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be diffi cult to defi ne beyond the general focus on synergy. This has led some
researchers to the more grounded concept of supervenience, which asserts that
changes in a system at one level are tied to changes at another level, and even small
changes in one level can lead to large changes in another (Sawyer, 2002). We
examine emergence below in greater detail.
Is Complexity New?
Complexity research is often promoted as a fundamentally 'New Kind of Science'
(after Wolfram, 2002), but it has deep conceptual roots. Such research refl ects long-
standing philosophical ideas, among them Aristotle's metaphysical work on synergy
and Whitehead's philosophy of organism, which contends that nature is not merely
a set of fi xed laws or circumstances, but instead is a continually evolving process
(Whitehead, 1925). Complexity also shares features with cybernetics, the study of
how feedback in systems relates to communication and control in entities ranging
from organisms to machines to social institutions (Wiener, 1961). Complexity can
also be traced to specifi c computational and analytical approaches like neural net-
works, computer or mechanical programs that mimic biological brain functioning
(McCulloch and Pitts, 1943), and cellular automata, simple computer programs that
interact with one another (von Neumann, 1966). It also shares attributes with
general systems theory, which posits that many human and natural systems can be
understood by holistically treating them as stocks and fl ows of energy, matter, or
information (von Bertalanffy, 1968).
Complexity theory differs from earlier movements in general systems theory,
computer science, or philosophy in its treatment of relationships among entities
(Phelan, 1999). These earlier efforts typically concentrated on fi xed entities and
stocks, such as animal populations linked by linear fl ows of energy or matter. Com-
plexity focuses more on how systems evolve or emerge from simple, local interac-
tions among individual system components. Systems theory, and much of current
systems dynamics modelling, focuses on parameterising fl ows and stocks of energy
or matter existing in equilibrium. Conversely, much complexity research contends
that systems often exist in disequilibrium or near the edge of chaos. Riverbanks, for
example, can be modelled as systems where bank erosion balances deposition of silt
and other matter. The balance between these two forces, however, is not always a
gradual to-and-fro, but instead a system that shuttles between them, which results
in periods of stability marked by sudden riverbank failures (Fonstad and Marcus,
2003). Similarly, complex systems as envisioned by aggregate complexity may have
emergent characteristics that cannot be explicitly specifi ed in advance of running a
model as a series of entities and their interrelationships. While the notion of a system
being more than the sum of its parts has long been central to systems thinking,
complexity research is interested specifi cally in how systems evolve over time as a
function of relationships among the entities that comprise them.
Simplicity and Complexity
A perennial tension in using complexity concepts is reconciling the theory and ethos
of complex systems with the complicated nature of the real world. In particular, it
is diffi cult to computationally represent environmental phenomena like ecosystems
