This book, already 40 years old, is itself a collection of (lightly
edited) papers and presentations from the 1940s, 50s and 60s. So my
review is necessarily part historical. However, my main interest is in
the science. As part of my research in complexity science and
emergence, I'm going back and reading some of the precursor
literature. It is easy to assume that everything is new and just
discovered by the current generation, especially in such a rapidly
progressing areas as Computer Science (my students at least seem to
think that anything before the mid-1990s is prehistoric). But
everything has a history (I can boggle those same students by telling
them that object-orientation
is over 40 years old; that apparently rapid progress is
mostly a feature of hardware changes!), and complexity science
has a particularly rich one. Even before I read this collection, I was
aware that General System(s) Theory is a significant precursor; now I
realise how insightful Bertalanffy was. This is a fascinating mix of
the outdated, the now obvious, and the still prescient.
There are rather more quotes from the text than usual for a review,
because I want to capture the main points of the argument, and it
seems best to do so in the original author's own words.
The first component of Bertalanffy's General System Theory is the
system part. GST is a theory of systems, of wholes,
rather than a reductionist theory of components, of parts. It grew out
of thinking of biological organisms holistically:
The present author, in the early 20's
... advocated an organismic conception in biology which emphasizes
consideration of the organism as a whole or system, and sees the main
objective of biological sciences in the discovery of the principles of
organization at its various levels.
The point is that the reductionist view works only if the component
parts are weakly coupled, and can be linearly composed into wholes
without disrupting or affecting their individual behaviours. This is
just not true of non-linear systems where the parts are closely
coupled (the very properties that make something a "system",
rather than an aggregation), and a new approach is needed.
The system problem is essentially the
problem of the limitations of analytical procedures in science. ... "Analytical
procedure" means that an entity investigated be resolved into,
and hence can be constituted or reconstituted from, the parts put
Application of the analytical
procedure depends on two conditions. The first is that interactions
between "parts" be non-existent or weak enough to be
neglected ... The second condition is that the relations describing
the behavior of parts be linear ...
These conditions are not fulfilled
in the entities called systems, i.e., consisting of parts "in
Bertalanffy defines systems as [p38] "sets
of elements standing in interrelation". It is not just the
components, but their relationships, how they interact, that make up a
system. It is this extra feature that gives rise to "non-summative",
"emergent", or system-level properties not seen in the
In dealing with complexes of "elements,"
three different kinds of distinction may be made--i.e., 1. according
to their number; 2. according to their species; 3. according to the
relations of elements. ...
In cases 1 and 2, the complex may be understood
sum of elements considered in isolation. In case 3, not
only the elements should be known, but also the relations between
them. Characteristics of the first kind may be called summative, of
the second kind constitutive. We can also say that summative
characteristics of an element are those which are the same within and
outside the complex; they may therefore be obtained by means of
summation of characteristics and behavior of elements as known in
isolation. Constitutive characteristics are those which are dependent
on the specific relations within the complex; for understanding such
characteristics we therefore must know not only the parts, but also
The meaning of the somewhat
mystical expression, "the whole is more than the sum of parts"
is simply that constitutive characteristics are not explainable from
the characteristics of isolated parts. The characteristics of the
complex, therefore, compared to those of the elements, appear as "new"
or "emergent." If, however, we know the total of parts
contained in a system and the relations between them, the behavior of
the system may be derived from the behavior of the parts. We can also
say: While we can conceive of a sum as being composed gradually, a
system as total of parts with its interrelations has to be conceived
of as being composed instantly.
may define summativity by saying that a complex can be built up, step
by step, by putting together the first separate elements; conversely,
the characteristics of the complex can be analyzed completely into
those of the separate elements. This is true for those complexes which
we may call "heaps," ...
emphasize the non-summative character of physical and biological
systems because the methodological attitude has been, and is yet to a
large extent, determined by the mechanistic program
states, according to Russell, that the laws
governing the behavior of the parts can be stated only by considering
the place of the parts in the whole. Russell rejects this view.
It is true that the principles of summativity are applicable to the
living organism to a certain extent. ... This applies to those
phenomena we shall define later as occurring in highly "mechanized"
partial systems. But [it]
is profoundly untrue with respect
exactly to the basic and primary biological phenomena.
behavior of an element is different within the system from what it is
in isolation. You cannot sum up the behavior of the whole from the
isolated parts, and you have to take into account the relations
between the various subordinated systems and the systems which are
super-ordinated to them in order to understand the behavior of the
parts. Analysis and artificial isolation are useful, but in no way
sufficient, methods of biological experimentation and theory.
The second component of GST is the general part.
This idea here is that systems have general properties that can be
used to analyse whole classes of systems, in addition to their
specific properties that apply only to particular instances of
systems. Which are which?
Which principles are common to the
several levels of organization and so may legitimately be transferred
from one level to another, and which are specific so that transfer
leads to dangerous fallacies?
It is important to note that principles can be general, but
not to take the analogies too far, to imply some kind of identity of
the systems that exhibit common principles.
The fact that simple growth laws apply
to social entities such as manufacturing companies, to urbanization,
division of labor, etc., proves that in these respects the "organismic
analogy" is correct. In spite of the historians' protests, the
application of theoretical models, in particular, the model of
dynamic, open and adaptive systems
to the historical process
certainly makes sense. This does not imply "biologism,"
i.e., reduction of social to biological concepts, but indicates system
principles applying in both fields.
Putting these ideas together gives GST: a non-reductionist theory of
systems, of the organisation and interrelations of the parts, with
general rules and laws governing the organisational principles.
We come, then, to a conception which in
contrast to reductionism, we may call perspectivism. We cannot reduce
the biological, behavioral, and social levels to the lowest level,
that of the constructs and laws of physics. We can, however, find
constructs and possibly laws within the individual levels. ... The
unifying principle is that we find organization at all levels.
Not only is GST non-reductionist in that it considers relationships
between parts, it is non-static in that it considers the dynamics
of those parts and relationships. Systems are not in equilibrium; they
Concepts and models of equilibrium,
homeostasis, adjustment, etc., are suitable for the maintenance of
systems, but inadequate for phenomena of change, differentiation,
evolution, negentropy, production of improbable states, creativity,
building-up of tensions, self-realization, emergence, etc.; as indeed
Cannon realized when he acknowledged, beside homeostasis, a "heterostasis"
including phenomena of the latter nature.
So behaviour is important. And because we are talking about systems,
we cannot understand the behaviour of the parts in isolation. Their
behaviour is fundamentally influenced and controlled by the context:
where in the system organisation they are.
It is necessary to study not only parts
and processes in isolation, but also to solve the decisive problems
found in the organization and order unifying them, resulting from
dynamic interaction of parts, and making the behavior of parts
different when studied in isolation or within the whole.
This is the main contrast with classical physics, that epitome of
the reductionist sciences. The underlying world view of classical
physics is not appropriate for non-linear, highly organised, complex
systems, and so a new approach is needed. Today we talk of complexity
science; Bertalanffy called it General System Theory.
Classical physics ... was highly
successful in developing the theory of unorganized complexity. ... The
theory of unorganized complexity is ultimately rooted in the laws of
chance and probability and in the second law of thermodynamics. In
contrast, the fundamental problem today is that of organized
complexity. Concepts like those of organization, wholeness,
directiveness, teleology, and differentiation are alien to
conventional physics. However, they pop up everywhere in the
biological, behavioral and social sciences ...
... The classical modes of thinking ... fail in the case of
interaction of a large but limited number of elements or processes.
Today we might speak of mesoscopic scale: too many elements
to treat all individually and exactly, but too few to treat en
mass and statistically. However, there is a difference: note
Bertalanffy's constant emphasis on the interactions
(relationships between elements), not just the elements themselves.
Even macroscopic systems can be complex, if they have structured
organisation, not mere average properties.
Despite this move away from classical physics, most of the
formalisms given in the book are in terms of Ordinary Differential
Equations, and how consideration of these can lead to general results.
Mathematically trivial as these examples
are, they illustrate
that certain laws of nature can be arrived
in a purely formal way. The equations discussed signify no
more than that the rather general system of equation
development into a Taylor series and suitable conditions have been
applied. In this sense such laws are "a priori," independent
from their physical, chemical, biological, sociological, etc.,
interpretation. In other words, this shows the existence of a general
system theory which deals with formal characteristics of systems,
concrete facts appearing as their special applications by defining
variables and parameters.
Bertalanffy does allow that ODEs are merely one formalism, and that
it might not be adequate for certain kinds of system, particularly
ones exhibiting hysteresis (a dependence on history, not just on
[an ODE] definition
of "system" is ... by no means general. It abstracts from
spatial and temporal conditions, which would be expressed by partial
differential equations. It also abstracts from a possible dependence
of happenings on the previous history of the system ("hysteresis"
in a broad sense); consideration of this would make the system into
integro-differential equations ... Introduction of such equations
would have a definite meaning: The system under consideration would be
not only a spatial but also a temporal whole.
He goes further, and says that there are other (non-DE) areas of
mathematics applicable to GST including, cybernetics, game theory,
decision theory, and topology (network and graph theory). At the time
(and indeed, still today) these other areas are not as exploited as
they might be:
information theory has been hailed as a "major
breakthrough," but outside the original technological field,
contributions have remained scarce.
in biology, DNA is spoken
of as "coded information" and of "breaking the code"
when the structure of nucleic acids is elucidated, use of the term
information is a façon de parler rather than
application of information theory in the technical sense
theory, although useful for computer design and network analysis, has
so far not found a significant place in biology" (Bell, 1962).
E., "Oogenesis," C. P. Raven, review, Science, 135
Other definitions of systems include consideration of
UC - set of all couplings between the
elements and the elements and environment; ST - set of all states and
all transitions between states
Now, thinking computationally, we are very used to "ST",
but still have less of a handle on the computational view of "UC".
We need a way to capture the dynamics of the relationships and
couplings. This is more than just class diagrams with associations: we
also need a view of coupling strength, and more importantly, a high
level view of the coupling dynamics: changes in relationships and
coupling strengths. We need to move the focus from the nodes to the
(dynamics of the) edges of the graphs. Indeed, part of the problem is
how to model novelty: not just new nodes and links, but new kinds of
nodes and links. This would require a system of ODEs that is
dynamically changing, with new equations being produced by the system
There are many things that haven't changed in the last half-century,
in fact. I was actually in the air when reading the following, which
caused a wry grin:
Anybody crossing continents by jet with
incredible speed and having to spend endless hours waiting, queuing,
being herded in airports, can easily realize that the physical
techniques in air travel are at their best, while "organizational"
techniques still are on a most primitive level.
There is a third component to GST: it is a study of open
systems, interacting with their environment. The emphasis is
on taking in ("eating") energy and information, rather than
on what is given back to the environment ("excreting"), but
both are necessary in an open system.
Conventional physics deals only with
closed systems, i.e., systems which are considered to be isolated from
their environment. ... Thermodynamics expressly declares that its laws
apply only to closed systems. In particular, the second principle of
thermodynamics states that, in a closed system
process comes to a stop at a state of equilibrium.
towards maximum entropy
is the tendency to maximum disorder.
However, we find systems which by
their very nature and definition are not closed systems. Every living
organism is essentially an open system. It maintains itself in a
continuous inflow and outflow, a building up and breaking down of
components, never being, so long as it is alive, in a state of
chemical and thermodynamic equilibrium but maintained in a so-called
steady state which is distinct from the latter. This is the very
essence of that fundamental phenomenon of life which is called
metabolism, the chemical processes within living cells. What now?
Obviously, the conventional formulations of physics are, in principle,
inapplicable to the living organism qua open system and steady
state, and we may well suspect that many characteristics of living
systems which are paradoxical in view of the laws of physics are a
consequence of this fact.
Today we might consider that dynamical systems theory has
taken over the mantle of much of GST, with its trajectories,
attractors, non-linearity, and general results. Some early ideas can
be seen in Bertalanffy's writings:
trajectories may converge towards a
stable node represented by the equilibrium point, may approach it as
stable focus in damped oscillations, or cycle around it in undamped
oscillations (stable solutions); or else diverge from an unstable
node, travel away from an unstable focus in oscillations, or from a
saddle point (unstable solutions).
Unsurprisingly, there is no mention of strange attractors, since
this term was not coined until later. But other dynamical systems
concepts are covered (albeit in passing):
A central notion of dynamical theory is
that of stability, i.e. the response of a system to perturbation.
stability arguments without actual solution of the differential
equations (direct method) and for non-linear systems are possible by
introduction of so-called Liapunov functions which are
essentially generalized energy functions, the sign of which indicates
whether or not an equilibrium is asymptotically stable
But today's dynamical systems theory is still mainly a study of closed
systems (in that they have no environmental inputs, although they are
often dissipative, and hence essentially losing energy or
information). Bertalanffy points out that closed systems have two very
different properties from open systems, and that intuitions built up
about what is possible in closed systems can lead us very astray when
considering open (for example, biological) systems, leading to all
kinds of apparently paradoxical results.
The first of these differences concerns final states. Open systems
exhibit what Bertalanffy calls "equifinality": they can end
in the same place despite different starting points.
In any closed system, the final state is
unequivocally determined by the initial conditions... If either the
initial conditions or the process is altered, the final state will
also be changed. This is not so in open systems. Here, the same final
state may be reached from different initial conditions and in
different ways. This is what is called equifinality ... It can be
that open systems, insofar as they attain a steady state,
must show equifinality
(Today, we might talk of "attractors" in dissipative
systems.) In fact, the use of final state values in formulae is often
forbidden in biology, yet is nothing at all strange or "vitalistic".
The "teleological" final-value
formula therefore is only a transformation of the differential
equation indicating actual conditions.
physics makes ample use
of such final-value formulas because the fact is mathematically clear
and nobody attributes an anthropomorphic "foresight" of the
goal to a physical system. Biologists, on the other hand, often
regarded such formulas as somewhat uncanny, either fearing a hidden
vitalism, or else considering such teleology or goal-directedness as "proof"
for vitalism. For with respect to animate rather than to inanimate
nature, we tend to compare finalistic processes with human foresight
of the goal; while, in fact, we are dealing with obvious, even
mathematically trivial relations.
The second difference between open and closed systems deals with
that old chestnut of the Second Law of Thermodynamics and the increase
In open systems ... we have not only
production of entropy due to irreversible processes, but also import
of entropy which may well be negative. This is the case in the living
organism which imports complex molecules high in free energy. Thus,
living systems, maintaining themselves in a steady state, can avoid
the increase of entropy, and may even develop towards states of
increased order and organization.
Bertalanffy notes that some of the relationships in a system can be
competitive (eg, sheep and rabbits competing for grass in a
Lotka-Volterra system), which can result in extinction of parts of the
system. Paradoxically, a predator-prey interaction (eg, foxes and
rabbits) does not so readily lead to extinction.
in Volterra's equations ... competition
eventually leads to the extermination of the species with the smaller
growth capacity; a predator-prey relation only leads to periodic
oscillation of the numbers of the species concerned around a mean
value. These relations have been stated for
systems, but it may well be that they have also sociological
He points out that these ideas of parts of the system competing with
other of its parts seems counter-intuitive, but are in fact essential.
If we are speaking of "systems,"
we mean "wholes" or "unities." Then it seems
paradoxical that, with respect to a whole, the concept of competition
between its parts is introduced. In fact, however, these apparently
contradictory statements both belong to the essentials of systems.
Every whole is based upon the competition of its elements
Putting together these ideas of interrelationships, organisation,
competition, and dynamics, inspired by developmental biology,
leads Bertalanffy to the concept of progressive mechanisation.
"Progress" requires differentiation and specialisation. This
is achieved by a weakening of interactions, which leads to
differentiation and segregation into to a hierarchical structure,
which develops into more weakly coupled (less system-like) components.
However, this has a corresponding downside: a loss of flexibility.
interactions between the elements decrease with time ... the system
passes from a state of wholeness to a state of independence of the
the organization of
biological wholes is built up by differentiation of an original whole
which segregates into parts. ...
segregation into subordinate
partial systems implies an increase of complexity in the system. Such
transition towards higher order presupposes a supply of energy, and
energy is delivered continuously into the system only if the latter is
an open system, taking energy from its environment.
... Increasing mechanization means
increasing determination of elements to functions only dependent on
themselves, and consequent loss of regulability which rests in the
system as a whole, owing to the interrelations present. The smaller
the interaction coefficients become,
the more "machine-like"
is the system--i.e., like a sum of independent parts.
This ... "progressive
mechanization," plays an important role in biology. ...
transition from behavior as a whole to summative behavior takes place.
... Mechanization, however, is never complete in the biological realm;
even though the organism is partly mechanized, it still remains a
In this contrast between wholeness and
sum lies the tragical tension in any biological, psychological and
sociological evolution. Progress is possible only by passing from a
state of undifferentiated wholeness to differentiation of parts. This
implies, however, that the parts become fixed with respect to a
certain action. Therefore progressive segregation also means
progressive mechanization. Progressive mechanization, however, implies
loss of regulability. As long as a system is a unitary whole, a
disturbance will be followed by the attainment of a new stationary
state, due to the interactions within the system. The system is
self-regulating. If, however, the system is split up into independent
causal chains, regulability disappears. The partial processes will go
on irrespective of each other. ...
Progress is possible only by
subdivision of an initially unitary action into actions of specialized
parts. This, however, means at the same time impoverishment, loss of
performances still possible in the undetermined state. The more parts
are specialized in a certain way, the more they are irreplaceable, and
loss of parts may lead to the breakdown of the total system. To speak
Aristotelian language, every evolution, by unfolding some
potentiality, nips in the bud many other possibilities.
This to me is possibly one of the key parts of the theory. It seems
to be an aspect that is not covered in anything like this manner by
today's complexity science, which tends to think of itself as
antithetical to a hierarchical organisational structure, and tends not
to look that deeply into the developmental aspects of systems. GST
isn't just a theory of systems, but of systems of systems:
Systems are frequently structured in a
way so that their individual members again are systems of the next
Such superposition of systems is
called hierarchical order. For its individual levels, again
the aspects of wholeness and summativity, progressive mechanization,
centralization, finality, etc., apply. Such hierarchical structure and
combination into systems of ever higher order, is characteristic of
reality as a whole and of fundamental importance especially in
biology, psychology and sociology.
This system of systems maintains itself by a dynamic process, with
different timescales at different levels.
The living organism is a hierarchical
order of open systems. What imposes as an enduring structure at a
certain level, in fact, is maintained by continuous exchange of
components of the next lower level. Thus, the multicellular organism
maintains itself in and by the exchange of cells, the cell in the
exchange of cell structures, these in the exchange of composing
chemical compounds, etc. As a general rule, turnover rates are the
faster the smaller the components envisaged
Timescales are important to keep an open system in its steady but
dynamic state out of (thermodynamic) equilibrium.
For the maintenance of "dynamic
equilibrium," it is necessary that the rates of processes be
exactly harmonized. Only in this way is it possible that certain
components can be broken down, so liberating usable energy while, on
the other hand, import prevents the system from attaining equilibrium.
Fast reactions, also in the organism, lead to chemical equilibrium
slow reactions do not reach equilibrium but are kept in a steady
state. Therefore, the condition for the existence of a chemical system
in a steady state is a certain slowness of reactions. Momentary
reactions, like those between ions, lead to equilibrium in "infinitely
short" time. The maintenance of a steady state in the organism is
due to the fact that it is composed of complex carbon compounds; these
are, on the one hand, rich in energy but chemically inert, so that the
maintenance of considerable chemical potential is possible; on the
other hand, rapid and regulated release of this amount of energy is
performed by enzyme actions, so that a steady state is maintained.
Bertalanffy idea's of progressive mechanisation leads on to some
interesting ideas about the emergence of and nature of individuality,
and hence identity, and why some systems (like that of the
foxes and rabbits) are not individuals (no progressive
From the biological viewpoint, we would
emphasize progressive mechanization and centralization. The primitive
state is that where the behavior of the system results from the
interactions of equipotential parts; progressively, subordination
under dominant parts takes place. ....
Thus, similar to progressive
mechanization a principle of progressive centralization is found in
biology. ... This viewpoint casts light on an important, but not
easily definable concept, that of the individual.
biological individuality does not exist, but only progressive
individualization in evolution and development resulting from
progressive centralization, certain parts gaining a dominant role and
so determining behavior of the whole. Hence the principle of
progressive centralization also constitutes progressive
individualization. An individual is to be defined as a centered
system, this actually being a limiting case approached in development
and evolution so that the organism becomes more unified and "indivisible"
This structuring principle also solves some of the "pseudoproblems"
of how systems work with relationships that are neither completely
one-to-one, nor all-to-all: there is intermediate structure that
allows many-to-many linkages whilst at the same time having some more
important or central than others.
Neglect of the principle of progressive
mechanization and centralization has frequently led to pseudoproblems,
because only the limiting cases of independent and summative elements,
or else complete interaction of equivalent elements were recognized,
not the biologically important intermediates. ... every gene influences not one
single trait but many, and possibly the total organism (polygeny of
characteristics and polypheny of genes). ... the genome as a whole
produces the organism as a whole, certain genes, however, preeminently
determining the direction of development of certain characters ... ...
in the function of the nervous system there was apparently the
alternative of considering it either as a sum of mechanisms for the
individual functions, or else as a homogeneous nervous net. Here, too,
the correct conception is that any function ultimately results from
interaction of all parts, but that certain parts of the central
nervous system influence it decisively and therefore can be denoted as
"centers" for that function.
Now, this talk of "wholeness", and of "centres"
reminds me of some of Alexander's
work, and I wonder if there has been an influence. However, I suspect
that this hierarchical idea might not find favour with Alexander; in
his essay "A City is not a Tree" he points out that certain
systems, like cities, have many more cross-linkages at all levels, and
rather have a lattice structure. I think it would be
worthwhile to delve into this structural aspect somewhat further.
One other main precursor of complexity science is cybernetics,
invented around the same time. Bertalanffy has a lot to say about
cybernetics, pointing out the differences between it and GST. One
characteristic difference between cybernetics and GST is from the
difference between closed and open systems. Bertalanffy describes
cybernetics to be of secondary importance.
the feedback scheme
structural arrangements of the type mentioned. There are, however,
many regulations in the living organism which are of essentially
different nature, namely, those where the order is effectuated by a
dynamic interplay of processes.
. It can be shown that the
primary regulations in organic systems
are of the nature of
dynamic interaction. They are based upon the fact that the living
organism is an open system, maintaining itself in, or approaching a
steady state. Superposed are those regulations which we may call
secondary, and which are controlled by fixed arrangements, especially
of the feedback type. This state of affairs is a consequence of a
general principle of organization which may be called progressive
mechanization. At first, systems---biological, neurological,
psychological or social---are governed by dynamic interaction of their
components; later on, fixed arrangements and conditions of constraint
are established which render the system and its parts more efficient,
but also gradually diminish and eventually abolish its
equipotentiality. Thus, dynamics is the broader aspect, since we can
always arrive from general system laws to machinelike function by
introducing suitable conditions of constraint, but the opposite is not
He classes cybernetic systems as a special class (subset) of all
feedback systems and "homeostatic"
control are a significant but special class of self-regulating systems
and phenomena of adaptation
In other places he lists the main differences between open systems
and cybernetic feedback, where the two approaches seem here to be more
on the same level, just capturing different aspects of systems: mainly
thermodynamics versus information.
The basis of the open-system model is
the dynamic interaction of its components. The basis of the cybernetic
model is the feed-back cycle
in which, by way of feedback of
information, a desired value
is maintained, a target is
reached, etc. The theory of open systems is a generalized kinetics and
thermodynamics. Cybernetic theory is based on feedback and
information. Both models have, in respective fields, been successfully
applied. However, one has to be aware of their differences and
The open-system model in kinetic
and thermodynamic formulation does not talk about information.
a feedback system is closed thermodynamically and kinetically; it has
In an open system increase of order
and decrease of entropy is thermodynamically possible.
closed feedback mechanism information can only decrease, never
An open system may "actively"
tend toward a state of higher organization, i.e., it may pass from a
lower to a higher state of order owing to conditions in the system. A
feedback mechanism can "reactively" reach a state of higher
organization owing to "learning," i.e., information fed into
In summary, the feedback model is
preeminently applicable to "secondary" regulations, i.e.,
regulations based on structural arrangements in the wide sense of the
word. Since, however, the structures of the organism are maintained in
metabolism and exchange of components, "primary" regulations
must evolve from the dynamics in an open system. Increasingly, the
organism becomes "mechanized" in the course of development;
hence later regulations particularly correspond to feedback mechanisms
(homeostasis, goal-directed behavior, etc.).
In fact, he explicitly compares open systems and cybernetic systems
as two sides (process and structure) of the same systems coin, and
calls for a synthesis. This synthesis is still to occur.
Typical feedback or homeostatic
phenomena are "open" with respect to incoming information,
but "closed" with respect to matter and energy. The concepts
of information theory
correspond therefore to "closed"
open systems. .... The
cybernetic scheme permits
clarification of many important
phenomena of self-regulation in physiology and lends itself to
information-theoretical analysis. The open-system scheme permits
kinetic and thermodynamic analysis.
dynamics in open systems and
feedback mechanisms are two different model concepts, each in its
right in its proper sphere. The open-system model is basically
nonmechanistic, and transcends not only conventional thermodynamics,
but also one-way causality as is basic in conventional physical theory
... The cybernetic approach retains the Cartesian machine model of the
organism, unidirectional causality and closed systems; its novelty
lies in the introduction of concepts transcending conventional
physics, especially those of information theory. Ultimately, the pair
is a modern expression of the ancient antithesis of "process"
and "structure"; it will eventually have to be resolved
dialectically in some new synthesis.
Towards the end, Bertalanffy attempts to show that GST is more
widely applicable than just to biology, and applies it to history, and
finally, to psychology. Here he is indulging in a justified rant
against overly mechanised and behaviourist views of psychology.
Viewing the brain not as a passive reactive system, but as a dynamic,
self-maintaining system, leads to a very different view.
cause a process in an otherwise inert system; it only modifies
processes in an autonomously active system" (Bertalanffy, 1937,
pp. 133 ff ...)
LUDWIG von, Das Gefüge des Lebens, Leipzig, Teubner,
This view has interesting parallels with theories of immunology that
view the immune system as an active dynamic maintenance system that is
perturbed by pathogen input, rather than as a passive "guardian"
springing into action only when attacked. He applies a systems theory
approach to discussing schizophrenic breakdown. However, there is no
indication that the problematic symbolic breakdown might be due to
physical/chemical problems in the brain material, rather than at a
higher symbolic level, so there is no discussion of whether the
problem could therefore be treated by drugs rather than psychotherapy.
He has an interesting take on the "free will" problem
being a category error pseudoproblem.
Within the framework developed, the
problem of free will or determinism also receives a
new and definite meaning. It is a pseudo-problem, resulting from
confusion of different levels of experience and of epistemology and
metaphysics. We experience ourselves as free, for the simple
reason that the category of causality is not applied in direct or
immediate experience. Causality is a category applied to bring order
into objectivated experience reproduced in symbols. Within the latter,
we try to explain mental and behavioral phenomena as causally
determined and can do so with increasing approximation by taking into
account ever more factors of motivation, by refining conceptual
causality is not metaphysical necessity, but is
one instrument to bring order into experience, and there are other "perspectives"
, of equal or superior standing.
He finally moves on to what might be called cultural relativism:
that the symbolic system built up in the brain might be a product of
culture as much as anything.
it seems amply demonstrated that the
style of thinking is different in the several civilizations even
though Whorf's supposition that this is more or less solely due to
linguistic factors, is open to criticism.
There is a rather eyebrow-raising 1960s feel about sentences like
the experience of the child, savage, and
non-Westerner, though primitive, nevertheless forms an organized
although only a little later he does say
There is no intrinsic justification to
consider as "true" representation of the world what we take
to be "normal" experience (i.e., the experience of the
average adult European of the twentieth century) , and to consider all
other sorts of experience that are equally vivid, as merely abnormal,
fantastic or, at best, a primitive precursor to our "scientific"
However, he is not subscribing to what might be called "strong
relativism". There is a factual basis to reality, but our culture
might affect which aspects of it we choose to study.
This, of course, does not mean that the
content of mathematics is "true" only within a certain
culture. It is a tautological system of hypothetico-deductive nature,
and hence any rational being accepting the premises must agree to all
its deductions. But which aspects or perspectives are mathematized
depends on the cultural context.
And even that choice isn't totally arbitrary. We are physical beings
inhabiting a material world, and we have to get it "right"
to at least some degree merely in order to survive.
As far as direct experience is
concerned, the categories of perception as determined by the
biophysiological organization of the species concerned cannot be
completely "wrong," fortuitous and arbitrary. Rather they
must, in a certain way and to a certain extent, correspond to "reality"---whatever
this means in a meta-physical sense. Any organism, man included, is
not a mere spectator, looking at the world scene and hence free to
adopt spectacles, however distorting, .... Rather he is a reactor and
actor in the drama. The organism has to react to stimuli coming from
outside .... There is a latitude in what is picked up as a stimulus
.... However, its perception must allow the animal to find its way in
the world. This would be impossible if the categories of experience,
such as space, time, substance, causality, were entirely deceptive.
The categories of experience have arisen in biological evolution, and
have continually to justify themselves in the struggle for existence.
If they would not, in some way, correspond to reality, appropriate
reaction would be impossible, and such organism would quickly be
eliminated by selection.
So, this is a fascinating account of General Systems Theory as it
stood 40-odd years ago. There are deep insights, and lots of food for
thought. So, what happened? Why is it not now mainstream? And if it
didn't succeed, why might its descendent, complexity science, fare
I can only speculate. But it may be that GST was ahead of its time.
Reductionism hadn't been mined out: there was still an awful lot of
progress to be made, particularly in biology, the "old"
reductionist way: genes to sequence, data to mine. Now we have a huge
heap of components, it is being made clear much more forcefully that
this is insufficient: all the King's horses and all the King's men are
having a devil of a time putting Humpty together again. And maybe the
mathematics wasn't up to it. We still don't really have a unified
theory of material information processing, of open systems that
process both matter and information.
Why might we do better now? If we are to do better, part of the
answer has to be: "computers". All that complexity just
can't be handled analytically. And all the emphasis is on behaviour,
on dynamics, and computers are great at exploring
dynamics. Maybe this time around, we can make more progress. But to do
so effectively, we must remember our history, what came before, so
that we don't waste effort reinventing the wheel. We need to stand on
the shoulders of giants, and Bertalanffy seems to have a good set of
shoulders on him.