Cities are excellent examples of complex adaptive systems and display all of the signature characteristics of complex systems.
Complexity and emergence
The idea of complexity hinges on the notion of emergence. In emergent
systems, a small number of rules or laws, applied at a local level and
among many objects or agents, are capable of generating surprising
complexity in aggregate form. These patterns manifest themselves in
such a way that the actions of the parts do not simply sum to the
activity of the whole. Essentially, this means that there is more going
on in the dynamics of the system than simply aggregating little pieces
into larger units. Although often ordered in their structure, the
complex systems that are generated are not always just random or
chaotic; recognizable and ordered features can emerge. Additionally,
these systems are dynamic and change over time and the dynamics often
operate without the direction of a centralized executive. Examples of
emergent systems abound. For example, the liquidity of water is more
than a simple extrapolation of characteristics that can be attributed
to individual water molecules, which have no liquid quality of their
own. Similarly, in economics, the activity of individual market
participants, trading without centralized control, often leads to
aggregate outcomes that are relatively efficient, as efficient as if
they were controlled. |
Reductionism versus synthetics
This detailed, bottom up approach to complexity is, in some senses, a
relatively new way of approaching scientific inquiry. Much research in
the social sciences, and particularly in geography, is challenged by a
dichotomy between the individual (the household, a person, and
independent objects) and the aggregate (populations, collectives, and
regions). In a spatial sense, researchers have been confronted with the
dilemma of reconciling patterns and processes that operate and manifest
at local scales with those at larger scales. This can be considered as
a problem of ecological fallacy.
An ecological fallacy occurs when it is inferred that results based on
aggregate data can be applied to the individuals who form the
aggregated group. A related problem in geography is the Modifiable
Areal Unit Problem. Of course, there are many examples in which
aggregate forms may be extrapolated from the individual. However,
reconciling the two often poses a challenge, particularly when
processes that operate at the local level are interdependent, i.e., the
actions of one individual depend on the actions of another individual.
In these cases, an understanding of the processes that generate
macro-scale patterns may not be easily gleaned by simply aggregating up
from the individual; what is needed instead is an understanding of the
interactive dynamics that link local-scale and larger-scale phenomena.
This is an argument of reductionism versus synthetics.
The reductionist approach analyzes problems by breaking them down to
their constituent components, reducing them to manageable pieces and
gaining an understanding of them in the process. In some cases this
approach works quite well, and for many phenomena the technique is
wholly appropriate: particularly in situations where the whole is the
sum of many small parts. However, the reductionist approach is flawed
in the respect that it may miss the emergent properties of a system:
those that come as a by-product of the interactive dynamics of
individual elements. In many instances, a synthetic approach may be
more appropriate.
In the context of this discussion, the synthetic or generative approach
involves studying phenomena by experimenting with simple rules for
behavior and allowing constituent components to interact, dynamically,
until macro-scale phenomena emerge--a piecing together rather than a
dissection. This is what happens in our own bodies. The rules encoded
in our DNA specify a set of behaviors for the development of our
biology over time. The products of that interactive development on a
genetic level are macro-scale structures-organs, systems, and
traits-that bare little resemblance to the original components of our
DNA. The central nervous system, for example, is significantly more
complicated than the arrangement of bits of guanine, adenine, thymine,
and cytosine along a genome. Researchers are increasingly adopting
synthetic approaches to the study of phenomena, particularly in
studying life, where it has been noted that, "Reductionism does not
work with complex systems, and it is now clear that a purely
reductionist approach cannot be applied when studying life: in living
systems the whole is more than the sum of its parts." (Stephen Levy, Artificial Life) These methodologies are also extending into other fields, including the social sciences and urban studies. |
Criticisms of complexity
Complexity studies are in their infancy as an academic discipline, but
they have drawn a relatively heavy degree of criticism recently,
perhaps as a by-product of the attention afforded the field and its
pioneers in popular science journalism and publishing. In particular,
there have been accusations that a gap exists between the 'rhetoric' of
complexity studies and reality. This is really a multifaceted reaction
against complexity studies. The field has been criticized for harboring
a 'reminiscence syndrome'. Also, there has been a backlash against the
claim of some complexity researchers, particularly those at the
flagship Santa Fe Institute, that complexity can offer a unified theory
of everything. Moreover, there have been growing concerns that the
techniques the field is offering up for the study of complexity are
even more complicated than the phenomena they purport to represent;
that researchers are moving from complexity to perplexity.
What was once thought to be the great strength of complexity has turned
into one of its chief criticisms. The intuitive sense that the idea of
complexity conjures owes a great deal to the idea of reminiscence:
"Look, isn't this reminiscent of a biological or physical phenomenon."
(Jack D. Cowan, co-founder of the Santa Fe Institute, quoted in Scientific American, 1995).
Reminiscence criticisms accuse researchers of yielding to the
"seductive syllogism" of complexity, particularly in the use of
computer-based models to explore complexity. Just because the dynamic
activity displayed in a computer model resembles a real-life process,
does not necessarily mean that it is a good model for that phenomenon.
Researchers may assume that reminiscence alone is justification for a
modeling paradigm, when really that reminiscence may be accidental,
coincidental, or may be a construct of the researcher's own ideas.
Others would defend themselves by countering that while complexity may
be guilty of reminiscence, the mechanisms of processing in naturally
appearing complex systems are very like those in computers, and
particularly in CA.
One of the goals of complexity
studies is to abstract simple features of complex behavior that are
common across a wide-range of systems, and perhaps to devise universal
laws of complex systems from those common principles. As Stephen
Wolfram puts it, "To discover and analyze the mathematical basis for
the generation of complexity, one must identify simple mathematical
systems that capture the essence of the process." Wolfram goes on to
speculate that universal laws analogous to the laws of thermodynamics
might be discovered for complex systems. However, there has been a
backlash against the claims for a unifying theory of complex systems.
Contrast Wolfram's sentiments with those of John Casti, expressed in
the introduction to his book, Would-be Worlds:
"it's really a pity that this book is not crammed full of mathematical
arcana, since if it were it could only mean that we had something that
looked like a decent theory of complex systems. In fact, we are not
even close." There are two justifications for doubting our ability to
arrive at universal laws of complexity, both of which center on the use
of computers to explore complex phenomena. The first relates to the
fact that some problems are not computable. The second centers on a
belief that complexity models may be more complex in themselves than
the phenomena that they are trying to simulate.
By
their very nature, computing machines are rule following devices; yet,
there is no reason to believe that all processes in the natural world
are rule-based. Some processes in the natural and physical worlds, and
many complex systems, may not be computable. In geography and urban
planning, the introduction of simulation techniques from complexity
studies was heralded with suspicion. In particular, researchers feared
that tinkering with the simple formalisms of techniques such as CA in
order to better tailor them to simulating geographic phenomena might
yield model structures as complicated as the realities that they were
designed to represent. In a true simulation model, the inputs and
states of the real-world object must be encoded in the states of the
simulated phenomena. Consequently, the simulated phenomena will have to
have more states than the real-world object, and thus the simulation
must by necessity be more complicated than the thing(s) being
simulated. The danger here is that in designing accurate models of
complex systems, we may end up with simulations that can be no better
understood than the systems that they simulate.
The criticisms of complexity are appropriate in many instances. Yet, to
reject complexity outright at this stage would be unwise; the field has
a lot to offer. Really, the important message to understand here is
that complexity has relevance to many systems, but not to all. This is
also true in the context of the city. Many urban systems lend
themselves to the complexity approach, but others-especially those that
operate from the top-down-really don't. Nevertheless, the approach does
provide a rich environment for understanding how systems work
dynamically and interactively, as well as offering some innovative
techniques for simulating such phenomena.
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