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Complexity Increases And Then…
(Thoughts From Natural System Evolution)
Darrell Mann
Director, CREAX nv
Ieper, Belgium
Phone/Fax: +44 (1275) 342960
E-mail: darrell.Mann@creax.com
“Life is much less a competitive struggle for survival than a
triumph
of co-operation and creativity. Indeed, since the creation of
the first
nucleated cells, evolution has proceeded through ever more
intricate
arrangements of co-operation and co-evolution.”
Fritjof Capra(1)
Introduction
According to the classic TRIZ trend, over the course of their complete
evolutionary existence, systems evolve in a manner that sees them first increase
and then decrease in complexity (2, 3) - Figure 1. This characteristic is shown
to be inconsistent with the process of natural evolution. This article describes
some of the reasons underlying the inconsistency and the implications they have
on our interpretation and application of the TRIZ trend.
Figure 1: TRIZ Complexity Trend
(Note: the precise shape of the characteristic can vary
considerably depending on the system being considered - for complex systems, the
reduction in complexity tends to be as shown; for relatively simple systems the
reduction in part-count or complexity is more usually manifested as a sudden
step-change)
Evolutionary Processes In Nature - A Review
Although there is no absolute consensus amongst biologists, it appears clear
that the evolution of natural systems over the course of the 4.5 billion year
existence of the Earth has taken place due to three basic mechanisms:-
1) Random Mutation - the classic Darwinist/neo-Darwinist view that
systems evolve through a combination of random mutation and natural
selection; random mutations produce variation, and natural selection
filters out the less successful variations.
2) Gene Trading - the success of bacteria in fighting our attempts to
control them lies for the most part in their ability to ‘communicate’ with
one another. The ‘gene-trading’ process allows different bacteria cells to
‘pass-on’ successful survival strategies from one to another.
3) Symbio-Genesis - the newest of the emerging evolution models, symbio-genesis
(4) describes how nucleated cells are much more likely to evolve through
the transition of a symbiotic co-existence of different cell types and
structures from temporary to permanent.
According to Margulis (4), the evolution mechanisms become more significant
as we read down the list - such that symbio-genesis is a many times more
significant factor governing the evolution of complex systems than random
mutation. While it is not the purpose of this article to question these
different mechanisms, it is worth reflecting on their links with some important
TRIZ concepts. The first is that ‘gene trading’ is closely related to the TRIZ
concept of knowledge transfer between different sectors and its importance in
the innovation process (Level 3 inventions are, for example, all the result of
this kind of inter-disciplinary communication and transfer (2)). The second is
that symbio-genesis is highly consistent with some of the most important
Inventive Principles and trends of evolution (for example Merging and the
various Mono-Bi-Poly trends).
Capra (1) suggests that the symbio-genesis evolution strategy has played a
significant role in the creation of life as we understand it. In particular, if
we draw the complexity - calculated in relation to number of different cell
types present - of biological systems as a function of evolutionary time, we
will produce a plot something like that illustrated in Figure 2.
Figure 2: Organism Cell Types Versus Time
The most interesting feature of this picture, and the more general
evolutionary model for natural systems is that complexity has consistently
increased over the course of the evolution of life on the planet. Furthermore,
there is no evidence of any natural equivalent of the
complexity-increases-then-decreases trend uncovered during TRIZ research.
Complexity Characteristics In Technical Systems
The over-riding complexity trend uncovered during TRIZ research on technical
systems is the characteristic previously shown in Figure 1; in the first stages
of the evolution of a system, complexity increases, and then in the later stages
it decreases. This trend is one also observed by Edward De Bono (5). Neither De
Bono nor TRIZ has any clear guidelines to describe when over the course of an
s-curve the shift takes place from increasing to decreasing complexity -
although (3) suggests that the shift takes place at a point of maximum viable
complexity - beyond which the problems that come with the increased complexity outweigh the benefits to a critical mass of customers - Figure 3.
Figure 3: Correlations Between Maximum Complexity Point and
S-Curve (from (3))
In order to fully understand the trend we need to understand what we mean by
the term ‘complexity’. To a large extent, many of us make a correlation between
part-count and complexity - such that the greater the number of components a
system contains, the higher we say its complexity is. The distinction between
‘complexity’ and ‘part-count’ can in fact be both subtle and important.
To take a simple example, think of a simple bi-metallic strip and the ‘shape
change with temperature’ function it is designed to deliver; in early
bi-metallic strip designs, there were usually three or more components - the two
different metals plus some intermediary agent present to bond the two together.
Later in the evolutionary history we may see that it was possible to ‘trim’ the
intermediary from the system and to bond the two metals using friction-welding
or some other form of non-additive manufacture process. More recently still, of
course, we are now beginning to see the function of the bi-metallic strip
increasingly being delivered by a ‘single’ shape-memory alloy. Thus, in this
simple evolution path, it is quite clear that part count has been through the
‘decreasing’ part of the complexity trend. It is far less clear, however, that
the overall ‘complexity’ of the system has likewise decreased over the course of
these three steps. It seems far clearer that rather than ‘decreasing’ per se,
the complexity has instead been subsumed into the sub-system structure of the
constituent parts. In other words, although a shape-memory alloy replaces three
or two parts with one, it has largely done so through clever configuration of
the crystal structure of the alloy - and as such, the complexity has transferred
from the engineer to the materials scientist.
In other cases, the case for the validity of the TRIZ trend for
increasing-followed-by-decreasing complexity trend is more evident. Take for
example the often used example of the control of temperature within a
greenhouse. Evolution here can be seen to have passed from simple manually
controlled systems (person opens and closes windows) to more complex ‘automatic
control’ systems featuring temperature sensors connected to actuators that open
and close windows, turn heaters or coolers on and off, and so on. In these
evolutionary jumps, both complexity and part count has clearly increased.
Likewise, the removal of temperature sensors and actuators made possible by the
incorporation of bi-metallic (or SMA) window hinges represents a clear reduction
in both part-count and complexity.
Both examples are, of course, relatively trivial. Nevertheless, both serve to
suggest that there is either an inconsistency with the evolution of natural
systems, or we are missing some knowledge to help us understand how the two
might actually be the same thing.
Bridging the Gap Between Nature and Technology - Autopoiesis
In attempting to understand the apparent differences between the mechanisms
of natural and technical system evolution, it is necessary to appreciate the
concept of autopoiesis in natural systems. Autopoiesis, as described in (1) is
defined as a network pattern in which the function of each component is to
participate in the production or transformation of other components. In general
terms, an autopoietic network possesses three characteristics - it must be
self-bounded (system extension is determined by a boundary that is an
integral part of the network), self-generating (all components necessary
to survival are produced within the network), and self-perpetuating
(production processes continue over time).
All natural systems are autopoietic, and therefore any evolution must also
produce a system with autopoietic properties if it is to survive. In simple
terms, natural systems are able to successfully evolve to ever higher levels of
complexity by successfully managing the increase in complexity using the
resources existing within the system. The key word throughout is ‘SELF’. Any
successful natural system must possess all of the self- properties required for
autopoiesis. In other words, there is never a need for a system to become less
complex because at any evolutionary stage, the system is capable of managing
that complexity by itself.
The autopoiesis requirement of a natural system is closely analogous to the
ideality-driven evolution trend first described in (6) (and later reproduced in
(3)). Figure 4, taken from that paper illustrates how, when problem solvers
recognize the increasing-decreasing complexity trend and aim to by-pass the
traditional route, they are doing (if they succeed) precisely what natural
systems manage to do.
Figure 4: Traditional versus Ideality-Driven Evolution Paths
Describing this evolution goal and actually achieving are two potentially
very different things of course. As quoted in (5):
“Sometimes a system starts off simple and then becomes more complex and then becomes simple once again. This can be a normal process of evolution and adaptation to change. If the ‘complex’ phase is disallowed, then that system may be unable to evolve adapt.”
On the other hand, it is something that nature always achieves as systems
evolve; systems are as complex as they need to be and no more. Nature, in other
words, always seems to have an ideal final result directed trajectory (note: not
as any kind of conscious aim - there is no evidence to suggest that natural
evolution of any kind is ‘consciously’ doing anything, rather that all outcomes
are emergent behaviours).
If we bring together the ideal final result end goal plus the evolution
direction of natural systems towards ever-increasing complexity, it tends to
suggest that technical systems actually evolve in a manner as illustrated in
Figure 5.
Figure 5: Combined Natural plus Technical System Complexity
Evolution Trend
Whether we call it an ‘autopoietic optimum’ or an ‘IFR-driven’ evolution
trajectory, the point of the figure is the same;
- systems will evolve to become more complex over the course of
successive generations or s-curves
- within each s-curve, it is likely that our inability as
engineers and designers to do what nature manages to do, will cause system
complexity to increase more than it needs to do to achieve the ideal
optimum at the limits of an s-curve
- as our understanding of the current generation of a designed
system increases, we will then progressively reduce the complexity to that
level determined by the autopoietic optimum
- the more we allow ourselves to be guided by IFR thinking, and
the related ‘self-x’ function delivery thinking, the closer we will become
to achieving the same evolution trajectory that nature manages.
We will return to examples of this increasing complexity trend in technical
systems and to the vitally important subject of autopoiesis in future articles.
References
- Capra, F., ‘The Web of Life: A New Synthesis of Mind and Matter’,
HarperCollins, London, 1996.
- Altshuller, G., ‘Creativity As An Exact Science’, Gordon &
Breach, New York, 1984.
- Mann, D.L., ‘Hands-On Systematic Innovation’, CREAX Press, April
2002.
- Margulis, L., ‘Symbiosis In Cell Evolution’, 2nd Edition,
Freeman, San Francisco, 1993.
- DeBono, E., ‘Simplicity’, Viking, London, 1998.
- Mann, D.L., ‘Ideality and Self-X’, paper presented at TRIZ Future 2001,
Bath, November 2001.
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