticality’ the effects of a single event are likely to be very large, breaking the linearity of Newtonian reasoning wherein small causes produce small effects. The straw breaks the camel’s back, and a small cause has a massive effect; a single grain of sand destroys the pyramid. Change implies, then, a move towards self-organized criticality, and such self-organized criticality evolves ‘without interference from any outside agent’ (Bak, 1996, p. 31); it emerges. In the sand pile, Michaels (1995) and Bak (1996) suggest that it would not exist were it not for the grains of sand relating to each other and holding each other together. Recalling Hebb’s account of learning earlier, if association is to be strengthened then this requires collaboration. A significant factor here is that the closer one moves towards the edge of chaos, the more creative, open-ended, imaginative, diverse, and rich are the behaviours, ideas and practices of individuals and organizations, and the greater is the connectivity, networking and information sharing (content and rate of flow) between participants (Stacey et al ., 2000, p. 146).

The above considerations suggest that linear, mechanistic models of research may no longer apply, and networks and dynamical, ever-changing systems and turbulent environments are the order of the day. Put simply, ‘complex adaptive systems’ (Waldrop, 1992, p. 294-9) scan and sense the external environment and then make internal adjustments and developments in order to meet the demands of the changing external environment. This is the ‘law of requisite variety’ (Ashby, 1964), which states that internal systems, flexibility, change and capability must be as powerful as those in the external environment. In the setting of education, in making internal changes in order to fit the law of requisite variety, school-based, collaborative research is required (Morrison, 2002).

Out go the simplistic views of linear causality, the ability to predict, control and manipulate, and in come uncertainty, networks and connection, self-organization, emergence over time through feedback and the relationships of the internal and external environments, and survival and development through adaptation and change. Society and societal systems are open; closed systems, as Prigogine and Stengers (1985) remind us, run down and decay into entropy unless they import energy from outside. They either adapt or die.

The implications of complexity theory for educational research

Chaos and complexity theories argue against the linear, deterministic, patterned, universalizable, stable, atomized, modernistic, objective, mechanist, controlled, closed systems of law-like behaviour which may be operating in the laboratory but which do not operate in the social world of education. These features of chaos and complexity theories seriously undermine the value of experiments and positivist research in education (e.g. Gleick, 1987; Waldrop, 1992; Lewin, 1993.

Complexity theory argues to replace an emphasis on simple causality with an affirmation of networks, linkages, feedback, impact, relationships and interactivity in context (Cohen and Stewart, 1995), emergence, dynamical systems, self-organization and distributed control (rather than the controlling mechanism of an experiment), and open system (rather than the closed system of the experimental laboratory) (Morrison, 2003). What is being argued here is that, even if one could conduct an experiment, the applicability of that laboratory procedure to ongoing, emerging, interactive, relational, changing, open situations, in practice, would be limited, even though some gross similarities might be computed through meta-analysis. The world of classrooms is not the world of computed statistics.

Schools exhibit many features of complex adaptive systems, being dynamical and unpredictable, non-linear organizations operating in unpredictable and changing external environments. Indeed schools both shape and adapt to macro- and micro-societal changes, organising themselves (maybe in response to external constraints and pressures), responding to, and shaping their communities and society (i.e. all parties co-evolve).

Linear systems demonstrate Newtonian mechanistic predictability and controllability. Small causes bring small effects and large causes bring large effects. Chaos occurs when small causes can bring huge effects and huge causes may have little or no effect, i.e. where unpredictability and uncontrollability reign. Complexity breaks the mechanistic determinism of linear systems but not in the unpredictable, uncontrolled way of chaos; as systems move away from linear predictability, the emphasis on creativity, divergence and fecundity are maximised but are still ordered, before they spill over into the breakdown of order that is chaos. Complexity theory suggests that the tenets of positivism are highly questionable. Control, predictability, manipulation and straightforward relationships between cause and effect no longer hold true in the complex world.

Circular causality and feedback are central elements in complexity theory. Fuchs (2003: 16) indicates that the term social self-organization is implied and refers to the dialectical relationship of structures and actions which results in the overall re-production of the system. The creativity and knowledgeability of actors is at the core of this process and secures the re-creation of social systems within and through self-conscious, creative activities of human actors. . . . The term self-organization refers to the role of the self-conscious, creative, reflective and knowledgeable human beings in the reproduction of social systems. (Fuchs, 2003: 16)

Marx’s remark is fitting here and applies to self-organization: we create our own history but not in circumstances of our own choosing; we are affected by external conditions.

With respect to research methodology, complexity theory suggests that educational research should concern itself with: (a) how multivalency and non-linearity enter into decision making in education; (b) how voluntarism and determinism, agency and structure, lifeworld and system, divergence and convergence interact to bring about or impede educational change; (c) how to both use, but transcend, simple causality in understanding the processes of school development and change; (d) how viewing a system holistically, as having its own ecology of multiply-interacting elements, yields greater insights than an atomized approach.

Complexity theory suggests that phenomena must be looked at holistically; to atomise phenomena into a restricted number of variables and then to focus only on certain factors is to miss the necessary dynamic interaction of several parts. More fundamentally, complexity theory suggests that the conventional units of analysis in educational research (as in other fields) should move away from, for example, individuals, institutions, communities and systems (c.f. Lemke, 2001). These should merge, so that the unit of analysis becomes a web or ecosystem (Capra, 1996: 301), focused on, and arising from, a specific topic or centre of interest (a ‘strange attractor’). Individuals, families, students, classes, schools, communities and societies exist in symbiosis; complexity theory tells us that their relationships are necessary, not contingent, and analytic, not synthetic. This is a challenging prospect for educational research, and complexity theory, a comparatively new perspective in educational research, offers considerable leverage into understanding societal, community, individual, and institutional change; it provides the nexus between macro and micro-research in understanding and promoting change.

In addressing holism, complexity theory suggests the need for case study methodology, action research, and participatory forms of research, premised in many ways on interactionist, qualitative accounts, i.e. looking at situations through the eyes of as many participants or stakeholders as possible. This enables multiple causality, multiple perspectives and multiple effects to be charted. Self-organization, a key feature of complexity theory, argues for participatory, collaborative and multi-perspectival approaches to educational research. This is not to deny ‘outsider’ research; it is to suggest that, if it is conducted, outsider research has to take in as many perspectives as possible.

In educational research terms, complexity theory stands against simple linear methodologies based on linear views of causality, arguing for multiple causality and multi-directional causes and effects, as organisms (however defined: individuals, groups, communities) are networked and relate at a host of different levels and in a range of diverse ways. No longer can one be certain that a simple cause brings a simple or single effect, or that a single effect is the result of a single cause, or that the location of causes will be in single fields only, or that the location of effects will be in a limited number of fields.

It is untenable to hold variables constant in a dynamical, evolving, fluid, idiographic, unique situation. It is a commonplace truism to say that naturalistic settings such as schools and classrooms are not the antiseptic, reductionist, analyzed-out or analyzable-out world of the laboratory, and that the degree of control required for experimental conditions to be met renders classrooms unnatural settings. Yet the implications of this are perhaps understated in advocating experiments – randomised controlled trials – the ‘gold standard’ of research. Even if one wanted to undertake an experiment, to what extent is it actually possible to identify, isolate, control and manipulate the key variables in an experiment, and, thence, to attribute causality?

For example, let us say that an experiment is conducted to increase security and reduce theft in two schools through the installation of a closed circuit television (CCTV). The effect is a reduction in theft in the experimental school. Exactly what is the cause here? It may be that potential offenders are deterred from theft, or it might be that offenders are caught more frequently, or it might be that the presence of the CCTV renders teachers and students more vigilant, and, indeed, such vigilance might make the teachers and students more security-conscious so that they either do not bring valuables to the school or they store them more securely. The experiment might succeed in reducing theft, but what exactly is happening in the experimental school? Are the changes occurring in the teachers, the students, or the thieves, or some combination of these?

We simply cannot infer causes from effects, and it is a pretence to believe that we can isolate, control and predict social behaviour from, and in, naturalistic settings. Indeed the successionist conceptualization of causality (Harré, 1972), wherein researchers make inferences about causality on the basis of observation, must admit its limitations in really understanding how an intervention or experiment actually works in practice. Yet it is precisely this explanatory understanding that should be informing practice. The experiment is still a comparatively opaque black box, disabling the identification of detailed causal mechanisms that produce treatment effects (Clarke and Dawson, 1999: 52), and it is precisely these detailed mechanisms that we need to understand in complex situations.

An experiment, as Pawson and Tilley (1993: 8) observe, might be ‘splendid epistemology’ but it is ‘lousy ontology’, as programmes are mediated by their participants. People in the programme might choose to make an intervention work or not work, and teachers’ and students’ motivations in, commitments to, and involvement in an intervention might be the critical factors. This is a crucial matter, for experiments require exactly the same intervention or programme across the control and experimental groups, i.e. to ensure that the protocols and procedures for the research are observed and are identical. Yet this is impossible. Because sentient people tailor their behaviour to each other, their behaviour will differ, and, therefore, the planned intervention or program will alter.

Social processes at work in the experiment may well be determining factors, and experiments may be quite unable to control for these. This is the well-rehearsed problem of causality – behind or alongside an apparent cause (A causes B) lurk other causes (C causes A which causes B, and D causes A and B respectively). The search for simple mono-causality is naïve; indeed it may be the interplay of causes and factors that is producing the effects observed (a feature of complexity theory); it is unclear how an experiment can disentangle this. It is akin to a person taking ten medicines for a stomach pain: she takes the medicines and the pain is alleviated – which medicine(s) was/were effective, or was it the synergy of some or all that caused the relief?

The butterfly beating its wings in the Caribbean and causing a hurricane in another part of the world frustrated Lorenz’s attempts at long range prediction of weather patterns (Gleick, 1987); so it is in classrooms. Small events cause major upsets and render long term prediction or generalizability futile. How do we know what the effects of small changes will be in an experiment? In the short term school inspections might improve academic results, but, in the longer term, they could lead to such demoralization of teachers that recruitment and retention rates suffer, leading to falling academic results. The thirst for improved grades in schools might lead to an initial improvement in performance but contributes to a testing and cramming culture of nightmare proportions (Noah and Eckstein, 1990; Sacks, 1999).

One might find, for example, that constant negative harassment of teachers by a school principal might increase the amount of time they spend on lesson preparation, which might (or, indeed might not) improve lesson quality. The results might be effective in the short term and in the longer term, but such behaviour might also be counter-productive, as the poor interpersonal relations, the hostile atmosphere, the ‘blame culture’ and the demotivation of teachers caused by the principal’s behaviour might lead to rapid staff turnover and the reduction in teachers’ commitment to their work. Further, though the intervention here might be judged a success in the principal’s eyes, in the eyes of the staff the harassment is a dismal failure; ‘what works’ for one party does not work in the eyes of another. Increasing homework may be effective according to a school principal, but may demotivate students from lifelong learning – clearly a failure in the eyes of students.

There is a problem in experiments, in that a single intervention does not produce only a single outcome; it produces several. A treatment for cancer can cure the disease but it might also bring several side-effects, for example hair loss, amputation, sickness and gross lethargy. A reading intervention programme may raise students’ measured achievements in reading but may provoke an intense dislike of books or reading for pleasure. Experiments are inherently reductionist and atomizing in their focus and methodology; they are incapable of taking in the whole picture (Cohen and Stewart, 1995: ch. 6).

Complexity theory not only questions the values of positivist research and experimentation, but it also underlines the importance of educational research to catch the deliberate, intentional, agentic actions of participants and to adopt interactionist and constructivist perspectives. Addressing complexity theory’s argument for self-organization, the call is for the teacher-as-researcher movement to be celebrated, and complexity theory suggests that research in education could concern itself with the symbiosis of internal and external researchers and research partnerships. Just as complexity theory suggests that there are multiple views of reality, so this accords not only with the need for several perspectives on a situation (using multi-methods), but resonates with those tenets of feminist research that argue for different voices and views to be heard. Heterogeneity is the watchword.

A quantitative research methodology that respects this has to break with simple linear models (e.g. simple linear correlations, regression and multiple regression analysis) and adopt a multi-lateral and multi-directional view of causality (e.g. in log-linear and curvilinear analysis, multiple analysis of variance, structural equation modeling, a recognition of the inevitability of heteroscedasticity of lines of relationships).

Complexity theory provides not only a powerful challenge to conventional approaches to educational research, but it suggests both a substantive agenda and also a set of methodologies. It provides an emerging new paradigm for research.

BIBLIOGRAPHY

Åm, O. (1994) Back to Basics. Introduction to Systems Theory and Complexity. http://www.stud.his.no/~onar/Ess/Back-to-Basics.html.

April, K. (1997) An Investigation into the Applicability of New Science, Chaos Theory, and Complexity Theory to Leadership, and Development of Guiding Principles for the Modern Leader and Organisation. Research Report. Capetown: Graduate School of Business, University of Cape Town.

April, K. A. Macdonald, R. and Vriesendorp, S. (2000) Rethinking Leadership. Capetown: University of Capetown Press.

Ashby, W. R. (1964) Introduction to Cybernetics ( London: Methuen).

Bak, P. (1996) How Nature Works. New York: Copernicus.

Bak, P. and Chen, K. (1991) Self-Organized Criticality. Scientific American, January, pp, 46–53.

Bar-Yam, Y. (1997) Dynamics of Complex Systems. New York: Perseus Press.

Capra, F. (1996) The Web of Life ( New York: Anchor Books).

Casti, J. (1997) Would Be World. New York: John Wiley and Sons.

Cilliers, P. (1998) Complexity and Postmodernism. London: Routledge.

Cohen, J. and Stewart, I. (1995) The Collapse of Chaos. Harmondsworth: Penguin.

Fuchs, C. (2003) Structuration theory and self-organization. Systematic Practice and Action Research, 16 (2), pp. 133-67.

Gleick, J. (1987) Chaos. London: Abacus.

Goodwin, B. (2000) Out of control into participation. Emergence, 2 (4), pp, 40-9.

Hebb, D. O. (1949) The Organization of Behaviour: A Neuropsychological Theory. New York: John Wiley and Sons Inc..

Karr, B. (1995) Complexity Theory and Rhetorical Invention. http://english.ttu.edu/courses/5361/papers/paper1_karr_420.html

Kauffman, S. A. (1995) At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Harmondsworth: Penguin.

Lemke, J. (2001) Toward Systemic Educational Change: Questions from a Complex Systems Perspective. ( New England: New England Complex Systems Institute). http://www.necsi.org/events/cxedk16_3.html.

Lewin, R. (1993) Complexity: Life on the Edge. London: Phoenix.

Lissack, M. R. (2000) Complexity Metaphors and the Management of a Knowledge Based Enterprise: An Exploration of Discovery. http://lissack.com/writings/proposal.htm.

Marion, R. (1999) The Edge of Organization: Chaos and Complexity Theories of Formal Social Systems. London: Sage.

Merry, U. (1998) Organizational Lifespan LO17822. http://www.learning-org.com/98.04/0206.html

Michaels, M. (1995) The chaos network on-line. The Chaos Network, http://www.prairienet.org/business.ptech

Morrison, K. R. B. (2002) School Leadership and Complexity Theory. London: RoutledgeFalmer.

• Morrison, K. R. B. (2003) Complexity theory and curriculum reforms in Hong Kong. Pedagogy, Culture and Society, 22 (2), 279-302.

Prigogine, L. and Stengers, I. (1985) Order out of Chaos. London: Flamingo.

• Robson, C. (1993) Real World Research. Oxford: Blackwell.

Santonus, M. (1998) Simple, Yet Complex. http://www.cio.com/archive/enterprise/041598_qanda_content.html.

Stacey, R. D. (1992) Managing the Unknowable. San Francisco: Jossey-Bass.

Stacey, R. D. (2000) Strategic Management and Organisational Dynamics (third edition). Harlow, England: Pearson Education Limited.

Stacey, R. D., Griffin, D. and Shaw, P. (2000) Complexity and Management: Fad or Radical Challenge to Systems Thinking? London: Routledge.

Stewart, I. (1990) Does God Play Dice? Harmondsworth: Penguin.

Stewart, M. (2001) The Co-Evolving Organization ( Rutland, UK: Decomplexity Associates Ltd.). http://www.decomplexity.com/Coevolving%20Organization%20VU.pdf

Tymms, P. B. (1996) Theories, models and simulations: school effectiveness at an impasse. In J. Gray, D. Reynolds, C. T. Fitz-Gibbon, D. Jesson (eds.) merging Traditions: the Future of Research on School Effectiveness and School Improvement. London: Cassell.

Waldrop, M. M. (1992) Complexity: the Emerging Science at the Edge of Order and Chaos. Harmondsworth: Penguin.

Wheatley, M. (1999) Leadership and the New Science: Discovering Order in a Chaotic World (second edition). San Francisco: Berrett-Koehler Publishers.

Youngblood, M. (1997) Life at the Edge of Chaos. Dallas, Texas: Perceval Publishing.