Principles of Systems and Cybernetics:
an evolutionary perspective
Francis HEYLIGHEN*
PO, Free University of Brussels, Pleinlaan 2,
B-1050 Brussels, Belgium
fheyligh@vnet3.vub.ac.be

ABSTRACT: A set of fundamental principles for the
cybernetics domain is sketched, based on the
spontaneous emergence of systems through variation andselection. The (mostly self-evident) principles are:selective retention, autocatalytic growth, asymmetrictransitions, blind variation, recursive systems
construction, selective variety, requisite knowledge andincomplete knowledge. Existing systems principles, suchas self-organization, “the whole is more than the sum of
its parts”, and order from noise can be reduced to
implications of these more primitive laws. Others, suchas the law of requisite variety, the 2nd law ofthermodynamics, and the law of maximum entropy
production are clarified, or restricted in their scope.

1

Introduction

Principles or laws play the role of expressing the mostbasic ideas in a science, establishing a framework or
methodology for problem solving. The domain ofGeneral Systems and Cybernetics is in particular need ofsuch principles, since it purports to guide thought ingeneral, not just in a specific discipline. Unfortunately,
the few generally used principles of the domain, such as
the law of requisite variety, or the principle that thewhole is more than the sum of its parts, are typicallyambiguous or controversial, and lack coherence witheach other.
The present work purports to start a general examinationof principles of cybernetics and systems, within theframework of the Principia Cybernetica Project(Heylighen, Joslyn & Turchin, 1991; Turchin, 1991).
The Principia Cybernetica philosophy is evolutionary:systems and their cybernetical organization are
constructed through the self-organizing process of blind
variation and natural selection. This process function as askeleton interconnecting all principles.
The study will on the one hand critically assess existing
principles, clarifying their meaning, on the other hand try
to formulate new principles which may generalize orinterconnect known laws. The ultimate goal is to arrive
at a network of concepts and principles similar to aformal system, with “axioms” implicitly defining
primitive concepts, definitions of higher order concepts,
and “theorems”, derived from the more primitive axioms

and definitions. The fundamental principles, like all goodaxioms, are supposed to be self-evident, if not tautologous.
Their implications, like most theorems, on the other hand,
may be far from trivial, and sometimes even counterintuitive.
This paper will propose a first, necessarily limited andsketchy, overview of the principles that I think are mostbasic, starting from the most primitive ones, and building up
towards less obvious ones. This overview is offered for
discussion and elaboration by other systems researchers. A
more in-depth treatment of this issue is being prepared in theform of a series of journal papers (Heylighen, forthcoming).

2

The Principle of Selective Retention

Stable configurations are retained, unstable ones are
eliminated.
This first principle is tautological in the sense that stability
can be defined as that what does not (easily) change ordisappear. Instability then is, by negation, that what tends tovanish or to be replaced by some other configuration, stable
or unstable. The word “configuration” denotes any
phenomenon that can be distinguished. It includes
everything that is called feature, property, state, pattern,
structure or system.
The principle can be interpreted as stating a basic distinction
between stable configurations and configurations undergoingvariation. This distinction has a role in evolution which is asfundamental as that between A and not A in logic. Without
negation, we cannot have a system of logic. Without(in)stability we cannot describe evolution. The tautology
plays a role similar to the principle of contradiction: “A andnot A cannot both be true”. The distinction between stable
and changing is not as absolute as that between A and not A,
though. We do not require a principle of the excluded
middle, since it is clear that most configurations are neither
absolutely stable nor absolutely unstable, but more or lessstable. In this more general formulation, the principle would
read:
More stable configurations are less easily eliminated thanless stable ones

3

The Principle of Autocatalytic
GrowthStable configurations that facilitate the appearance ofconfigurations similar to themselves will become more
numerous
This self-evident principle is the companion of theprinciple of selective retention. Whereas the latter
expresses the conservative aspect of evolution,
maintenance or survival, the former expresses the
progressive
aspect,growthanddevelopment.
Autocatalytic growth describes as well biologicalreproduction, as the positive feedback or non-linearity
characterizing most inorganic processes of selforganization, such as crystal growth. The principle
simply states that it suffices for a configuration to bestable, and in some respect autocatalytic or selfreplicating, in order to undergo a potentially explosive
growth.
Such configurations, in biology, are said to have a highfitness and that gives them a selective advantage overconfigurations with a lower fitness. The fact that growthrequires (finite) resources implies that growth must
eventually stop, and that two configurations using thesame resources will come in competition for theseresources. Normally the fitter configuration will
outcompete the less fit one, so that no resources are left
for the latter (survival of the fittest). Such ageneralization of the principle of selective retention may
be called the principle of natural selection.

4

The Principle of AsymmetricTransitions: entropy and energy

A transition from an unstable configuration to a stable
one is possible, but the converse is not.
This principle implies a fundamental asymmetry inevolution: one direction of change (from unstable tostable) is more likely than the opposite direction. The
generalized, “continuous” version of the principle is thefollowing:
The probability of transition from a less stableconfiguration A to a more stable one B is larger than theprobability for the inverse transition: P (A -> B) > P (B > A) (under the condition P (A -> B) =/ 0)
A similar principle was proposed by Ashby in his
Principles of the Self-Organizing System (1962):”We
start with the fact that systems in general go toequilibrium. Now most of a system’s states are nonequilibrial [...] So in going from any state to one of theequilibria, the system is going from a larger number ofstates to a smaller. In this way, it is performing a
selection, in the purely objective sense that it rejects
some states, by leaving them, and retains some otherstate, by sticking to it. “This reduction in the number ofreachable states signifies that the variety, and hence thestatistical entropy, of the system diminishes. It is because ofthis increase in neguentropy or organization that Ashby calls
the process self-organization. But how does this fit in withthe 2nd law of thermodynamics, which states that entropy inclosed systems cannot decrease? The easy way out is toconclude that such a self-organizing system cannot be
closed, and must lose entropy to its environment (vonFoerster, 1960).
A deeper understanding can be reached by going back fromthe statistical definition of entropy to the thermodynamic
one, in terms of energy or heat. Energy is defined as thecapacity to do work, and working means making changes,that is to say exerting variation. Hence energy can ve
viewed as potential variation. A stable configuration does
not undergo variation. In order to destroy a stableequilibrium, you need to add energy, and the more stable theconfiguration, the more energy you will need. Therefore
stability is traditionally equated with minimal energy.
The 1st law of thermodynamics states that energy isconserved. A naive interpretation of that law would conclude
that the principle of asymmetric transitions cannot be valid,
since it postulates a transition from an unstable (high energy)
to a stable (low energy) configuration. If energy isabsolutely conserved, then an unstable configuration can
only be followed by another unstable configuration. This is
the picture used in classical mechanics, where evolution isreversible, that is to say symmetric. Incidentally, this showsthat the principle of asymmetric transitions is not
tautological - though it may appear self-evident - , since a
perfectly consistent theory (classical mechanics) can be built
on its negation.
Thermodynamics has enlarged that picture by allowing
energy dissipation. But what happens with the “dissipated”
energy? A simple model is provided by a quantum system(e.g. an electron bound in an atom) with its set of - usuallydiscrete - energy levels. A configuration at a higher level
will spontaneously fall down to a lower level, emitting aphoton which carries the surplus energy away. In order tobring back the electron to its higher level, energy must be
added by having a photon of the right energy and direction
hit the electron, a rather improbable event. Hence, the lowlevel can be viewed as a stable configuration, with a small
probability of transition.
The conjunction of energy conservation and asymmetrictransitions implies that configurations will tend to dissipate
energy (or heat) in order to move to a more stable state. Fora closed system, this is equivalent to the thermodynamicalinterpretation of the 2nd law, but not to the statistical one, as
the statistical entropy can decrease when transition
probabilities are asymmetric. In an open system, on the other
hand, where new energy is continuously added, theconfiguration will not be able to reach the minimum energy
level. In that case we might assume that it will merely tend
to maximally dissipate incoming energy, since transitions
where energy is emitted are (much) more probable than

transitions where energy is absorbed. That hypothesis
seems equivalent to the Law of maximum entropy
production (Swenson, 19), which describes dissipative
structures and other far-from-equilibrium configurations.
In such configurations the stability is dynamic, in thesense that what is maintained is not a static state but aninvariant process.
Such an application of the principle of asymmetrictransitions is opposite to the most common interpretationof the 2nd law, namely that disorder and with ithomogeneity tend to increase. In the present view,
configurations tend to become more and more stable,
emitting energy in the process. This might be seen as a
growing differentiation between the negative energy ofstable bonds, and the positive energy of photons andmovement. Recent cosmological theories hypothesize asimilar spontaneous separation of negative and positiveenergies to account for the creation of the universe out ofa zero-energy vacuum (Hawking, 1988).

5

The Principle of Blind Variation

At the most fundamental level variation processes “donot know” which of the variants they produce will turnout be be selected
This principle is not self-evident, but can be motivatedby Ockham’s razor. If it were not valid, we would haveto introduce some explanation (e.g. design by God) toaccount for the “foreknowledge” of variation, and thatwould make the model more complicated than it needs to
be. The blindness of variation is obvious in biologicalevolution, based on random mutations andrecombinations. Yet even perfectly deterministic
dynamical systems can be called blind, in the sense that
if the system is complex enough it is impossible topredict whether the system will reach a particular
attractor (select a stable configuration of states) without
explicitly tracing its sequence of state transitions(variation) (Heylighen, 1991).
Of course many interactions are not blind. If I tackle apractical problem, I normally do not try out things at
random, but rather have some expectations of what will
work and what will not. Yet this knowledge itself was
the result of previous trial-and-error processes, where theexperience of success and failure was selectively retainedin my memory, available for guiding later activities.
Similarly, all knowledge can be reduced to inductive
achievements based on blind-variation-and-selectiveretention (BVSR) at an earlier stage. Together with
Campbell (1974), I postulate that it must be possible toexplain all cases of “non-blindness” (that is to say
variation constrained in such a way as to make it more
likely to satisfy selection) as the result of previous BVSR
processes.
The BVSR formula summarizes three previous
principles: selective retention, asymmetric transitions,and blind variation. The second principle is implicit in the“and” of “blind-variation-and-selective-retention”, since it
ensures that configurations produced by blind variation canmake the transition to selective retention, unlike
configurations in classical mechanics which remain unstable.

6

The Principle of Selective Variety

The larger the variety of configurations a system undergoes,the larger the probability that at least one of these
configurations will be selectively retained.
Although this principle is again self-evident or tautologous,
it leads to a number of useful and far from trivial
conclusions. For example, the less numerous or the farther
apart potential stable configurations are, the more variation(passing through a variety of configurations) the system will
have to undergo in order to maintain its chances to find astable configuration. In cases where selection criteria,
determining which configurations are stable and which are
not, can change, it is better to dispose of a large variety ofpossible configurations. If under a new selective regime
configurations lose their stability, a large initial variety will
make it probable that at least some configurations will retain
their stability. A classic example is the danger ofmonoculture with genetically similar or identical plants: asingle disease or parasite invasion can be sufficient todestroy all crops. If there is variety, on the other hand, there
will always be some crops that survive the invasion.
Another special case is the “order from noise” principle (vonFoerster, 1960), related to “order out of chaos”. Noise orchaos can here be interpreted as rapid and blind variation.
The principle states that addition of such noise makes it
more likely for a system to evolve to an ordered (stable)
configuration. A practical application is the technique of(simulated) annealing, where noise or variation is applied instepwise decreasing amounts, in order to reach a maximallystable configuration.

7

The Principle of Recursive Systems
Construction

BVSR processes recursively construct stable systems by therecombination of stable building blocks
The stable configurations resulting from BVSR processes
can be seen as primitive elements: their stability
distinguishes them from their variable background, and this
distinction, defining a “boundary”, is itself stable. The
relations between these elements, extending outside theboundaries, will initially still undergo variation. A change ofthese relations can be interpreted as a recombination of theelements. Of all the different combinations of elements,
some will be more stable, and hence will be selectively
retained.
Such a higher-order configuration might now be called asystem. The lower-level elements in this process play therole of building blocks: their stability provides thefirmness needed to support the construction , while theirvariable connections allow several configurations to betried out. The principle of “the whole is more than thesum of its parts” is implied by this systemic construction
principle, since the system in the present conception ismore than a mere configuration of parts, it is a stableconfiguration, and this entails a number of emergent
constraints and properties (Heylighen, 1991). A stablesystem can now again function as a building block, andcombine with other building blocks to a form anassembly of an even higher order, in a recursive way.
Simon (1962) has argued in his famous “TheArchitecture of Complexity” that such stable assemblies
will tend to contain a relatively small number of building
blocks, since the larger a specific assembly, the lessprobable that it would arise through blind variation. This
leads to a hierarchical architecture, that can be
represented by a tree.
Two extensions must be made to the Simon argument(cf. Heylighen, 1989). 1) If one takes into accountautocatalytic growth, as when a small stable assembly
makes it easier for other building blocks to join theassembly, the number of building blocks at a given levelcan become unlimited. 2) It is possible, though lessprobable, that a given building block would participate inseveral, overlapping stable assemblies; it suffices that its
configuration would satisfy two (or more) selection
criteria, determining stable systems. It is clear, however,that the more selection criteria a configuration would
have to satisfy, the less likely that such a configuration
would be discovered by blind variation. These two points
lead us to generalize the tree structure of Simon’s“nearly-decomposable” architecture to a loose or quasihierarchy (Joslyn, 1991), which in parts can be very flat,
and where some nodes might have more than one mother
node.

8

Control systems

The previous principles provide a set of mechanisms
describing the spontaneous emergence and selforganization of multilevel systems, becoming ever morestable (in a generalized, ‘dynamical’ sense), more fit, andmore complex. Control systems are a specific type ofsuch multilevel systems, where a stable configuration is
maintained by selectively counteracting perturbations.
There is no space here to examine in detail how control
systems emerge through BVSR, but the issue can beclarified by considering the concept of an anticipatory orvicarious selector (Campbell, 1974).
A selector is a stable system capable of selecting
variation. A vicarious selector carries this selection out inanticipation of something else, e.g. the environment or“Nature” at large. For example, molecule configurations
selectively retained by a crystal template are intrinsicallystable, and would have been selected even without thepresence of a template. The template basically accelerates(catalyses) selection, and thus can be said to anticipate, or tovicariously represent, the naturally selected configuration.
The selection made by a template is invariant. However,
one can also imagine anticipatory selectors making different
selections under different circumstances, compensating
different perturbations by different actions. This anticipatory
selection has the advantage that inadequate internalvariations will no longer lead to the destruction of thesystem, since they will be eliminated before the system as awhole becomes unstable.
This mechanism can be illustrated by considering what
Powers (1989) calls the most primitive example of a control
system, a bacterium that changes the rate of randomvariation of its movements in function of the favorablenessof its environment. When the concentration of foodincreases, its variation of movement becomes small. Whenthe concentration of food decreases (or that of poison
increases), there is a strong variation. The only selection thebacterium makes is that between moving in the same
direction (selective retention), or changing course (blind
variation). That selection anticipates the natural selectionthat would happen if the bacterium was passive (that is tosay, if it was not exerting control): if it would stay longenough in the unfavorable place, it would die; if it would
move to a more favorable place it would survive. The
bacterium is in fact applying the principle of selective
variety: it increases variation when the chances of being
selectively retained become less. This internally directed,
selective counteraction of perturbations from a stableconfiguration can be taken as a definition of control. This
leads us straightforwardly to a derivation of some classic
principles of control.

9

The Law of Requisite Variety

The larger the variety of actions available to a control
system, the larger the variety of perturbations it is able tocompensate.
This is another application of the principle of selective
variety, formulated above. However, a stronger form of
Ashby’s Law (1958), “the variety in the control system must
be equal to or larger than the variety of the perturbations inorder to maintain stability”, does not hold in general. Indeed
the underlying “only variety can destroy variety” assumption
is in contradiction with the principle of asymmetrictransitions which implies that spontaneous decrease ofvariety is possible. For example, the bacterium describedabove disposes of a minimal variety of only two actions:increase or decrease the rate of random movements. Yet, it iscapable to cope with a quite complex environment, withmany different types of perturbations (Powers, 1989). Its
blind “transitions” are normally sufficient to find afavourable (“stable”) situation, thus escaping all dangers.

10 The Law of Requisite Knowledge
In order to adequately compensate perturbations, acontrol system must “know” which action to select fromthe variety of available actions.
This principle reminds us that a variety of actions is not
sufficient for effective control, the system must be ableto (vicariously) select an appropriate one. Without
knowledge, the system would have to try out an action
blindly, and the larger the variety of perturbations, thesmaller the probability that this action would turn out tobe adequate. Notice the tension between this law and theprevious one: the more variety, the more difficult the
selection to be made, and the more complex the requisiteknowledge. “Knowing” signifies that the internal(vicarious) selector must be a model or representation of
the external, potentially selecting perturbations. Ideally,
to every class of perturbations there corresponds a classof adequate counteractions. This correspondence might
be represented as a homomorphism from the set of
perturbations to the set of (equivalence classes of)compensations. In the case of the bacterium, the class offavourable situations is mapped onto the action “decrease
variation”, whereas unfavourable situations are mapped
onto “increase variation”. However, this does not imply
that knowledge would consist of a homomorphic imageof the objects in the environment. Only the (perturbing)processes of the environment need to be represented, notits static structure.
An equivalent principle was formulated by Conant andAshby (1970) as “Every good regulator of a system must
be a model of that system”. Therefore the present
principle can also be called the law of regulating models.

time in the model as in the real world, and no anticipation
would be possible, precluding any control. Finally, models
are constructed by blind variation processes, and, hence,cannot be expected to reach any form of complete
representation of an infinitely complex environment.
Acknowledgments: I thank C. Joslyn, V. Turchin and otherPrincipia Cybernetica contributors for a preliminary
discussion, inciting me to clarify many points in the draft.

12 References
Ashby W.R. (1958): “Requisite Variety and Implications forControl of Complex Systems”, Cybernetica 1, p.
83-99.
Ashby W.R. (1962): “Principles of the Self-Organizing
System”, in:
Principles of Self-Organization, von Foerster H. & Zopf
G.(eds.), (Pergamon, Oxford), p. 255-278.
Campbell D.T. (1974): “Evolutionary Epistemology”, in:
The Philosophy of Karl Popper, Schilpp P.A. (ed.),(Open Court Publish., La Salle, Ill.), p. 413-463.
Conant R.C. and Ashby W.R. (1970): “Every Good
Regulator of a System Must Be Model of that
System”, Int. J. Systems Science 1:2, p. 89-97.
Hawking S.W. (1988): A Brief History of Time, (Bantam,
London).
Heylighen F. (1989): “Self-Organization, Emergence and theArchitecture of Complexity”, in: Proc. 1st Eur.
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11 The Principle of Incomplete
Knowledge

Heylighen F. (1991): “Modelling Emergence”, World
Futures 31, p. 89-104.

The model embodied in a control system is necessarily
incomplete

Heylighen F. (forthcoming): “Principles of Evolution and
Self-organization”, to be submitted to Int. Journalof General Systems.

This principle can be deduced from a lot of other, more
specific principles: Heisenberg’s uncertainty principle,
implying that the information a control system can get isnecessarily incomplete; the relativistic principle of thefiniteness of the speed of light, implying that the moment
information arrives, it is already obsolete to some extent;
the principle of bounded rationality (Simon, 1957),
stating that a decision-maker in a real-world situation
will never have all information necessary for making an
optimal decision; the principle of the partiality of selfreference (Loefgren, 1990), a generalization of Goedel’s
incompleteness theorem, implying that a system cannot
represent itself completely, and hence cannot havecomplete knowledge of how its own actions may feedback into the perturbations. As a more general argument,
one might note that models must be simpler than thephenomena they are supposed to model. Otherwise,
variation and selection processes would take as much

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Gravel Switch: KY), p. 253-293.
Simon H.A. (1957): Models of Man : Social and Rational,(Wiley, London).

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to a theory of general evolution”, Systems
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* Senior Research Assistant NFWO (Belgian National
Fund for Scientific Research)