A Note on the Difference Between Complicated and Complex Social Systems

2. The Difference between Complicated and Complex Systems
If, as we claim, the difference between complicated and complex systems is a difference of type and not of degree, suitable reasons should be provided. As a matter of fact, quite a few reasons can be proposed. The following are the three most obvious reasons for the difference between complicated and complex systems:

  1. The primary way to understand complicated systems is through their structural decomposition – that is, through the segmentation of the whole system into disjoined structural parts and their relations, and the further subdivision of these parts into smaller subparts and their relations. On the other hand, the primary way to understand complex systems is through functional analysis – that is, through the activities exerted by the system. Structural and functional analyses mirror each other only in very special cases. In general, they are different, and the relations among them are far from trivial. One way to see their difference is to note that the same structural part can perform different functions, and the same function can be performed by different structural parts. The ‘one structure-one function’ assumption works only in very rare cases, which implies that it is a highly non-generic assumption.
  2. Whilst systems have a definite number of structural parts, the functions that a system is able to perform are potentially unlimited. The primary way to constrain the range of functions that a system can perform is to delimit its environment, e.g., by allowing the system to interact with only selected types of systems. That is to say, functions can be delimited either by closing the system (no interaction) or closing its environment (limited or constrained interactions).
  3. The above two reasons show that the complexity of a system is not directly connected to the amount of available data or knowledge. Collecting more data or developing better theories will not transform complex systems into complicated ones. This introduces the third reason for the difference between complex and complicated systems. Complicated systems can be – at least in principle – fully understood and modeled. They can be entirely captured by suitable models. Whilst it may not be feasible to build these models with all the necessary details – e.g. because it will be too costly or because some information would be missing – in principle they can be constructed. Complex systems, on the other hand, are such that they are never fully graspable by any model whatsoever: models of them – even in principle – are always incomplete and diverge over time.

The main reason why complex systems have these apparently strange features is that they are creative. Being creative includes the capacity to change, learn, and over time become different from what one was before. But it is more than this. Everything changes, but not everything is creative. To mention but one component of creativity, the capacity to (either implicitly or explicitly) reframe is one of the defining features of creativity. Creativity also includes some capacity to see values and disvalues, and to accept and reject them. Therefore, it is also the source of hope and despair. None of these properties are possessed by complicated systems.

3. Which Systems are Generic?
The proposed acceptance of complexity (and complex systems) is far less trivial than it may at first appear. According to our understanding of complexity, almost everything that falls under the heading of complexity pertains instead to the science of complicated (even extremely or ‘hideously’ complicated, as Mulgan and Leadbeater put it) systems. Complex­ity is an entirely different matter. The irony is that complex (in the proposed acceptation) systems are not rare. Complex systems are the usual, normal case. All living systems, all psychological systems, all social systems are complex. It is complicated systems that are highly distinctive, very special, and therefore rare.

Two obstructions block our capacity to acknowledge that complex systems are the generic – i.e. the usual – type of system. The first is the idea that “physics is the queen of science” – meaning that the other sciences are authentic sciences only if they force themselves into the straitjacket of the physical framework (the positivist or reductionist attitude). This is not meant to be a criticism of physics, not even an implicit one: physics deals with complicated systems, not with complex ones, and its methods have proven exceedingly successful in yield­ing an understanding of complicated systems. There is no reason, however, to believe that its methods can be used to understand complex systems as well. When the objects are remarkably different, this may happen, and it should not be surprising that different view­points and methods are required.

By further developing this train of thought, one arrives at an idea of science that is more general than the competing mainstream acceptance of science presently available: to wit, instead of distinguishing between the Queen (physics) and the pawns (all the rest), the new vision distinguishes between the general framework underlying all sciences (what Rosen called the modeling relation) and a variety of different concretizations of that framework where each concretization depends on specific assumptions or constraints. In this view, physics is a highly specific – that is, non-generic – science, while other sciences, notably biology and all the sciences that rely on it (i.e. all the human and social sci­ences), will require less demanding constraints.

The foregoing is a highly compressed presentation of Rosen’s ideas as developed in his groundbreaking trilogy (see references). Needless to say, I have had to omit many otherwise necessary details.

The second reason is that, willy-nilly, most decision-makers are positivists, and they regularly ask their consultants to give them definitive ‘solutions’ to problems. What they have in mind are (again!) complicated systems, and they want complex systems to be managed as if they were complicated ones. Complexity and the nature of contemporary science show that the claim that (complex) problems can be ‘solved’ is ungrounded.

To call attention to one of the major transformations exhibited by contemporary science, I have found it helpful to contrast the present situation with the basic understanding of tradition­al modern science. In a variety of papers I have presented the following summary, according to which Newtonian science teaches us that natural systems are closed (only efficient causality is accepted; bottom-up, top-down, ‘final’ causes are forbidden), atomic (fractionable), reversible (no intrinsic temporal direction), deterministic (given enough information about initial and boundary conditions, the future evolution of the system can be specified with any required precision), and universal (natural laws apply everywhere, at all times, and on all scales). By contrast, contemporary science shows that these claims are all false, in the literal sense that they work only for some special kinds of systems (technically, they are not generic).3, 4, 5, 6, 7 The framework currently under development in many scientific quarters includes open, non-fractionable, irreversible, non-deterministic and context-dependent systems.§

Since, as they say, the devil is in the details, this is the point to note: there is something even more important than the static opposition between closed and open systems. It is the opposition between the processes of opening or closing a system.8 More often than not, when dealing with a system, we have to modify it in order to be able to understand its functioning or develop a policy. The ways in which a system is opened or (more usually) closed is of utmost importance. Science is for the most part a set of techniques for closing open systems in order to scrutinize them. The problem is, it is in this way we study other systems, systems that are different from the original ones.


1. Bhaskar, R. 1998. The Possibility of Naturalism, London, Routledge (3rd edition)
2. Louie, A. H. 2009. More Than Life Itself: A Synthetic Continuation in Relational Biology, Frankfurt, Ontos Verlag
3. Poli, R. 2010a. “The Many Aspects of Anticipation,” Foresight, 12(3), 7-17
4. Poli, R. 2010b. “An Introduction to the Ontology of Anticipation,” Futures, 42(7), 769-776
5. Poli, R. 2010c. “The Complexity of Self-reference – A Critical Evaluation of Luhmann’s Theory of Social Systems,” Journal of Sociocybernetics, 8(1-2), 1-23
6. Poli, R. 2011a. “Ethics and Futures Studies,” International Journal of Management Concepts and Philosophy, 5(4), 403-410
7. Poli, R. 2011b. “Analysis—Synthesis,” in V. Petrov (ed.), Ontological Landscapes, Frankfurt, Ontos Verlag, 19-42
8. Poli, R. 2012a. “The Many Aspects of Anticipation,” in M. N. Seel (ed.), Encyclopedia of the Sciences of Learning, New York, Springer, 2092-2094
9. Poli, R. 2014. “Anticipatory Governance, Auftragstaktik, and the Discipline of Anticipation”. Forthcoming in the Journal of Futures Studies
10. Popper, K. R. 1990. A World of Propensities, Bristol: Thoemmes
11. Rosen R. 1991. Life Itself. A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life, Columbia UP, NY, 1991
12. Rosen, R. 2012. Anticipatory Systems. Philosophical, Mathematical and Methodological Foundations, New York, Springer (1st ed. Pergamon Press 1985)

‡ During the past fifty years or so, many scholars have tried to contribute to this body of ideas, including Bateson, Capra, Hofstadter, Luhmann, Maturana, Rashevsky, Rosen, and Varela. The clearest and most complete treatment, however, is Rosen’s (1991).
§ While the traditional, reductionist strategy has proved enormously successful and cannot be simply abandoned, the problems that prove refractory to a reductionist treatment are growing, and this calls for complementary non-reductionist strategies. Reductionist methods work well when a system can be decomposed (fragmented) without losing information. On the other hand, for many systems, any fragmentation causes a loss of information (Poli 2011b).
The most promising alternative strategy is to substitute analysis via decomposition (the reductionist mantra) with analysis via natural levels (i.e. the theory
of levels of reality), introduce indecomposable wholes and substitute Humean causation with powers and propensities. Note that, since indecomposable wholes are not (entirely) understandable from their parts, manipulation of parts may engender unexpected consequences (Popper 1990, Rosen 1985, Bhaskar 1988, Poli 2010a,b, Poli 2011a, Louie and Poli 2011, Poli 2012a,b).
3. David Depew and Bruce Weber, Darwinism Evolving: System Dynamics and the Genealogy of Natural Selection (Cambridge: The MIT Press, 1995)
4. Barbara Adam and Chris Groves, Future Matters (Leiden: Brill, 2007)
5. Robert E. Ulanowicz, A Third Window: Natural Life beyond Newton and Darwin (West Conshohocken: Templeton Foundation Press, 2009)
6. A. H. Louie and Roberto Poli, “The Spread of Hierarchical Cycles,” International Journal of General Systems 40, no. 3 (2011): 237-261
7. Roberto Poli, “Overcoming Divides,” On the Horizon 21, no. 1 (2013): 3-14
8. Robert Rosen, Essays on Life Itself (New York: Columbia University Press, 2000)

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