Systems Theory & the Gaia Hypothesis
Since the Renaissance of western culture, the emerging discipline of science has gotten very good at
dissecting and analyzing the pieces of our world. It has done this through a process known as
‘reductionism’, which basically means that we can understand complicated things (like a plant, or a human)
by breaking the things down to the smallest parts and attempting to describe those parts. This works well,
for example, with a newspaper photo: it appears, at a distance, that you are looking at a real person’s face,
for instance, but as you get closer, you see that the image is made up of many little dots of differing shades
of gray. If you can describe exactly what shade of gray is in each point of the image, you can exactly describe
any image.
This reasoning begins to quickly break down when dealing with anything more complex that we
would find in the natural world: we broke wood down into molecules, those molecules into atoms, the
atoms into electrons and nuclei, and further into various quarks. It appears, now, that even quarks are made
up of smaller things. I speculate that this process of ‘dissection’ could go on forever – and ultimately tell us
very little about what is going on. It is as if we were small children, taking apart a radio. Now we are staring
at all the disconnected pieces and have no idea how to put it all back together into a functioning whole.
In recent years, science itself has begun to see the shortcomings of reductionist thought. If we
cannot understand how nature works by examining its individual pieces, then perhaps we can get closer to
the mark by examining the relationships between different elements.
To this end, systems theory was born.
The basic concept of a system (also called a network) is fairly
simple: it consists of a set of nodes, with links connecting them. For
our purposes, the links quickly become more important because they
are the ones that truly describe what the nature of the system is: in the
example to the right, the arrows represent the flow between nodes,
and you can quickly realize that certain nodes are ‘beginnings’ and
certain others are ‘ends’ based solely on the direction of the links. In
addition, we can see that some nodes are more linked than others; but
again, it is because of the links, not the nodes, that this characteristic
exists.
There are innumerable examples of living systems all around
us. Perhaps the most familiar is the ‘food web’, an intricate set of relationships between plants, animals,
bacteria and nutrients in an ecosystem. These systems are a bit more complex than the example above,
because the incorporate the next important characteristic of systems: feedback.
There are two types of feedback: the first, positive feedback, occurs when an increase in the first node
of the system increases the node linked to it, and this second node in turn increases the first. For instance,
the more you exercise, the more toned your muscles will become. Toned muscles, in turn, will allow you
to exercise more. Another example is commonly referred to as the ‘vicious circle’: the more you worry
about your health, the higher your blood pressure will become. The higher the blood pressure, the more
you worry. And so on.
The second type of feedback is called negative feedback, and occurs
when an increase in the first node increases the second, but an increase in
the second actually decreases the first. For example, if you increase the
amount of food available to a herd of buffalo, the population will increase.
But as the herd size increases and grazes on the extra fodder, the amount of
food will actually diminish. This has the additional effect of coming back around to eventually put a check
on the herd’s expansion.
An interesting feature of systems with negative feedback is that they are self-regulating, meaning
they usually gravitate towards a state of balance. They cannot escalate wildly in one direction or the other,
because the negative feedback will always provide the counterbalancing force, in either direction. This was
an important realization in the 1940s and 50s, and allowed science to explain concepts such as homeostasis in
the body (our body’s ability to maintain a stable temperature, electrolyte balance, etc…), food webs, and
sustainable ecology (to name but a few). The idea of self-regulation also became one of the first glimmers in
modern science of an ‘intelligence’ in nature, a concept we will return to often.
In the 1970s James Lovelock and Lynn Margulis elaborated on the ideas of feedback within complex
systems and self-regulation to present a bold ‘new’ view of our planet, a view that they termed the Gaia
hypothesis. In Lovelock’s own words,
“Consider Gaia theory as an alternative to the conventional wisdom that sees Earth as a dead planet
made of inanimate rocks, ocean, and atmosphere, and merely inhabited by life. Consider it as a real
system, comprising all of life and all of its environment tightly coupled so as to form a self-regulating
entity.”
In essence, the entire planet could be seen as a living, breathing system that maintained its own homeostasis
much like our own bodies do. This was a difficult pill for many to swallow, because the prevailing dogma
was still that of ‘humans as masters of creation’. To see ourselves as just a living piece of a much, much
larger living system was somehow demeaning to the individuals who had spent their lives in pursuit of the
smallest pieces of nature. Fortunately, the Gaia hypothesis has now become much more accepted as we
move into concepts such as deep ecology and holism.
The two key points to remember from the Gaia hypothesis are:
 relationship. This is the ‘tight coupling’ Lovelock refers to above, and emphasizes again that the
links between the pieces are the most important part to making the whole work.
 self-regulation. Negative feedback loops allow all life that is interconnected by relationship to
maintain balance. This balance is essential to sustaining life itself, and is a property of the system that cannot
be predicted from looking at its components, only from looking at the relationships within the system.