Complexity as an idea, a theory, a science, and a way of thinking is a recent phenomena (~30 years). It originated in the convergence of ways of thinking about physics, chemistry, artificial intelligence, chaos theory, fractal geometry, cybernetics, information science, and systems theory, but has expanded to the social sciences, and education. It is concerned with systems that learns in some way, for example, brains, consciousness, social collectives, emergent technologies, or knowledge bodies.
" The emergent realm of complexity thinking answers, that, to make sense of the sorts of phenomena mentioned above, one must "level jump"--that is, simultaneously examine the phenomena in its own right (for its particular coherence and its specific rules of behavior) and pay attention to the conditions of its emergence (e.g., the agents that come together, the contexts of their co-activity, etc.)."(Davis & Sumara, 2006)
The use of the term transdisciplinary is now used to describe complexity studies, as research teams may come from different backgrounds, but are adequately informed of each other's perspectives to work in sync as a collective.
For a phenomena to be classes as complex, it must be:
•self-organized
•bottom-up emergent
•short-range relationships
•nested structure
•ambiguously bounded
•organizationally closed
•structure determined
•far-from equilibrium (Davis & Sumara, 2006)
Here is a diagram of the complexity science tree from the Complexity and Education website:
accessed on March 10,2012, Complexity and Education, http://www.complexityandeducation.ualberta.ca/glossary.htm.
Origins
Physicist Warren Weaver published a paper in 1948 that provided a rubric defining complex and not-complex forms and events. He identified simple, complicated, and complex phenomena. His category of simple were the forms and events that Galileo, Descartes and Newton studied. The scientists developed analytic methods to reduce these mechanical phenomena to basic laws and elementary particles. (Davis & Sumara, pp. 8-9)
In the early days of modern science, this analytic approach, which cut apart and reassembled phenomena into unshakeable explanations. This evolved into the philosphy of determinism-- the understanding that all that will happen is already determined, there are no accidents, and all that has happened can be calculated. Determinism is still in effect in our sciences now, although even Newton realized that in his simple systems where three or more components were in play, understanding and study became intractable. Probability and statistics were developed to attempt interpretation at these more complicated systems. However, they did not seem to result in a shift in thinking.
However, by the early 1900s French mathematician Henri Poincaré explained:
[E]ven if it were the case that the natural laws no longer held any secret for us, we could still only know the initial situation approximately. If that enables us to predict the succedding situation with the same spproximation, that is all we require, and we should say the phenomenon had been predicated, that is governed by laws. But it is not aalways so; it may happen that small differences in the initial conditions producevery great ones in the final pheonmenon. A small error in the former will produce an enormous error in the latter. Predication becomes impossible." (Poincaré,1905)
Poincaré suggested that actions of systems may transform them; some systems are self-transformative. In complex systems, interactions are not fixed and may be subject to ongoing evolution and adaptations. Realizing the variability of complex systems is immensely important. New principles of this adaptation, or learning are now needed to study complex systems, as it becomes more and more clear that it may be impossible to predict behavior of these systems.
Reconsideration of Learning as Complex Activity
Learning becomes a transformative process, structural change occurs in the learner uniquely because of their own biologic and experiential structures. This is indeed, another argument to refute Skinner and behaviorism. The external stimulus does not cause the learning or behavior of the learner.
The learner must also be rethought. Understanding of multiple meanings of structure is important to define what a learner is. Buildings are fixed structures and can be mapped with blueprints, defined by scaffolds, platforms, foundations, hierarchies, etc. Biological structure is again complex. Structures of living systems and organisms are incomprehensible.
"Returning to the issue at hand, then, a learner in this text is understood to be a structuring structured structure, to borrow from Dyke. A learner is a complex unity that is capable of adapting itself to the sorts of new and diverse circumstances that an active agent is likely to encounter in a dynamic world." (Davis & Sumara, p14)
Ian Stewart and Jack Cohen recombine terms simplicity and complexity to generate simplexity and complicity. They discuss that simplexities have often been taken at face value to define the truth of how and what things are, mistakenly so. Complicities "totally different rules converge to produce similar features, and so exhibit the same large-scale structural patterns." (Cohen & Stewart, 1994, p. 414)
Bibliography
Davis, Brent, Sumara, Dennis, Complexity and Education Inquiries into Learning, Teaching, and Research, Routledge, New York, 2006.
Cohen, J., Stewart, I., The collapse of chaos: discovering simplicity in a complex world (New York: Penquin, 1994)
Dyke, C., The evolutionary dynamics of complex systems (Oxford: Oxford University Press, 1988)
H. Poincaré, Science and Hypothesis (London: Walter Scott Publishing, 1905)
Complexity Science tree diagram, accessed on March 10,2012, Complexity and Education, http://www.complexityandeducation.ualberta.ca/glossary.htm.
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