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WHAT IS COMPLEXITY?
AN INTRODUCTION FOR EDUCATORS
Sara Heinrich and Ella Jamsin, Ellen MacArthur Foundation
HISTORICAL FOUNDATIONS
The historical roots of complexity science are numerous and draw notably from
thermodynamics and the theory of evolution. To mention a few:
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A range of related schools of
thought emerged in the early 20th
century, such as Smut’s ‘Holism’,
Bertalanffy’s ‘General Systems
Theory’ and Ashby’s Cybernetics.
In natural sciences, Ilya Prigogine
played a key role in studying
complex systems in chemistry in the
1950s, uncovering key insights into
path dependency and irreversibility.
Around the same time, Jay Forrester
pioneered system thinking in
management, and created system
dynamics, a methodology to
visualise and analyse the behaviours
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of complex systems. His team at
MIT was later joined by Donella
Meadows, member of the Club
of Rome and of the team that
produced the global computer
model supporting ‘The Limits to
Growth’.
In 1984, a group of physicists
created the Santa Fe Institute,
an independent theoretical
research institute dedicated to
the multidisciplinary study of
complex adaptive systems. It is now
recognised as a leading institution in
complexity science research.
TO DIVE DEEPER, EXPLORE THE
WORK OF SOME OF THE FOLLOWING
AUTHORS:
Jan Smuts (1870-1950)
Holism and evolution
Ludwig von Bertalanffy (1901-1972)
General systems theory
William Ross Ashby (1903-1972)
Cybernetics
Alan Turing (1912-1954)
Morphogenesis (theory of how
biological growth occurs)
Ilya Prigogine (1917-2003)
System boundaries, open systems,
thermodynamics
Jay Forrester (1918-2016)
Organisational learning
Elinor Ostrom (1933-2012)
Political economy and ‘tragedy of the
commons’
Donella Meadows (1941-2001)
Thinking in systems
Brian Arthur (1946-)
Complexity economics
Peter Senge (1947-)
Organisational management
Marten Scheffer (1958-)
Ecological resilience and tipping points
CONCEPT AND RELEVANCE
Most real-world systems are complex.
They cannot be understood by
analysing their separate parts, often
called agents, because these agents
are strongly linked by feedback
relationships: the agents’ behaviour
influences the whole and the whole
influences the behaviour of agents.
As a consequence, you can’t accurately
predict the future state of the system:
a very small change in initial conditions
can have surprisingly large effects.
The weather is a typical example of a
complex system.
Complex systems are often described
as adaptive (and known as ‘complex
adaptive systems’) because they can
adapt to changes in their environment.
Learning is a particular case of
adaptation, particularly present in
systems of animals, where the
experience of a previous change in
conditions is stored in the system’s
memory and influences future
behaviours.
Complexity has become a highly
interdisciplinary topic today, building
bridges across for example biology,
physics and social sciences. It relies
on computer modelling and machine
learning, and finds applications in
education and management. This
reflects the fact that most real-world
systems are complex and adaptive, and
so increasingly are our technologies.
Through the study of flocks of birds,
social media and artificial intelligence,
to name but a few, complexity
researchers have uncovered a number
of principles that can help understand
the dynamics of all complex systems
(see box on the next page).
All organisations, including schools
and workplaces, are complex adaptive
systems of people. Their level of (de)
centralisation of decision making,
learning capacity, nature of hierarchy
and self-organisation, and feedback
mechanisms all influence their
dynamics and success. The history
of an organisation shapes its future
development or, in other words, the
evolution of an organisation is path
dependent.
The economy is another complex
adaptive system, strongly coupled
with ecological systems. This fact
is taken into account for example
in the model of circular economy,
which emphasises the importance of
feedback loops and draws from the
notion of resilience – the ability of the
system to maintain its core function
in the presence of changes in its
environment. In a circular economy
stocks and flows of resources, such
as money, materials, information and
energy, are acknowledged to interact
with each other. Designing a product
or service to fit into such an economy
demands considering its interactions
with economic and ecological systems
along its entire lifecycle. Likewise, any
organisation active in the transition to a
circular economy needs to consider its
interactions with the wider system and
pay attention to emergent behaviours
– system level effects that cannot
simply be explained or predicted
from the actions of individuals and
organisations.
The science of complex systems can
also shed light on how systems change
and therefore provide critical insights
on how to create better economic,
environmental and social outcomes.
Change does not always take place
gradually, but sometimes through a
critical transition after the system
reaches a tipping point. This also
brings in the concept of resilience,
but this time as something we might
want to break in order to move to a
different, more effective model. We
can accelerate transitions by creating
the right conditions - the rules of the
game - in which an adaptive system
can spontaneously evolve towards
a more positive outcome. We might
for example leverage the high (and
increasing) level of connectedness
between people in the modern world
to spread new ideas and practices.
Developing better models of the
economy and how it changes,
sharpening our ability to think in
systems, and leveraging concepts of
complexity in the way we work are just
a few of the practical implications of
complex systems for individuals and
organisations.
A SUMMARY OF COMPLEX SYSTEMS
PRINCIPLES 1
Basic principles
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A system is more than the sum of
its parts.
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Many of the interconnections in
systems operate through the flow
of information.
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A system’s structure influences its
behaviour.
Stocks and flows
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A stock is the memory of the
history of changing flows within
the system.
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A stock can be increased by
decreasing its outflow rate or
increasing its inflow rate.
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Stocks allow inflows and
outflows to be de-coupled and
independent as they act as delays,
buffers and shock absorbers in
systems.
Feedback loops
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A feedback loop is a chain of
causal connections that runs
from a stock – through a set
of decisions, rules or actions
dependent on the level of that
stock – to a flow, which once it
changes will alter the level of that
stock.
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This list is an adapted version of an
overview provided in Donella H. Meadows,
2008, Thinking in Systems – A primer (White
River Junction: Chelsea Green), pp. 188
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Balancing feedback loops push
a system towards an equilibrium
or goal and are therefore both
sources of stability and resistance
to change (for example, a price
increase causes a reduction in
demand, which in turn causes a
decrease in price).
Reinforcing feedback loops
are self-enhancing and lead to
exponential growth or runaway
collapse over time (for example,
technological innovation provides
the means to develop further
technologies).
• The information delivered by a
feedback loop—even nonphysical
feedback—can affect only future
behaviour: it can’t deliver a
signal fast enough to correct the
behaviour that led to the current
feedback.
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A system often exhibits complex
behaviour as the relative
strengths of its feedback loops
shift, causing first one loop and
then another to dominate.
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A delay in a balancing feedback
loop makes a system likely to
oscillate.
Changing the length of a delay
may drive a large change in the
behaviour of a system.
Constraints
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In physical, growing systems,
there must be at least one
reinforcing loop driving the
growth and at least one balancing
loop constraining it, because no
system can grow forever in a finite
environment.
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Non-renewable resources are
stock-limited while renewable
resources are flow-limited.
.
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The resilience of a system
describes “the ability of a system
to persist and maintain its core
function and/or purpose in
the presence of disturbances,
stress or other changes in its
environment.”2
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There are always limits to
resilience.3
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Complex systems can transition
from one phase to another
suddenly and unexpectedly after
reaching a tipping point.
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In the presence of strong
reinforcing feedback loops, a
tipping point can occur when the
resilience of the system is low.
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Complex adaptive systems
often have the property of selforganisation, i.e. the ability
to structure and re-structure
themselves, to learn, diversify, and
increase their complexity.
Systems with similar feedback
structures produce similar
dynamic behaviours.
Dominance, delays, and oscillations
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Resilience, tipping points and selforganisation
Path dependency
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The state of a complex system at
a point in time depends on the
sequence of events and decisions
that preceded that point.
Source of surprises
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Many relationships in complex
systems are nonlinear, meaning
that a small change in the system
can lead to disproportionate
effects.
2
Complexity Explorer glossary https://
www.complexityexplorer.org/explore/glossary
3
See for example the concept of ‘planetary boundaries’, i.e. thresholds beyond which
the structure of the planetary ecosystem could
tip into a new regime, developed by Johan
Rockström and 28 other scientists in a 2009
article ‘Planetary Boundaries: Exploring the
Safe Operating Space for Humanity’. To learn
more about it, Johan Rockström TED talk is a
good starting point: http://www.ted.com/talks/
johan_rockstrom_let_the_environment_guide_
our_development?language=en
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When there are long delays in
feedback loops, foresight is
essential.
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Rational optimising decisions
made by each actor in a system
may lead to suboptimal results for
the system as a whole.
Mindsets and models
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Everything we think we know
about the world and its subsystems is a model.
• Strictly speaking there are no
separate systems – the universe
is a continuum – so where to
draw boundaries depends on
the purpose of the discussion
and therefore what we want to
model.
•
Our models of complex systems
can have strong congruence with
their true nature, but fall far short
of representing them fully.
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Our mental models underpin and
drive our beliefs and actions.4
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The usefulness of a model
depends not on whether it makes
good predictions (since no such
certainty can be obtained), but
on whether it exhibits realistic
patterns of behaviour.
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INTERCULTURAL LINKS
Early Western philosophers and
Eastern philosophical traditions
developed worldviews in the 6th
century that are almost identical
to complexity, focusing on flow,
interdependency and the emergence
of structures and patterns. Why then is
the world today mostly governed by a
mechanistic worldview?
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George Lakoff’s work is a good starting point to further explore this concept and
includes the books Don’t think of an Elephant
and The Metaphors we live by.
While these concepts continued to be
important to Eastern philosophies, in
the West a transition started with the
dialogues of Plato and Socrates, who
introduced discussion and reasoning
– the dialectic method – and decided
that form does not emerge but is
governed by rules. Such ideas led,
a thousand years later, to Newton’s
physics, a remarkable scientific
breakthrough that nonetheless sowed
the seeds of modern worldviews in
which the natural and social worlds are
seen as measurable and predictable.
Many modern scientific disciplines
developed since are reductionist:
they attempt to describe systems in
terms of their constituent parts. While
this method has given rise to very
significant scientific progress and new
discoveries, when it comes to complex
systems, such an approach may
underplay the higher-level phenomena
emerging from the interactions
between the parts.
This way of thinking culminated in the
Industrial Revolution where machines
- reductionist by design - drove
unprecedented economic growth
and development. This reinforced
the mechanistic worldview and its
underlying assumption of control: “we
could predict what would come out of
the man-made system, provided we
guaranteed consistency of feedstock.”5
Thinking of processes and systems
surrounding us as ‘mechanisms’ with
cogs and wheels and simple causalities
proved useful and, as machines entered
everyone’s lives, so did the mechanistic
mindset and linear thought processes.
Techniques however continued
evolving and the advent of digital
technology and computing power
5
Ellen MacArthur, Only a circular
economy will lead to prosperity for all, http://
circulatenews.org/2016/04/only-a-circulareconomy-will-lead-to-prosperity-for-all/
REFERENCES AND FURTHER
READING
As an example, today’s dominant
economic thinking is the legacy of a
reductionist model developed in the
19th century in an attempt to mimic
the physical sciences. This approach
enabled great progress in economic
theory but led to the development
of models that fail to reflect key
macroeconomic patterns, most notably
financial crises. Complexity economics,
a movement in its infancy, is trying to
address this shortcoming by applying
the framework of complexity to the
economy, thereby seeing it “not as a
system in equilibrium but as one in
motion, perpetually ‘computing’ itself perpetually constructing itself anew.”6
Arthur, W.B. 2013. Complexity
Economics: A Different Framework for
Economic thought (Santa Fe Institute)
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allowed models to be developed that
better reflect the real world and its
complexity. An illustration of that
evolution is agent-based modelling,
which takes into account feedback
between the whole and its parts
and can demonstrate emerging
phenomena. These new tools
have boosted the development of
complexity science. In many areas of
work, such as education, management,
and the environment, thought leaders
are increasingly recognising the
complex adaptive nature of real-world
systems and are therefore using a
systems thinking approach to study
and influence them.
6
Arthur, W.B. 2013. Complexity
Economics: A Different Framework for
Economic thought (Santa Fe Institute)
The following sources were consulted
during the preparation of this paper.
They also provide good references to
learn more about complexity.
De Rosnay, J. 1979. The Macroscope.
(New York: Harper & Row)
Meadows, Donella H. 2008. Thinking
in Systems – A primer (White River
Junction: Chelsea Green)
Mitchell, M. 2011. Complexity: a Guided
Tour (Oxford University Press)
Colander, D. and Kupers, R. 2014.
Complexity and the Art of Public Policy
(Princeton: Princeton University Press)
Webster, K. 2015. A Wealth of Flows
(Isle of Wight: Ellen MacArthur
Foundation Publishing)
Boulton, J. Allen, P. and Bowman, C.
2015. Embracing Complexity – Strategic
Perspectives for an Age of Turbulence
(Oxford: Oxford University Press)
Complexity Explorer. Various courses
and resources, including ‘Introduction
to Complexity’ by Melanie Mitchell
and a glossary of complexity
terms, are available on the website
complexityexplorer.org developed by
the Santa Fe Institute.