Abstract
During the past five or six decades, ‘complexity’ has been defined in many different ways. Owing to the too many definitions of complexity, the difference between ‘complex’ and ‘complicated’ problems and systems has become unclear and difficult to determine. The following is possibly the golden rule for distinguishing ‘complex’ from ‘complicated’ problems and systems. Complicated problems originate from causes that can be individually distinguished; they can be addressed piece-by-piece; for each input to the system there is a proportionate output; the relevant systems can be controlled and the problems that they present admit permanent solutions. On the other hand, complex problems and systems result from networks of multiple interacting causes that cannot be individually distinguished; they must be addressed as entire systems, that is, cannot be addressed in a piecemeal way; they are such that small inputs may result in disproportionate effects; the problems that they present cannot be solved once and for ever, but require systematic management, and typically any intervention merges into new problems as the result of the actions taken to deal with them; and the relevant systems cannot be controlled – the best one can do is influence them, learn to “dance with them” as Donella Meadows aptly said.
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Notes
- 1.
Here I use ‘complexity ’ in regard to both non-linear phenomena (complexity proper) and infinite sensibility to initial and boundary conditions (what is usually called ‘chaos’ or ‘deterministic chaos’). Both are based on an internal machinery of a predicative, algorithmic, i.e. mechanical, formal nature.
- 2.
Instead of the opposition between complicated and complex systems , Bar-Yam distinguishes between superficial and inherent complexity (Bar-Yam, 2004). The following are some further aspects that a less cursory analysis will have to consider: (1) the ‘complicated’ perspective point tends to work with closed systems, while the ‘complex’ perspective point works with open systems; (2) the former naturally adopts a zero-sum framework, while the latter can adopt a positive-sum framework; (3) the former relies on first-order systems, while the latter includes second-order systems, that is, systems able to observe themselves (which is one of the sources of their complexity ).
- 3.
“Biophysics” was then understood as the “physics of living matter”; see (Abraham, 2004, p. 348).
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Poli, R. (2017). Complexity. In: Introduction to Anticipation Studies. Anticipation Science, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-63023-6_10
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