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The terms are not in alphabetic order, but follow a cumulative sequence of exposition. The
terminology will be used as the starting point for the creation of a Lexicon of
organisational complexity, which will be compiled by a study group at the LSE, for
publication.
Context: The following terms refer to the work done over the past three
decades by scientists in the fields of physics, chemistry, biology, economics and
mathematics. Many of these scientists are associated with the Santa Fe Institute in New
Mexico, but also include scientists based in Europe, such as Prigogine, Sengers,
Nicolis,
Allen and Goodwin. There is no one theory of complexity, but many such theories arising
from the various sciences studying complex adaptive systems (CAS).
As many of these concepts are borrowed from the natural sciences, their use within an
organisational and management context, can lead to misapplication and misunderstanding.
The research project will attempt to clarify the concepts and principles which are common
to all complex systems and to explore their application to organisations. A series of
seminars on Strategy & Complexity and a Study Group on Organisational Complexity, will
be held at the LSE and will endeavour to stimulate the dialogue between academics from a
variety of disciplines and business executives, through the discussion of work in progress
as well as the application of the principles of complexity in organisations.
Complexity: there are many
definitions of complexity and all are context-dependent. For the purposes of this
proposal, complexity is defined as organisational complexity and is associated with the
intricate inter-relationships of individuals, of individuals with artifacts (such as IT),
and with the effects of inter-actions within the organisation and between organisations
and their 'environment' which includes related businesses. Complexity arises through
connectivity and the inter-relationships of a system's constituent elements. The way these
inter-relations arise, the way they help maintain and create new patterns and structures
which enable an organisation to evolve, is not well understood. Complexity in this context
is also associated with the characteristics of non-linearity*, self-organisation*,
emergent properties*, far-from-equilibrium* operation and sensitivity to initial
conditions*.
Complex evolving systems (CES) refers to those systems which are able to
learn and which change their internal structure and organisation over time, thus changing
the behaviour of individual elements. The term complex adaptive systems is used by the
Santa Fe scientists to describe complex systems which adapt through a process of
self-organisation* and selection. However, physical, chemical and biological systems are
not conscious and do not 'learn' in the sense that humans learn. Hence the term complex
evolving system [Allen, 1996] is used in this proposal to distinguish human from other
complex systems. Both CAS and CES are subject to change through mutation or totally
unexpected change, which is then subject to adaptation. Characteristics of complex
systems: The study of natural complex systems has shown that all complex systems share
certain generic characteristics. Some of these characteristics are included in this
terminology and the research project will explore their application to social systems.
Such application, however, questions long held assumptions and has profound implications
for management, methods of work, the shape of organisations, and the development and use
of information technology.
Non-linearity & multiple
outcomes: Modelling of aggregate behaviour in organisations is usually based on
the assumption that all individuals exhibit average and thus predictable
behaviour, when
organisations are entities made up of individuals who interact, are mutually
inter-dependent and exhibit non-average behaviour. Through multiple inter-actions,
organisations are capable of many possible responses; that is, they are complex,
unpredictable, non-linear systems, producing multiple outcomes. Yet they are studied as if
they were simple, linear systems guaranteed to produce a single, predictable outcome.
Another aspect which is often ignored, is that any outcome is influenced by a number of
contributing factors. These factor cannot all be taken into account for various reasons;
they may not be known, may not be quantifiable or they may be ignored as relatively
insignificant, yet these factors may be subject to the phenomenon known as sensitivity to
initial conditions*, which could lead to unforeseen and often undesirable consequences.
Sensitivity to initial
conditions:
when a small change in the initial conditions produces major and unpredictable qualitative
changes. Traditional approaches implicitly assume that events occur at an average rate
(there are exceptions, and Robust Planning for example does not make that assumption) and
that they can be adjusted if they deviate from the desired plan by employing the
appropriate adjustment mechanism. But events do not unfold with average regularity and
adjustments rarely produce the desired effect. No planning mechanism can take all initial
and influencing conditions into account, and at times a small change in the initial
conditions produces major and unpredictable qualitative changes. This coupled with
positive feedback or increasing returns [Arthur 1990, 1995], makes accurate forecasting
and the planning of specific outcomes extremely difficult.
Innovation as exploration of the
space of possibilities: Traditional approaches also ignore an organisation's
capacity to learn and change and to maintain diverse and varied strategies, assuming that
a single 'optimum' strategy is both possible and desirable. The sciences of complexity
have shown that for an entity such an organisation to survive and thrive it needs to
explore its space of possibilities and to encourage variety. When markets were stable and
growth was a constant, single optimum strategies based on extrapolation from historical
data, were thought to be feasible. But unstable environments and rapidly changing markets
require flexible approaches based on variety. [Ashby, 1956]
Far-from-equilibrium:
Economic models often assume that a state of equilibrium is a desirable condition, but the
sciences of complexity show that systems which survive and thrive, do so when they are
pushed away from equilibrium, while if they remain at equilibrium they die. When
far-from-equilibrium, systems are forced to experiment and explore their space of
possibilities and this exploration helps them discover and create new patterns of
relationships, different structures and innovative ways of working. [Prigogine,
Nicolis]
Non-linear dynamics (or chaos theory) may be used to explain the emergence of these new
patterns as analogous to the transition phase of bounded instability, between stability
and instability which is a state of creativity and innovation. [Gleick 1990, Parker &
Stacey 1994] Although non-linear dynamics is an integral part of the theories of
complexity it is only one aspect and needs to be balanced by the broader realm of
understanding offered by complexity. Analogies based on the 'edge of chaos' need to be
made applicable to social systems and organisations. One key question which will be
addressed in both phases of the research project is the balance between stability and
instability necessary to encourage innovation while avoiding both instability and
stagnation. At the transition state between stability and instability, order and
organisation may arise spontaneously out of disorder through a process of
"self-organisation".
Self-organisation: The
spontaneous organisation of the system's elements into coherent new patterns, structures
and behaviours. Change in human organisations may be brought about by the spontaneous
self-organisation of individuals. These new patterns are not decreed, designed or imposed
by any specific individual. They simply happen. They may subsequently dissolve and leave
little trace or they may have a longer lasting effect and change the structure of the
organisation. In the latter case true evolution has taken place and the internal structure
of the organisation has changed. We need to understand how to encourage self-organisation
as a means of creating new innovative patterns of behaviour as well as a means of
devolving the strategy process throughout the organisation. If the organisation of the
future is to work on a different basis, that pattern or shape will need to emerge and
evolve from a given set of simple principles. [Allen, Bovaird, Goodwin, Holland,
Kauffman,
Lane, Nicolis, Varela]
Dissipative structures (Prigogine
& Stengers 1985) are open systems exchanging energy, matter or information with their
environment. In Prigoginian terms, all systems contain subsystems which are continually
"fluctuating". When one or more fluctuations become so powerful, as a result of
positive feedback, that they shatter the preexisting organisation, the system has been
forced into a far-from-equilibrium condition and has reached a point of bifurcation. It is
inherently impossible to determine in advance which direction change will take. The system
may disintegrate into instability or leap to a new level of order or organisation called a
"dissipative structure". It is given that name because it requires more energy
(or information) to sustain it than the simpler structure it replaced. In terms of the
flow of information, a stable system can be sustained with a sluggish flow, but a much
more vigorous and richer flow is necessary for a system operating far-from-equilibrium. If
the flow of information becomes too fast, however, then the system may disintegrate.
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Context
Complexity
Complex evolving systems
Non-linearity & multiple
outcomes
Sensitivity to initial
conditions
Innovation as exploration of the
space of possibilities
Far-from-equilibrium
Self-organisation
Dissipative structures |
