| (Institute for the Study of Coherence and Emergence/Information Age Publishing) How can we think the complex? (2008) | |||||||||||||||
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| This chapter does not deal with specific tools and techniques for managing complex systems, but proposes some basic concepts that help us to think and speak about complexity. We review classical thinking and its intrinsic drawbacks when dealing with complexity. We then show how complexity forces us to take build models with indeterminacy and unpredictability. However, we can still deal with the problems created in this way by being adaptive, and profiting from a complex system’s capability for selforganization, and the distributed intelligence this may produce. 1. Classical Thinking The majority of scientific models—as well as much of our intuitive understanding—implicitly rely on a “classical ” or Cartesian mode of thinking, which is expressed most explicitly in the classical or Newtonian mechanics that dominated the scientific worldview until the beginning of the 20th century. It is based on the following assumptions (Heylighen, 1990): • reductionism or analysis: to fully understand a system you should decompose it into its constituent elements and their fundamental properties. • determinism: every change can be represented as a trajectory of the system through (state) | |||||||||||||||
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