A Note on the Difference Between Complicated and Complex Social Systems
Abstract
The distinction between complicated and complex systems is of immense importance, yet it is often overlooked. Decision-makers commonly mistake complex systems for simply complicated ones and look for solutions without realizing that ‘learning to dance’ with a complex system is definitely different from ‘solving’ the problems arising from it. The situation becomes even worse as far as modern social systems are concerned. This article analyzes the difference between complicated and complex systems to show that (1) what is at stake is a difference of type, not of degree; (2) the difference is based on two different ways of understanding systems, namely through decomposition into smaller parts and through functional analysis; (3) complex systems are the generic, normal case, while complicated systems are highly distinctive, special, and therefore rare.
1. Introduction
During the past five or six decades, ‘complexity’ has been defined in many different ways.* As a consequence, the difference between ‘complex’ and ‘complicated’ problems and systems has become unclear and difficult to trace. 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 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; must be addressed as entire systems, that is they cannot be addressed in a piecemeal way; they are such that small inputs may result in disproportionate effects; the problems they present cannot be solved once and for ever, but require to be systematically managed and typically any intervention merges into new problems as a result of the interventions dealing with them; and the relevant systems cannot be controlled – the best one can do is to influence them, learn to “dance with them”, as Donella Meadows aptly said.†
Unfortunately, the vast majority of decision-makers ask their consultants to give them ‘solutions’ that can solve problems once and for all. That is, they ask their consultants to treat complex problems as if they were complicated ones. Complexity and the nature of contemporary science show that the claim to ‘solve’ (complex) problems is often ungrounded.1 ‘Learning to dance’ with a complex system is definitely different from ‘solving’ the problems arising from it.
The situation becomes even worse as far as modern social systems are concerned – not the least because “most modern systems are both hideously complicated and bewilderingly complex”.2 According to the golden rule above, the difference between ‘complicated’ and ‘complex’ systems is a difference of type, not a difference of degree. In this sense, a complex system is not a system that is remarkably more complicated than a customarily complicated system. A complex system is a system of completely different type from a complicated system. This understanding is apparently at odds with the quotation from Mulgan and Leadbeater. According to that quote, a system can be both complicated and complex. The apparent contradiction vanishes as soon as one recognizes that the qualities or properties that make a system complicated are different from the qualities or properties that make a system complex. The properties used to classify a system as complicated are different from the properties used to understand a system as complex. This difference explains why the same system can be classified as pertaining to two otherwise different categories – and explains also why decision-makers tend to keep their focus on the side of complicatedness and downsize or misinterpret the issue of complexity. Many contemporary problems are made worse by trading one type of problem for the other, because the problems arising from what makes a system complicated can eventually be solved, while those arising from what makes a system complex can at best be transformed or modified, but not solved once and for ever. This is precisely the meaning of Meadows’ learning to ‘dance with them’.
In this regard, reductionism is the thesis that the type-difference between complicated and complex systems is only apparent because the properties that make a system complex are based on the properties that make a system complicated. Or that the latter can simulate, or approximate, as far as one likes, the former. On the other hand, a non-reductionist position maintains that the difference between complicated and complex systems is a type-difference that cannot be bridged, and all simulations of the latter from the former miss relevant information.
This observation introduces the theme of ‘adequate’ models. In short, one can always use physical models in non-physical contexts. This does not mean, however, that these models are able to capture the proprium of different situations. One can measure the weight and volume of a cat – and these measures provide authentic information – but neither the weight nor the volume of a living being properly characterizes the human being’s nature. Similarly, it is always possible to quantify psychological and social phenomena, without being able to capture their nature.
It is our claim that the difference between complicated and complex systems is of the same kind: one can always exploit complicated systems to understand complex ones – e.g. by developing simulations of the latter that come as close as possible – but in doing so, something essential is systematically lost.
To see what is at stake, I shall now dig deeper into the difference between complicated and complex systems.
* Here I use “complexity” with 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.
† 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 that are
able to observe themselves (which is one of the sources of their complexity).
1. Roberto Poli, “Complexity, Acceleration, and Anticipation,” ECO 14, no. 4 (2012): 124-138
2. Geoff Mulgan and Charlie Leadbeater, “The Systems Innovator,” Nesta http://www.nesta.org.uk/library/documents/Systemsinnovationv8.pdf