Risk assessment and management of civil infrastructure networks: a systems approach - Seismic Risk Analysis and Management of Civil Infrastructure Systems - page 430

A system is described in terms of a purpose, which identifi es it as a whole;
e.g., the nervous system as a unit is distinguished from other systems within
an organism. The parts of a system can also be considered as a whole in
terms of different purposes; e.g., a neuron, as part of the nervous system, is
a whole for the purpose of perception. The latter corresponds to the concept
of holon : an element which is part and whole at the same time. The way in
which systems are embedded into more complex systems to accomplish
more complex purposes leads to a hierarchical structure. The concept of
hierarchy usually implies relationships of command, but in this context, it
will be used to describe recursion of systems and subsystems. Emergent
properties are those that can be observed in the whole, but not in its parts
in isolation; e.g., cognition is a property of the nervous system, not of
neurons.
A system can be defi ned as a set of interacting elements exhibiting
closure and synergy: closure implies that the network of elements is per-
fectly distinguishable from its environment, while synergy implies that the
whole is more than the sum of the parts (Checkland, 1981; Blockley and
Godfrey, 2000). The defi nition of a system is tied to an observer, who
ascribes a purpose to it: 'everything is said by an observer' (Maturana, 1987).
There is a boundary that separates the system and its environment. More-
over, the system may interchange energy, matter, and information with its
environment. Therefore, the system interacts with the environment/
observer. In the context of this chapter, this means that the defi nition and
analysis of systems must be tied to decision-makers' interests.
The British cybernetician Ross Ashby (1964) defi ned variety as the
number of possible states that a system can take. This concept accounts for
potentiality, whereas complexity accounts for actuality. For example, a traffi c
light (having three lights with two states, on/off) might take 2 3 8 different

states (variety), but its complexity is lower (at most 4 states) because not
all permutations are part of the system's behaviour that is relevant to
observers. Furthermore, uncertainty complicates the defi nition and treat-
ment of these issues. Ashby's Law of Requisite Variety implies that only
complexity absorbs complexity. Thus, a decision maker cannot cope with
complexity of a system (e.g., infrastructure) that exceeds his/her own com-
plexity (understood as the capacity to deal with the problem). It is necessary
to apply complexity attenuators and response amplifi ers (Beer, 1967) in
order to deal with complex problems appropriately (Fig. 17.1). For instance,
an organization may attenuate the complexity of customer service by using
automated phone-call response to fi lter requests that do not need personal-
ized attention; on the other hand, response amplifi cation could be achieved
by publishing online help information about common customer requests.
The proposed systems approach uses clustering methods to fi lter/aggregate
information from networks, which acts both as an attenuator of complexity

=