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The Global Superorganism: an evolutionary-cybernetic model of the emerging network society

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The organismic view of society is updated by incorporating concepts from cybernetics, evolutionary theory, and complex adaptive systems. Global society can be seen as an autopoietic network of self-producing components, and therefore as a living system or superorganism. Millers living systems theory suggests a list of functional components for societys metabolism and nervous system. Powers perceptual control theory suggests a model for a distributed control system implemented through the market mechanism. An analysis of the evolution of complex, networked systems points to the general trends of increasing efficiency, differentiation and integration. In society these trends are realized as increasing productivity, decreasing friction, increasing division of labor and outsourcing, and increasing cooperativity, transnational mergers and global institutions. This is accompanied by increasing functional autonomy of individuals and organizations and the decline of hierarchies. The increasing complexity of interactions and instability of certain processes caused by reduced friction necessitate a strengthening of societys capacity for information processing and control, i.e. its nervous system. This is realized by the creation of an intelligent global computer network, capable of sensing, interpreting, learning, thinking, deciding and initiating actions: the global brain. Individuals are being integrated ever more tightly into this collective intelligence. Although this image may raise worries about a totalitarian system that restricts individual initiative, the superorganism model points in the opposite direction, towards increasing freedom and diversity. The model further suggests some specific futurological predictions for the coming decades, such as the emergence of an automated distribution network, a computer immune system, and a global consensus about values and standards.
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submitted to: Journal of Social and Evolutionary Systems
The Global Superorganism:
an evolutionary-cybernetic model of the
emerging network society
Francis HEYLIGHEN
CLEA, Free University of Brussels, Pleinlaan 2, B-1050 Brussels, Belgium
E-mail: fheyligh@vub.ac.be
ABSTRACT. The organismic view of society is updated by incorporating concepts
from cybernetics, evolutionary theory, and complex adaptive systems. Global
society can be seen as an autopoietic network of self-producing components, and
therefore as a living system or “superorganism”. Miller’s living systems theory
suggests a list of functional components for society’s metabolism and nervous
system. Powers’ perceptual control theory suggests a model for a distributed control
system implemented through the market mechanism. An analysis of the evolution of
complex, networked systems points to the general trends of increasing efficiency,
differentiation and integration. In society these trends are realized as increasing
productivity, decreasing friction, increasing division of labor and outsourcing, and
increasing cooperativity, transnational mergers and global institutions. This is
accompanied by increasing functional autonomy of individuals and organizations
and the decline of hierarchies. The increasing complexity of interactions and
instability of certain processes caused by reduced friction necessitate a strengthening
of society’s capacity for information processing and control, i.e. its nervous system.
This is realized by the creation of an intelligent global computer network, capable of
sensing, interpreting, learning, thinking, deciding and initiating actions: the
“global brain”. Individuals are being integrated ever more tightly into this
collective intelligence. Although this image may raise worries about a totalitarian
system that restricts individual initiative, the superorganism model points in the
opposite direction, towards increasing freedom and diversity. The model further
suggests some specific futurological predictions for the coming decades, such as the
emergence of an automated distribution network, a computer immune system, and a
global consensus about values and standards.
KEYWORDS: superorganism, global brain, collective intelligence, cybernetics,
networks, evolution, self-organization, society, globalization, complexity, division
of labor, living systems.
1. Introduction
It is an old idea that society is in a number of respects similar to an organism, a living
system with its cells, metabolic circuits and systems. In this metaphor, different
organizations or institutions play the role of organs, each fulfilling its particular function
in keeping the system alive. For example, the army functions like an immune system,
protecting the organism from invaders, while the government functions like the brain,
steering the whole and making decisions. This metaphor can be traced back at least as far
as Aristotle (Stock, 1993). It was a major inspiration for the founding fathers of
sociology, such as Comte, Durkheim and especially Spencer (1969).
- 1 -
THE GLOBAL SUPERORGANISM 2
The organismic view of society has much less appeal to contemporary theorists.
Their models of society are much more interactive, open-ended, and indeterministic than
those of earlier sociologists, and they have learned to recognize the intrinsic complexity
and unpredictability of society. The static, centralized, hierarchical structure with its
rigid division of labor that seems to underlie the older organismic models appears poorly
suited for understanding the intricacies of our fast-evolving society. Moreover, a vision
of society where individuals are merely little cells subordinated to a collective system
has unpleasant connotations to the totalitarian states created by Hitler and Stalin, or to
the distopias depicted by Orwell and Huxley. As a result, the organismic model is at
present generally discredited in sociology.
In the meantime, however, new scientific developments have done away with rigid,
mechanistic views of organisms. When studying living systems, biologists no longer
focus on the static structures of their anatomy, but on the multitude of interacting
processes that allow the organism to adapt to an ever changing environment. Most
recently, the variety of ideas and methods that is commonly grouped under the header
of “the sciences of complexity” has led to the understanding that organisms are self-
organizing, adaptive systems. Most processes in such systems are decentralized,
indeterministic and in constant flux. They thrive on “noise”, chaos, and creativity. Their
collective intelligence emerges out of the free interactions between individually
autonomous components. Models that explain organization and adaptation through a
central, “Big Brother”-like planning module have been found unrealistic for most
systems.
This development again opens up the possibility of modelling both organisms and
societies as complex, adaptive systems (CAS). Indeed, the typical examples studied by
the CAS approach (Holland, 1996; 1992) are either biological (the immune system, the
nervous system, the origin of life) or social (stock markets, economies (Anderson,
Arrow, & Pines, 1988), ancient civilisations). However, this approach is as yet not very
well developed, and it proposes a set of useful concepts and methods rather than an
integrated theory of either organisms or societies.
The gap may be filled by a slightly older tradition, which is related to the CAS
approach: cybernetics and systems theory. Although some of the original cybernetic
models may be reminiscent of the centralized, hierarchical view, more recent approaches
emphasize self-organization, autonomy, decentralization and the interaction between
multiple agents. Within the larger cybernetics and systems tradition, several models
were developed that can be applied to both organisms and social systems: Miller's
(1978) living systems theory, Maturana's and Varela's (1980, 1992) theory of
autopoiesis, Powers' (1973, 1989) perceptual control theory, and Turchin's (1977)
theory of metasystem transitions.
These scientific approaches, together with the more mystical vision of Teilhard de
Chardin (1955), have inspired a number of authors in recent years to revive the
organismic view (de Rosnay, 1979, 1986, 2000; Stock, 1993; Russell, 1995; Turchin,
1977, 1981; Chen & Gaines, 1997). This gain in interest was triggered in particular by
the spectacular development of communication networks, which seem to function like a
nervous system for the social organism. However, these descriptions remain mostly on
the level of metaphor, pointing out analogies without analysing the precise mechanisms
that underlie society's organism-like functions.
The present paper sets out to develop a new, more detailed, scientific model of
global society which integrates and builds upon these various approaches, thus updating
the organismic metaphor. The main contribution I want to make is a focus on the
process of evolution, which constantly creates and develops organization. Because of
this focus on on-going development, the proposed model should give us a much better
understanding of our present, fast changing society, and the direction in which it is
3 HEYLIGHEN
heading. The “cybernetic” foundation in particular will help us to analyse the
increasingly important role of information in this networked society.
The main idea of this model is that global society can be understood as a
superorganism, and that it becomes more like a superorganism as technology and
globalization advance. A superorganism is a higher-order, “living” system, whose
components (in this case, individual humans) are organisms themselves. Biologists agree
that social insect colonies, such as ant nests or bee hives, can best be seen as such
superorganisms (Seeley, 1989). If individual cells are considered as organisms, then a
multicellular organism too is a superorganism. Human society, on the other hand, is
probably more similar to “colonial” organisms, like sponges or slime molds, whose cells
can survive individually as well as collectively. Unlike social insects, humans are
genetically ambivalent towards social systems, as illustrated by the remaining conflicts
and competition between selfish individuals and groups within the larger society
(Heylighen & Campbell, 1995; Campbell, 1982, 1983).
The issue here, however, is not so much whether human society is a superorganism
in the strict sense, but in how far it is useful to model society as if it were an organism.
This is what Gaines (1994) has called the “collective stance”: viewing a collective as if it
were an individual in its own right. My point is that this stance will help us to make
sense of a variety of momentous changes that are taking place in the fabric of society,
and this more so than the more traditional stance which views society merely as a
complicated collection of interacting individuals (cf. Heylighen & Campbell, 1995).
More generally, my point is that both societies and biological organisms can be seen as
special cases of a more general category of “living” or “autopoietic” systems that will be
defined further on.
The paper will first try to determine what it exactly means for a system to be an
“organism”, and look in more detail at two essential subsystems of any organism:
metabolism and nervous system. It will then argue that society's metabolism and
nervous system, under the influence of accelerating technological change, are becoming
ever more efficient and cohesive. This evolution will in particular give rise to the
emergence of a “global brain” for the superorganism. Finally, the paper will try to look
at some of the radical implications of this development for the future.
2. Society as an Autopoietic System
If we want to characterize society as a living system, we will first need to define what
life is, in a manner sufficiently general to be applicable to non-DNA-based systems.
Perhaps the best abstract characterization of living organization was given by Maturana
and Varela (1980; 1992): autopoiesis (Greek for “self-production”). An autopoietic
system consists of a network of processes that recursively produces its own
components, and thus separates itself from its environment. This defines an autopoietic
system as an autonomous unit: it is responsible for its own maintenance and growth,
and will consider the environment merely as a potential cause of perturbations for its
inner functioning. Indeed, a living cell can be characterized as a complex network of
chemical processes that constantly produce and recycle the molecules needed for a
proper functioning of the cell.
Reproduction, which is often seen as the defining feature of life, in this view is
merely a potential application or aspect of autopoiesis: if you can produce your own
components, then you can generally also produce an extra copy of those components.
Reproduction without autopoiesis—which can be designated more precisely as
replication—does not imply life: certain crystals, molecules and computer viruses can
replicate without being alive. Conversely, autopoiesis without reproduction does imply
life: you would not deny your childless aunt the property of being alive because she is
no longer capable of giving birth.
THE GLOBAL SUPERORGANISM 4
S
I
O
a
b
c
d
e
f
g
h
i
j
k
l
E
Fig. 1: An autopoietic network.
The system S consists of a network of components or subsystems {a, b, c, d, ...} that are connected to
each other via their inputs and outputs, recursively producing its own organization. For example,
component l receives input (goods, services, information, ...) from k and h, processes this input and
passes on the resulting product to c. The network is mostly closed (the paths connecting components are
cyclical) but it still receives some input I from the environment E, and passes on some output O to this
same environment. There are in general many redundant or “parallel” paths that start with the same
component (e.g. i) and end in the same component (e.g. l). In this particular case, the component h may
be performing the same function for l as j and k, and therefore l might decide to “bypass” the longer
process i j k l, in favor of the shorter process i h l.
Taking autopoiesis rather than reproduction as a defining characteristic removes one
major obstacle to the interpretation of societies as living: although societies generally do
not reproduce, they undoubtedly produce their own components. The physical
components of society can be defined as all its human members together with their
artefacts (buildings, cars, roads, computers, books, etc.). Each of these components is
produced by a combination of other components in the system. People, with the help of
artefacts, produce other people, and artefacts, with the help of people, produce other
artefacts. Together, they constantly recreate the fabric of society. (To the non-human
components of society we may in fact add all domesticated plants and animals, that is
to say, that part of the global ecosystem whose reproduction is under human control.
As human control expands, this may come to include the complete biosphere of the
Earth, so that the social superorganism may eventually encompass Gaia, the “living
Earth” superorganism postulated by some theorists).
These processes of self-production clearly exhibit the network-like, cyclical
organization that characterizes autopoiesis (see Fig. 1): a component of type a is used
to produce a b component, which is used to produce a c, and so, on, until a z is again
used to produce an a.
5 HEYLIGHEN
Although societies rarely reproduce, in the sense of engendering another,
independent society, their autopoiesis gives them in principle the capacity for
reproduction. It could be argued that when Britain created colonies in regions like North
America and Australia, these colonies, once they became independent, should be seen as
offspring of British society. Like all children, the colonies inherited many
characteristics, such as language, customs and technologies, from their parent, but still
developed their own personality. This form of reproduction is most similar to the type
of vegetative reproduction used by many plants, such as vines and grasses, where a
parent plant produces offshoots, spreading ever further from the core. When such a
shoot, once it has produced its own roots, gets separated from the mother plant, it will
survive independently and define a new plant. Thus, the growth of society is more like
that of plants than like that of the higher animals that we are most familiar with: there is
no a priori, clear separation between parent and offspring. As we will discuss further, in
the present globalized world geographical separation is no longer sufficient to create
independence. Yet, we could still imagine global society spawning offspring in the form
of colonies on other planets.
A society, like all autopoietic systems, is an open system: it needs an input of
matter and energy (resources) to build its components, and it will produce an output of
matter and energy in the form of waste products and heat. In spite of being
thermodynamically open, an autopoietic system is organizationally closed: its
organization is determined purely internally. The environment does not tell the system
how it should organize itself; it merely provides raw material. The autopoietic system
contains its own knowledge on how to organize its network of production processes.
Closure means that every component of the system is produced by one or more other
components of the same system. No component or subsystem of components is
produced autonomously. If it were, the subsystem would itself constitute an
independent autopoietic system, instead of being merely a component of the overall
system.
This requirement of closure is perhaps what makes the application of autopoiesis
to social systems so controversial. Closure distinguishes what is inside, part of the
system, from what is outside, part of the environment. Maturana and Varela's (1980)
original definition of autopoiesis adds to this that an autopoietic system should produce
its own boundary, that is, a spatial or topological separation between system and
environment. Unlike biological organisms, most social systems do not have a clear
spatial boundary. Moreover, for most social systems the closure requirement is only
partially fulfilled. For example, a country may produce most of its essential
components internally, but it will still import some organized components (people,
artefacts) or knowledge from outside. This means that any boundary we could draw
around a social system will be porous or fuzzy. The only way to fulfil the requirement
of organizational closure is to consider global society as a whole as an autopoietic
system. None of its subsystems, whether they be countries, corporations, institutions,
communities or families is properly autopoietic. All of them are to some extent
dependent on outside organization for their maintenance.
This observation may explain why different authors disagree about whether social
systems can be autopoietic. Although Maturana and Varela, the originators of the
autopoiesis concept, would restrict it to biological organisms, several others (e.g.
Luhmann, 1995; Robb, 1989; Zeleny & Hufford, 1991; see Mingers, 1994, for a review)
have suggested that social systems can be autopoietic, while disagreeing about exactly
which systems exhibit autopoiesis. To me, it seems that the controversy can be resolved
by only considering global society, the supersystem which encompasses all other social
systems, as intrinsically autopoietic.
The problem of the boundary can be resolved by relaxing the requirement that an
autopoietic system should produce a physical boundary in space (like the membrane
THE GLOBAL SUPERORGANISM 6
enveloping cells). Although countries, cities or firms sometimes do produce physical
boundaries, such as walls or an “Iron Curtain”, planetary society has no need for such a
boundary. Indeed, the Earth on which we live offers its own boundary, consisting of the
atmosphere which protects the social organism from cosmic rays and meteorite impact,
and the lithosphere, which protects its from the heat and magma inside the planet. If an
organism, such as a hermit crab, uses a readily available encasing or shell for its
protection, rather than invest effort in producing one of its own, then we can hardly
blame it for not being sufficiently autopoietic.
If we take the concept of the boundary in a less literal, not purely physical sense,
then society clearly does separate its internal components from the environment. The
mechanism an organism uses to distinguish and separate insiders from outsiders is the
immune system. The immune system is programmed to recognize and expel all alien
material, all “trespassers” that do not obey the rules of the game. These trespassers may
in fact include internally produced components, such as cancer cells, that for some
reason have stopped obeying the laws that govern the organization. Society too has an
immune system that will try to control both external invaders (e.g. wild animals,
infectious diseases, hurricanes, foreign enemies) and internal renegades (e.g. criminals,
terrorists, computer viruses). Basic components of a society's immune system are the
police, justice and army.
Both the greatest strength and the greatest weakness of the concept of autopoiesis
is its all-or-none character: a system is either organizationally closed, or it is not; it is
either alive, or dead. In practice, the distinction between internally and externally
produced organization is not always that clear-cut. Organisms do not just need raw
matter and energy as input: these resources must exhibit some form of organization. For
example, an animal, unlike a plant, cannot produce its components on the basis of air,
water and minerals. The resources an animal needs must already have gone through some
degree of organization into complex organic molecules, such as lipids, carbohydrates,
proteins and vitamins. Similarly, society is to some degree dependent on organization in
the outside world. For example, our present society is dependent for furniture and
firewood on trees, and is dependent for energy on fossil fuels produced by plants
millions of years ago.
This observation suggests that we distinguish degrees of autopoiesis: a system will
be more autopoietic if it produces more of its organization internally, and thereby
becomes less dependent on its environment. As we will discuss later, the evolution of
society will typically lead to more autonomy and a greater capacity to internally
produce organization with a minimum of external input.
To understand how society achieves autopoiesis, we must look in more detail at
how the network of production processes can produce a stable organization, in spite of
a variable input of resources and various perturbations in the environment. This
mechanism can be functionally decomposed into different tasks to be performed by
different subsystems. The most important decomposition is the one distinguishing
metabolism, responsible for the processing of matter and energy, and nervous system,
responsible for the processing of information. The purpose of both subsystems is to
maintain a stable identity by compensating or buffering the effect of perturbations. We
will now discuss in more detail the different components for each of the subsystems,
and the way they are connected.
3. Metabolism: processing of matter-energy
Organisms are dissipative systems (Nicolis & Prigogine, 1977): because of the second
law of thermodynamics, they must export entropy or heat in order to maintain a
dynamic steady state. This means that matter and/or energy must enter the system in
7 HEYLIGHEN
low entropy form (input I in fig. 1) and leave the system in high entropy form (output
O in fig. 1), after undergoing a number of conversions. The entropy that is dissipated or
“wasted” by the system is needed to keep up the cycle of production processes that
maintains its organization.
Although autopoiesis theorists focus on the closed, internal cycle of processes
inside an organism, the fact that this cycle has an input and an output allows us to make
a more of less “linear” decomposition, which follows matter sequentially from the
moment it enters the system, through the processing it undergoes, until the moment it
exits. The systems theorist James Grier Miller (1978) has proposed a detailed
decomposition scheme which can be used to analyse any “living system”, from a cell to
a society. It must be emphasized that such decomposition is functional, but not in
general structural. This means that the functional subsystems we will distinguish do not
necessarily correspond to separate physical components: the same function can be
performed by several physical or structural components, while the same component can
participate in several functions. Although complex organisms tend to evolve organs, i.e.
localized, structural components specialized in one or a few functions (e.g. the heart for
pumping blood), other functions remain distributed throughout the organism (e.g. the
immune system).
Since this decomposition does not take into account autopoiesis or organizational
closure, Miller applies his living system model also to systems—such as organs or
communities—which are organizationally open and which I therefore would not classify
as “organisms”. It seems to me that to fully model organism-like systems, we need to
integrate organizational closure with its focus on cycles and thermodynamical openness
with its focus on input-output processing (cf. Heylighen, 1990). In the following I will
discuss the main functional subsystems of an “organism”, using examples both from the
animal body and from society. For the societal examples I will focus on artefacts, so as
not to repeat the bodily functions that society's human components share with other
biological organism.
Function Body Society
Ingestor eating, drinking, inhaling mining, harvesting,
pumping
Converter digestive system, lungs refineries, processing plants
Distributor circulatory system transport networks
Producer stem cells factories, builders
Extruder urine excretion, defecation,
exhaling
sewers, waste disposal,
smokestacks
Storage fat, bones warehouses, containers
Support skeleton buildings, bridges...
Motor muscles engines, people, animals
Table 1: Functional subsystems of the metabolism (processing of matter-energy) in
animals and in societies.
The first function in Miller's model is the ingestor, the subsystem responsible for
bringing matter and energy from the environment into the system. In fig. 1, for example,
the components a and b, that directly receive input from the environment, participate in
the ingestor function. In animals, this role is typically played by mouth and nose, to
swallow food and inhale air. In society, the ingestor is not so clearly localised. Its role is
THE GLOBAL SUPERORGANISM 8
played by diverse systems such as mines and quarries, which extract ores from the soil,
water pits, and oil pumping installations. The next processing stage takes place in the
converter, which transforms the raw input into resources usable by the system. For
example, in Fig. 1 insofar that a and b have not already processed the input they
received from the environment, we could situate the converter function in components
such as g that receive their input from a or b. In the body, this function is carried out by
the digestive system, which reduces diverse morsels of food to simple sugars, fatty
acids and amino acids, and by the lungs which ensure that the oxygen fraction of the
inhaled air is dissolved into the blood, where it is taken up by the hemoglobin in the red
blood cells. In society, the converter function is performed by different refineries and
processing plants, which purify water, oil and ores.
A usually subsequent processing stage is transport to those places where the
resources are needed. This is the responsibility of the distributor. In an autopoietic
network such as Fig. 1 all components whose output is similar to their input, but
delivered at a different location, can be said to partake in the distribution function. In
animals, the distributor function is carried out by the circulatory system: heart and
blood-vessels. In society, this is the role of the transport system: pipelines, ships,
railways, planes, roads. Resources that have arrived at their destination are then
processed in order to produce components for the organism. In animals, this producer
function is carried out by stem cells and glands that produce either other cells or specific
chemicals, such as enzymes and hormones. In society, this is done by different plants
and factories, producing specialized goods. These products can again be transported by
the distributor to wherever they are needed.
One destination where many products end up is storage: since the supply of
resources from the environment is variable, and internal production cannot always be
adjusted to the present need, it is necessary to have a reserve of resources and products,
that will help to buffer against fluctuations. In an autopoietic network, components
whose output is similar to their input, but delivered at a later time, can be seen as
contributing to the storage function. In the body, different organs can fulfil the function
of storage for different products. The most general reserve is the one of fat, which can
be used as an all-round supply of energy. In society, products are stored in warehouses,
silos and containers. Another important destination for products is the support
function, which physically upholds, protects and separates different parts of the
organism. In the body, this function is performed by the skeleton. In society as a whole,
which does not have a clear physical structure, the support function is not really
needed, but locally it is performed by structures such as buildings, bridges and walls.
Another destination is the motor, the subsystem that uses energy to generate motion for
the organism. In the body, the motor function is performed by muscles, in society by
different engines and machines.
Products are typically transformed and recycled into other products. For example,
when a cell dies, the lipids that form its membranes will be reused by the body to build
other membranes, or stored in fat reserves. In society, the steel of discarded cars will be
reprocessed to build cans, steel rods or new cars. Because of the second law of
thermodynamics, processes can never be completely reversed: there is always some
loss, which is accompanied by the production of entropy. This means that processes
will always bring about waste, which cannot be fully recycled. These waste products
must be separated from the still usable products and collected. In the body, this is the
function of the liver and kidneys, which filter waste products out of the blood. In
society, it is carried out by garbage collectors and installations for the treatment of
waste. The final matter processing subsystem is the extruder, which expulses the waste
products out of the system. In the network of Fig. 1, the components d and e that
deliver output straight into the environment, can be seen as part of the extruder. In the
body, this function is performed by the urinary tract, the rectum, and the lungs, which
9 HEYLIGHEN
get rid respectively of the liquid, solid and gaseous wastes. In society, the respective
subsystems are sewers, garbage dumps, and chimneys or exhausts.
4. Nervous System: information and control
Before proceeding with Miller's functional decomposition of information processing, we
must discuss the overall function of information in a closed organization. As Maturana
and Varela (1980, 1992) like to emphasize, an autopoietic system is not in-formed by
the environment: its form is determined purely by its internal organization. Autopoietic
systems are self-organizing. Data from the environment are only needed to warn the
system about perturbations of normal functioning, that may damage or destroy its
organization. By appropriately counteracting or compensating for these perturbations,
the system can maintain an invariant organization in a variable environment
(homeostasis).
Thus, organisms are by definition control systems in the cybernetic sense (Ashby,
1964): they regulate or control the values of certain essential variables, so as to
minimize deviations from the optimum range. For example, to sustain their intricate
organization, warm-blooded animals must maintain their body temperature within a
close range of temperatures (for humans, roughly around 36.5 degrees Celsius). If the
temperature of the environment changes, internal processes, such as transpiration or
shivering, will be activated to counteract the effect of these perturbations on the internal
temperature.
Possibly the clearest overall model of such regulation is proposed by William
Powers' (1973; 1989) theory of living control systems. In this model, the behavior or
sequence of actions of an organism is explained solely as an on-going attempt to bring
the situation perceived by the organism as close as possible to its goal or preferred state
(“reference level”). Actions change the state of the environment, and this state is
perceived by the organism in order to check in what way it deviates from the goal. The
sensed deviation triggers another action, intended to correct the remaining deviation. The
effect of this action is again sensed, possibly triggering a further action, and so on, in a
continuing negative feedback loop (see Fig. 2). This loop, if it functions well, keeps the
system in a remarkably stable state, in spite of the continuous tug of war between
environmental perturbations and compensating actions.
perturbations
perception
action
goal
SYSTEM
ENVIRONMENT
Fig. 2: a control system according to Powers
THE GLOBAL SUPERORGANISM 10
Although the resulting state may look largely static, the power exerted to
counteract perturbations requires a constant supply of energy. As Powers shows with
his mathematical models, an effective control loop is characterized by amplification:
small deviations must be compensated by relatively large actions. Otherwise, the result
will be merely a give and take between organism and environment, and the result will
depend as much on the perturbation as on the action. With large amplification, on the
other hand, the result will be much closer to the system's goal than to the external
disturbances. In addition to energetic action, such amplification requires very fine-
grained, sensitive perception, so that deviations can be detected at the earliest stage
where relatively little energy may be sufficient to counteract them.
The different goals or reference levels for the different variables that an organism
tries to optimize are typically arranged in a hierarchy, where a combination of
perception and a higher level goal determines a goal at the lower level. Thus, goals are
not static but adapt constantly to the perceived situation. This perception is not an
objective reflection of the state of the environment: it is merely a registration of those
aspects of the environment that are relevant to the system's goals, which themselves are
subordinated to the overall goal of survival and reproduction of the organization.
Therefore, the epistemology of both autopoiesis theory and perceptual control theory is
constructivist: an organism's knowledge should not be seen as an objective reflection of
outside reality, but as a subjective construction, intended to help find a way to reconcile
the system's overall goal of maintaining its organization with the different outside
perturbations that may endanger that goal.
For most non-cyberneticists, the word “control” connotes the image of a central
controller, an autocratic agent that oversees and directs the system being controlled. A
cybernetic analysis of the control relation, such as the one of Powers, on the other hand,
is purely functional. The “controller” does not need to be embodied in a separate
structural component. In fact, I have argued (Heylighen, 1997) that the market can be
seen as a distributed control system in the sense of Powers. The goal of the market
system is to satisfy “demand”, by producing a matching “supply”, in spite of
perturbations such as fluctuations in the availability of resources or components.
Demand for any particular commodity is itself determined by the overall perception of
availability of other commodities and the higher level goals or values (survival, quality of
life, ...) of the collective consumer.
This is a negative feedback loop with amplification: small fluctuations in the
supply will be sensed and translated into changes in the commodity's price, which is a
measure for the difference between supply and demand. Small increases in price
(perception) will lead producers to immediately invest more effort in production
(action) thus increasing the supply. This will in turn decrease the price, thus reducing
the deviation. Similarly, reductions in price will trigger decreased production and
therefore decreased supply and increased price. Thus, the market functions to regulate
the availability of commodities that the system needs. In spite of this unambiguous
control function, no single agent or group of agents is “in control”. The demand variable,
which directs the process, emerges from the collective desire of all consumers, while the
supply variable is the aggregate result of all actions by all producers. The control
function is not centralized, but distributed over the entire economic system.
With a few generalizations, this analysis can be developed into a general model for
the control mechanism of the social superorganism. Here, Miller's analysis can once
more come to our support. Again, we must note that while Miller's functional
subsystems are arranged more or less linearly, in the order of processing for information
that enters the system, the mechanism as a whole is cyclic: the information that exits the
system in the form of actions affects the environment, which in turn determines the
information that comes in through perception.
11 HEYLIGHEN
Function Animal Society
Sensor sensory organs reporters, researchers, ...
Decoder perception experts, politicians, public
opinion, ...
Channel and Net nerves, neurons communication media
Associator synaptic learning scientific discovery, social
learning, ...
Memory neural memory libraries, schools, collective
knowledge
Decider higher brain functions government, market, voters,
...
Effector nerves activating muscles executives
Table 2: Functions of the nervous system (processing of information) in animals and
societies.
Miller's first two subsystems fulfil Powers's function of perception: the input
transducer brings information from the environment into the system, similar to the
ingestor bringing matter into the system; the internal transducer plays the same role for
information originating inside the system. The function of this information is to signal
real or potential perturbations away from the goal (dangers, problems), and/or
opportunities to achieve the goal (resources, tools). These dangers and opportunities
may originate both inside and outside the system, but for simplicity we will discuss
them together as if they all come from outside, thus merging “input transducer” and
“internal transducer” into a single sensor function.
This can be motivated by the observation that a truly functional logic implies that
we should not consider the actual physical location of a problem or opportunity, but its
functional characteristic of being or not being under the control of the system.
Remember our discussion of the immune system as the functional “boundary” of an
autopoietic system: internal renegades, such as tumors, are as much perturbations to the
system as external invaders, such as pathogenic micro-organisms. Similarly, external
extensions of the body, such as clothes, tools or vehicles, are as much under the control
of the system as its own components and can therefore be functionally interpreted as
parts of the system.
In the body, the sensor function is performed by the sensory organs: eyes, ears,
nose, tongue and various cells sensitive to touch, heat and motion in skin, muscles and
joints, but also by internal chemoreceptors for hormones, etc.. In society, many
components take part in sensing: the market, reporters, scientists, polling institutions,
voters, and various automatic sensors such as seismographs, thermometers, and satellite
sensing installations.
The next information processing function is the decoder, which transforms the
incoming stimuli into internally meaningful information. In the control system model,
this interpretation process functions basically to relate information about the external
situation to the system's goals or values, thus making it easier to use this information as
a guideline for action. This implies that information irrelevant to any of the system's
goals is ignored or filtered out. The decoded information is then used by the decider
subsystem to select a particular action or sequence of actions in response to the
perceived state of the environment. In higher order control systems, where there is a
THE GLOBAL SUPERORGANISM 12
complex hierarchy of goals and subgoals, the actual actions selected may have little to do
with the present situation, but rather anticipate potential situations in an as yet far
away and uncertain future (cf. Heylighen, 1992). Exploratory behavior is an example of
such action that does not seem to have direct relations to the present situation and goal,
although in general it helps the system to find new opportunities to achieve its goals.
Only urgent danger signals will require immediate counteraction. In animals, both
decoder and decider functions are performed by the brain. In society, they tend to be
concentrated in political, scientific, legal and commercial institutions, although basic
forms of interpretation and decision are distributed throughout the whole of society, as
illustrated by market demand “deciding” which types of commodities are to be
produced, or voters deciding which political values should steer the country.
The next step consists in implementing the decision, that is, translating the
information generated by the decider into a concrete plan and executing the
corresponding actions This is the task of the effector function. This function is absent in
Miller's scheme, who proposes the encoder and output transducer functions instead.
The reason is that Miller's decomposition hinges on the linear sequence of information
entering the system, being processed and finally leaving again, not on the cyclical control
function, where the only function of information is to help select the right control
action. Although actions may be informative to other systems, they are not generally
intended to transmit information, but to compensate for perturbations. Therefore, there
is no a priori need for the encoding and output of information.
Of course, certain actions, such as speech, have a communicative intention. But this
intended transfer of information is subordinated to the more general purpose of
achieving the organism's goal. Typical goals of linguistic expression are to make another
person do something (a command or request), to get specific information (a question), to
get general feedback about one's own state (free expression), or to provide information
that might help the other and thus indirectly—through reciprocation, social ties or
kinship—help oneself. In such cases, Miller's encoder and output transducer are present
as specialized subfunctions of the more general effector function.
In animals, the effector function is performed by motor neurons that activate
muscles. In society, it is performed by “executives” of various ilk, including government
ministers, managers, engineers, drivers, and by automated systems that drive machines.
The nervous system also has its analogues of the metabolism's distributor, storage
and producer functions. Miller's channel and net function is responsible for the
communication of information between the various subsystems, such as sensor, decider
and effector. In the body, this function is performed by various nerves, in society by
communication channels such as mass media, telephone and post. Another destination
for information circulating in the system is memory, where information about previous
interactions is maintained to support future decisions. Unlike the storage function,
memory does not simply accumulate incoming data chunks—the way a computer disk
records bytes—, but maintains a selective, ever adapting trace of correlations between
various perceptions and actions so as to increase the effectiveness of decision-making
when similar situations are encountered later. The function responsible for creating this
network of associations is Miller's associator. In animals, associator and memory are
distributed over the neurons in the brain. In society, memory is supported by written
documents, libraries and databases. The associator function is performed among others
by scientists, scholars and archivists.
5. Evolutionary Development of the Superorganism
5.1. Evolution of cooperation
Although Darwinian theory provides a robust model of the evolution of individual
organisms, the evolution of societies of organisms does not fit so obviously into that
13 HEYLIGHEN
model. The main issue is the tension between individual and group selection. Darwinian
theory predicts that if an organism is to choose between behavior that will promote its
selfish interests, and behavior that benefits the group or society to which it belongs,
then in the long term only the selfish behavior tends to be selected. The reason is that
selfish individuals in an altruist group (“free riders”) profit more from altruist behavior
in others than the altruists themselves do. Therefore, altruist behavior tends to be
eliminated. Yet, animal and human groups provide plenty of examples of altruism, i.e.
behavior that contributes more to the fitness of others than to the fitness of the altruist
individual. Several explanations have been offered for this development (see e.g.
Campbell, 1983; Axelrod, 1984; Dawkins, 1989; Stewart, 1997). Since I have discussed
this issue in depth elsewhere (Heylighen, 1992b; Heylighen & Campbell, 1995), the
following paragraphs will merely sketch the main arguments.
The mechanism of group selection (groups of altruists being more likely to survive
than groups of selfish individuals) seems rather unsatisfactory because of the free rider
problem mentioned earlier. Yet, group selection has recently again become more popular
(Wilson & Sober, 1994), in part because of the observation that not all behaviors
beneficial to the group have a high cost to the altruist individual. The most popular
explanation, which is at the base of the sociobiological approach, is kin selection: the
principle that it is evolutionarily advantageous to be altruist towards individuals that
carry the same genes (“kin”). This mechanism seems sufficient to explain insect
societies, where all individuals are closely related to each other via their shared mother
(the “queen” of the nest). Another popular mechanism is reciprocal altruism, or “tit for
tat” (Axelrod, 1984), but this seems insufficient to explain cooperation in large societies
where there is often no opportunity for reciprocation.
To explain the emergence of human society, for me the most compelling mechanism
seems to be cultural conformism or “meme selfishness” (Campbell, 1982; Heylighen,
1992b; Heylighen & Campbell, 1995): if a cultural norm (“meme”) prescribing altruism
manages to spread over a group, conformist pressures will make it very difficult for
would-be “free riders” to deviate from that norm. Since different groups in general
follow different norms, there will be a cultural group selection promoting the more
altruist norms, which have the strongest benefit to the group as a whole. Stewart (1997)
has proposed a more general mechanism, where a “manager” (which may be a dominant
individual, a subgroup, or a cultural norm) takes control of a group for selfish purposes,
to appropriate part of the group's production, but undergoes selection for promoting
altruistic behavior within the group: groups whose manager does not efficiently
suppress cheating and free riding will be less productive, and thus their manager will be
less fit.
Whichever its precise origin, once a stable pattern of cooperation had been
established as a basis for human society, it quickly led to a division of labor. Division of
labor is based on the principle that if individuals specialize in carrying out particular
tasks, they can be more efficient. However, if an individual is exclusively busy
producing one particular type of commodity or service, then that individual will be
dependent on reciprocation by others for providing the other resources (s)he needs.
Therefore, division of labor can only evolve on a solid basis of cooperation. But once
the process has started, division of labor will spontaneously increase, driven by a
positive feedback mechanism, as illustrated by Gaines's (1994) computer simulation:
individuals who were successful in providing a particular type of service—because of
opportunity, competence or simply accident—will get more requests for that type of
service, and thus get the chance to develop a growing expertise in the domain. This in
turn will increase the demand for their specific service, stimulating them to further
specialize. For example, an individual who happens to live near to fruit trees may find it
easier to make a living by exchanging fruit for meat and other resources than by
THE GLOBAL SUPERORGANISM 14
participating in the communal hunting and gathering, and therefore will tend to invest
increasingly more time, attention and resources in developing fruit harvesting capacities.
The increasing division of labor entails an accompanying increase in mutual
dependence and therefore cooperativity. Cooperativity could be defined positively as
probability or dependability of cooperation, and negatively as lack of cheating or free
riding. This property of social systems is related to the concept of “social capital”. It is
implicit in the legal system, the organization of the economy, and the unwritten rules
which individuals follow in their interactions with others. For example, a society in
which no one trusts anyone and everybody is constantly trying to take advantage of the
others without doing anything in return, has low cooperativity. More concretely, the
failure of the market to produce economic growth after the fall of communism in the
former Soviet states may well be due to a lack of cooperativity in these societies:
without enforceable contracts or fair-play between economic actors, market transactions
will in general not bring mutual benefit.
The cooperativity of a society may be estimated by indicators such as level of
corruption or crime (negative), and tolerance or trust in other people (positive). These
have all significant correlations with the overall quality of life or development level of a
nation (Heylighen & Bernheim, 2000a), Third World and Eastern European countries
scoring in general much lower than North American and Western European societies.
This does not mean that there is no cooperation in primitive societies, but only that it
tends to be limited to small “in-groups” such as an extended family, village, or clan, with
a lack of care, distrust or even hostility towards outsiders (Campbell, 1982). In
conclusion we might hypothesize that the evolutionary development of a social system
is generally accompanied by an extension of cooperativity, and thus of “organismic
cohesion”.
5.2. Network evolution
Once there is division of labor, the main engine of evolution in a society will be neither
group selection nor individual selection, but what might be called bootstrapping or
network selection. This mechanism can perhaps be explained most simply in economic
terms. An individual or subgroup specialized in supplying a particular commodity can
be seen as a subsystem of the overall social system. In return for its “product”, the
subsystem receives payment (“reciprocation”), which it invests in resources
(components, raw materials, energy, people, information, infrastructure, etc.) necessary
for further production. The product defines the subsystem's output, the resources its
input. The subsystem's “function” within the larger whole is to process input into
output. The output of one subsystem is used as input by one or more other
subsystems, which in turn pass their output to a third line of systems, and so on. Thus
all subsystems are linked to each other via the input and output they exchange, together
forming a huge network of processes that feed each other, as illustrated in Fig. 1. If the
global system is autopoietic, then this network will be largely closed in on itself, and
exchange only a limited amount of raw materials and waste with the outside
environment. But before we can analyse the evolution of the global network, we must
examine the evolution of its individual links.
The input it receives and the output it supplies determine a system's relation with
its local environment. Systems will be variably adapted to that environment. For
example, a system that produces poor output, for which its “clients” are not willing to
pay much, or that cannot get the input it requires, will be ill-adapted. If different
systems compete to perform the same function, then those that are best adapted will
survive, while the others will be eliminated. Even if only one system is available to
perform a given function (e.g. a government agency or a commercial monopoly), there
will be external pressure on it to improve its efficacy. This means that if the system
15 HEYLIGHEN
undergoes variation, the more productive variants will be preferentially retained, while
the less productive ones will be pushed to undergo further variation. Thus, all
subsystems or components in the network of production processes are under constant
pressure to increase their productivity, that is, produce more or better output, while
requiring less or more easily available input.
This is the mechanism from the point of view of a single system: variation and
selection produce ever better fit to the constraints and opportunities of the given, local
environment. From the point of view of the entire network, all the components are
constantly adapting to each other's input and output. The network as a whole will adapt
to its overall input and output, but this will have only an indirect effect on its
subsystems. If we ignore this relatively small effect of the global environment, the
network's evolution can be seen as self-organization (Heylighen, in press, b), or
“bootstrapping” (Heylighen, in press, a): its co-evolving components are mutually
adapting, thus increasing the overall efficiency and coherence of the network, without
need for external selection. No component can afford to ignore this drive for mutual
improvement, since all components are dependent on the others for their input because
of their specialization, as discussed earlier. And no component can afford not to
specialize, because otherwise it would lose the competition with the more efficient
specialists.
The dynamic we sketched applies to all complex systems that can be analysed as a
network of interacting subsystems: markets, ecosystems, organisms, chemical reaction
networks, neural networks, etc. A number of authors in the complex adaptive systems
tradition have proposed formal models and computer simulations of different aspects of
such network evolution. Kauffman's (1993) models of autocatalytic chemical cycles, and
Holland's (1992) “bucket brigade algorithm” for rules evolving in a cognitive system are
worth mentioning in particular. The network systems that interest us here are those that
achieve organizational closure. As Kauffman proposes for chemical networks, it is
precisely the emergence of closure that characterizes the origin of “living systems” or
organisms. However, since the whole preceding argument assumes that society already
has achieved a basic form of closure, we will now focus on the concrete implications of
this network dynamic for our present, globalizing society.
5.3. Evolution of complexity
The increasing division of labor leads to a differentiation of the system into ever more
specialized subunits. The increasing dependency of these units on the rest of the
system, to compensate for the capacities they lost through specialization, leads to
increasing integration and cohesion. Differentiation and integration together produce
complexification (Heylighen, 1999) of the global system, and an ever greater
independence from the environment. The positive feedback relation between integration
and differentiation leads to the accelerated development of a complex organization out of
an aggregate of initially similar components. This is a metasystem transition: the
evolutionary emergence of a higher level of cybernetic organization (Turchin, 1977;
Heylighen & Campbell, 1995; Heylighen, 1995; see also Maynard Smith & Szathmáry,
1995). This overall dynamic is at the base of both the evolution of multicellular
organisms out of similar cells and of societies out of individuals. It is in a number of
respects similar to the phase transitions, such as crystallization, magnetization or
condensation, that characterize self-organizing systems in physics.
The ever accelerating differentiation and integration of societal components is
particularly striking in our present age of globalization. It becomes ever more difficult
for individuals, groups or countries not to participate in the global economic and
political system. If some manage to escape, such as a few primitive tribes still living in
the rain forest, it is largely because global society artificially tries to maintain their niche,
as a kind of relic of the past. At the same time, society becomes ever more complex with
THE GLOBAL SUPERORGANISM 16
ever more businesses, organizations and institutions providing ever more diverse goods
and services, interacting through ever more wide-spread networks of exchanges and
influences, and subjected to ever more intricate systems of standards and rules.
Increasing complexity is merely a side-effect of this dynamic, though (Heylighen,
1999): the underlying drive is increasing efficiency or productivity, which itself results
from the selective pressure for increasing control, and—most fundamentally—increasing
fitness. A component in a societal network will fit its environment better if it can
produce more of what is in demand, while being less dependent on the resources it needs
as input. In particular, a fit component should be able to provide a dependable output
under conditions of variable input, e.g. by using reserves, or shifting methods of
production to work with different resources. Thus the component should have good
control over its production. The eventual purveyor of the production does not care how
it was produced, or what resources or components were used, as long as quantity and
quality are satisfactory. This means that if the client can find equivalent products that,
because of a different production method, are sold more cheaply, he or she will switch
suppliers, possibly bypassing a whole chain of production processes. For example, in
Fig. 1, the component h may perform the same function for component l as j and k, and
therefore l might decide to “bypass” the longer process i j k l, in favor of the
shorter process i h l.
For a more concrete example, a company that requires a quick and constant news
feed in general does not care whether this information reaches it via written reports,
computer disks or telecommunication networks. If it can get the same information more
quickly and cheaply via electronic mail, it will stop its contract with the organization
that can only supply paper reports, thus bypassing a whole production chain that
transforms news into printouts, transports these printouts across continents and
delivers them on the company's doorstep. Instead of requiring a chain of three
organizations, one that collects news, one that prints documents and one that delivers
packages, the company will now rely on a single organization that directly enters and
transmits the news via its computer terminals.
This is an effective simplification of the organization, by the elimination of
processing stages that have become redundant. The more common development, though,
will be complexification by the creation of novel products or services. Every demand
that is not perfectly satisfied, or every resource that is not completely consumed,
determines a niche in which a new type of subsystem can potentially make its living.
For example, the huge amount of grape seeds left as waste after the production of wine
provides a valuable resource for the extraction of bioflavonoids that can help cure
circulatory problems. Without the enormous surplus in grape seeds, these valuable
medicines might have to be extracted from a more scarce source (e.g. leaves of the
Ginkgo tree), at a much higher cost. But this new grape seed processing subsystem will
itself create a demand for certain products or services (e.g. solvents and reactors to
extract the flavonoids from seeds), and supply some products waiting for a purveyor
(e.g. most people are not yet aware of the benefits of grape seed extracts). Thus, the
filling of a niche will itself create a number of new niches, providing opportunities for
new subsystems to evolve (Heylighen, 1999; Wilson, 1992).
5.4. Increasing efficiency in the social metabolism
Now that we have a general qualitative understanding of the evolution of a societal
system, we can look in more detail at the quantitative evolution of some of its
components. As noted, there is a universal selective pressure for subsystems to become
more efficient, that is, produce more or better output while using less or more readily
available input. This general tendency is easy to observe in society: employees, tools,
17 HEYLIGHEN
technologies and organizations become in general more efficient or productive as time
goes by. Perhaps the most spectacular illustration of the underlying technological
progress is Moore's Law, the observation that the speed of microprocessors doubles
every 18 months, while the price halves. This improvement results mainly from
miniaturization of the components, so that more (processing power) is achieved with
less (materials).
Buckminster Fuller (1969) called this on-going trend to progressively do more with
less “ephemeralization(see also Heylighen, 1997). Ephemeralization is at the basis of
all evolutionary progress (Heylighen & Bernheim, 2000b). The increasing productivity
means that less resources and labor are needed to produce the same amount of goods or
services. It leads to a steadily decreasing importance of physical production factors,
such as matter, energy, space and time, and a concomitantly increasing importance of
cybernetical factors, such as information, communication, intelligence, knowledge and
organization, which are necessary to efficiently regulate the processes. Ephemeralization
explains the stable or declining prices (corrected for inflation) of raw materials and
energy, in spite of largely non-renewable supplies. The decline is particularly evident if
the value of a resource is expressed as a percentage of the average income (Simon, 1995).
The more society develops, the less its members need to spend on physical resources
such as food, energy and materials, and the more they tend to spend on non-material
ones, such as information, education and entertainment.
The increasing efficiency of society's metabolism is particularly noticeable in the
distributor function: the transport of people, goods and services becomes ever faster and
less expensive. As a result, distances become ever less important. A few decades ago,
intercontinental travel was still a luxury that could be enjoyed by only a select few.
Nowadays, people in developed countries routinely fly to other continents for business
or leisure. Moreover, the globalization of trade means that increasing amount of goods
are transported across the globe by ships, planes or pipelines, at costs too low to make
it worthwhile producing the goods locally.
A general characteristic of ephemeralization is that more and more functions are
automated, that is, human effort is substituted by more efficient technological systems.
At first, only physical work was replaced by machines, but recently technology more
and more takes over tasks that require intellectual effort. In this domain, there is still a
very wide range of possible improvements. For example, apart from pipelines, transport
is still largely controlled by humans: truckers, drivers, navigators or pilots. In spite of
the great efficiency of modern shopping, having to drive through dense traffic, find a
parking space, enter the shop, collect goods, pay for them at the cashier, load them in
the car, and drive them back to your home is still a quite inefficient way to get goods
from the distributor to the consumer. At present, great advances are made in electronic
shopping so that goods can be ordered and paid automatically, via the computer
network. However, this still requires a truck being loaded with the goods, being driven
to your home, and being off-loaded there.
In densely populated urban areas, it would seem much more efficient to build an
automated distribution network that would connect all homes and warehouses, e.g. via
tunnels. Containers filled with goods by robots could then be transported on “conveyor
belts” and automatically switched at crossing points in order to reach their destination in
the basement of the house from which the order was made. Packaging, used products,
and other waste could similarly leave the house, to be transferred automatically to the
appropriate recycling installations. This would strongly reduce human effort, traffic
congestion, energy usage, and pollution. Of course, building such a network of tunnels
under all streets and buildings would demand a huge investment, but it would not be
intrinsically more difficult or costly than developing the roads, railways, sewage
systems and communication networks that are already there. In the densely populated
Netherlands, prototypes of such a distribution system are already being tested out.
THE GLOBAL SUPERORGANISM 18
Such an automated network of tunnels would be a real equivalent of the body's
circulatory system.
5.5. Reduced friction and it effects on control
The net result of the drive towards increasing efficiency is that matter, energy and
information are processed and transported ever more easily throughout the social
organism. This can be seen as the reduction of friction. Normally, objects are difficult to
move because friction creates an opposing force, which dissipates energy and thereby
slows down the movement, until standstill. Noise plays a similar role in information
transmission: over noisy channels, parts of the message get lost on the way.
The elimination of noise or friction is beneficial for desired processes. However, it
can be dangerous when there is a risk for unwanted processes. For example, ice as a
surface produces much less friction than earth or asphalt. That is why you can reach
higher speeds and sustain them for a longer time when skating than when running.
However, walking on ice is much more difficult and potentially dangerous than walking
on asphalt: once you start slipping there is very little to stop the movement getting out
of control.
In a similar way, technology smoothens or lubricates all the mechanisms of society.
Movements of matter and information run freely, with very little loss or resistance. But
this applies to unwanted movements too. It has become much easier to distribute
weapons, drugs or poisonous materials, like plutonium or pesticides. Once such a
movement gets started, it can develop very quickly, making it difficult to counteract it in
time. For example, an infectious disease can spread much more quickly in a world where
people travel frequently. Computer viruses are a more modern variant of the same
principle: the easier and faster the exchange of information between computers, the more
quickly viruses can spread.
The reduction of friction is particularly dangerous for such self-reinforcing
processes. The typical example of such a positive feedback process is speculation on
the stock exchange, where buying triggers more buying (causing a “boom”) and selling
triggers more selling (causing a “bust”). For that reason, a number of processes in our
present low-friction society have become intrinsically less stable, with a higher risk for
catastrophic outcomes. This has led to proposals to artificially increase friction, such as
restrictions on computerized trade or the imposition of a “Tobin” tax on international
movements of capital.
On the other hand, reduced friction also improves the efficiency of the negative
feedback cycles that characterize regulation. It basically leads to greater amplification in
the control loop: smaller error signals lead to appropriate reactions more quickly and
more easily, correcting the deviation before it has had the chance to grow. Another
buying and selling example may illustrate the principle: the negative feedback between
supply and demand in a normal market will become more effective when friction is
reduced; prices will come down more quickly when supply increases or demand
decreases, and an increase in price will more readily trigger an increase in production to
fulfil the unsatisfied demand. Thus, it should take less time for the market to come back
to equilibrium after a perturbation. Although the changes in the external situation are so
frequent, large and complex that a market will never actually reach equilibrium, reduced
friction should at least help it to remain closer to this theoretical state where all labor
and resources are optimally allocated to the different demands. This phenomenon may
well be at the base of the low inflation and surprisingly stable growth characterizing the
“new economy” which is thought to have been ushered in by global communication
networks.
In the longer term, gains in stability due to low-friction, negative feedback are likely
to be maintained or amplified, while the loss of stability due to low-friction, positive
19 HEYLIGHEN
feedbacks will increase the selective pressure for evolving new regulatory mechanisms
For example, the appearance of a circulatory system in multicellular organisms strongly
reduced friction, making it much easier for nutrients and hormonal signals to travel
through the body. This gave the organism better overall control over its dispersed
tissues and organs, allowing it to become larger and more differentiated. The
disadvantage was that microbes too could travel more efficiently. This was compensated
by the evolution of white blood cells, allowing the immune functions to keep up with
the flow. Similarly, the spread of computer viruses and other threats on the computer
network might be stemmed by an artificial immune system, inspired by the mechanisms
(such as self-other distinction) used by white blood cells to recognize intruders
(Somayaji, Hofmeyr & Forrest, 1998).
More generally, the easier flow of goods, people and money in a globalizing society
has led to new dangers, such as international crime syndicates, money laundering, and
competition between countries that erodes wages, governments' capacities to collect
taxes, and working standards. These can only be solved by the creation of global control
mechanisms, such as transnational crime fighting agencies, rules for international bank
transfers, and agreements on minimum social standards (e.g. restricting the import of
goods made by child labor). The need to monitor and control such problems is at the
base of the increasing importance of global institutions, such as WTO, IMF, World
Bank, WHO, and UN. Thus, if short-term catastrophes can be avoided, reduction of
friction will further enhance the robustness of societal autopoiesis.
A more subtle effect of reduced friction is the lengthening of cause-and-effect
chains. Imagine a row of billiard balls, each ball a short distance from the next one. If
you hit the first ball with your cue (cause), it will hit the second ball (effect), which will
itself hit the third ball (second effect), and so on. Because of friction, energy is lost, and
each next ball will move more slowly than the previous one, until the point where the
ball stops before it has reached the next one in line: the causal chain has broken. With
lower friction, the chain will be longer.
This same mechanism applies to our low friction society. In earlier periods, an
event happening in one country would have little or no effect on events happening in
another country. The chain of effects would have died down long before it would have
reached the national border. Nowadays, the world as a whole has become
interdependent. A poor harvest in one country will affect the production in another
country, which will affect the stock market in a third country, which will affect the
employment in yet another country, and so on. Each of these events will not just have
one effect, but many, each of which will again influence many other events. The on-
going decrease of friction makes these interactions more numerous and more complex.
Because of this ever greater interdependency, subsystems of the superorganism
need to keep informed about an increasing number of events taking place in other
subsystems. Longer causal chains mean that many more potential causes and effects
need to be monitored, resulting in the concurrent dangers of unpreparedness—when
relevant information is not available—and information overload—when the system is
incapable to effectively interpret the available information. This augments the selective
pressure to develop a more sophisticated capacity for the gathering and processing of
information, and therefore to increase the efficiency of the superorganism's nervous
system.
5.6. Organizational restructuring
Yet another phenomenon affected by reduced friction is the segmentation of the
superorganism into cooperating/competing subsystems. First, friction reduction leads to
increased “liquidity” in the markets: capital is more easily available and can more
quickly flow from one investment into another one. This makes it easier to start up new
ventures, providing novel products and services. This will accelerate the overall trend of
THE GLOBAL SUPERORGANISM 20
differentiation and innovation, and the emergence of an ever greater diversity of
specialized suppliers.
Second, it leads to increased competition: in earlier periods, competition was largely
constrained by geographical proximity. A producer or service provider residing in a far-
away region could not compete with a local supplier, because of the extra cost of
transport of the good or service. The essence of economic globalization is that distance
nowadays contributes very little to cost, and therefore competition becomes global.
This means that for a given commodity far fewer providers will be able to survive. For
domains where size confers advantage, this leads to mergers and acquisitions among
firms that provide the same type of commodities. In some sectors (e.g. operating
systems for computers), this may even lead to the formation of global monopolies.
The concurrent reduction of diversity will be compensated by another trend,
though: outsourcing. To explain this, we must understand why organizations arise in the
first place. An organization can be defined as a system of individuals with diverse skills
and specializations who cooperate for a common purpose. In a pure market logic, it
might seem strange that these individuals collaborate in a rigid organizational structure,
instead of flexibly providing their services to whoever is the highest bidder at that
particular moment. Williamson (1975) and other economists have developed a theory
according to which hierarchical organizations arise in order to minimize transaction
costs. When two components in the societal network engage in the exchange of goods or
services, this costs them effort in addition to the effort needed to produce the goods.
They need to explore the market, compare different suppliers, compete with others,
exchange information about the goods or services they provide or require, build up a
relation of trust, sign a contract, establish a channel of exchange, etc. These costs can be
minimized by entering into a fixed arrangement, so that the whole process does not need
to be started anew each time another exchange is to be initiated. Thus, organizations
reduce transaction costs.
Reduced friction (better communication, information processing, more efficient
exchanges, etc.) together with increased cooperativity produces a marked reduction in
transaction costs. This means that there will be less need to group a variety of services
into a single organization. Different subsystems of the larger organization can become
more autonomous, exchanging products and services via different flexible channels rather
than through a rigid structure. This produces many benefits. Let us illustrate this by the
example of a typical organization: a hospital (cf. Drucker, 1993).
The main function of the hospital is curing patients. However, in order to achieve
this purpose, it must perform a number of supporting functions, such as maintaining the
infrastructure, providing meals to patients, staff and visitors, cleaning the rooms, doing
the administration of the bills, etc. Since these various tasks have little to do with the
specialized goal of curing patients, the management of the hospital will have little time
or interest in overseeing, improving or developing these supporting functions. Yet, the
fact that these tasks are immediately needed for the main function means that they tend
to be performed by subsystems of the same organization. The hospital's cooks,
technicians and cleaners are employees of the hospital, just like the doctors and nurses.
The reduction of transaction costs means that now a number of these functions can
be performed by independent organizations. As long as these organizations have an
efficient and reliable communication channel with the hospital, and therefore are able to
react quickly and adequately to every demand, they do not need to be physically or
organizationally subordinated to the hospital. For example, the cleaning of the hospital
rooms can be contracted-out to a specialized cleaning firm. Such a firm can perform this
same function for a multitude of hospitals and other organizations, and thereby profit
from an economy of scale. For example, it can have a large pool of cleaners and
specialized products and machines at its disposal, so that it can quickly respond to
special demands, without having to hire more personnel or to order additional tools.
21 HEYLIGHEN
Because it is specialized in this one function of cleaning buildings, it can also devote
more of its resources to research and development of more efficient cleaning methods
than the hospital, thus optimizing its resources. Finally, it can better motivate its
personnel to perform their cleaning task, since this is the basic mission of the
organization, rather than a mere supporting activity that is low on the management's list
of priorities.
Such transfer of a particular function to an external organization is called
“outsourcing”. A similar scenario can be developed for other supporting activities. For
example, many organizations now use specialized firms to do their administration and
accounting. This is facilitated by modern communication technologies. In principle, a
firm selling some product would only need to enter the number of items sold and the
money received into a computer network, and, using those data, administration of the
accounts could be done by a specialized organization located anywhere in the world.
The reason why outsourcing increases efficiency is the cybernetic principle of
functional autonomy. In a complex control system, consisting of many interrelated
subsystems performing a variety of tasks, the higher order system cannot oversee the
activities of all of its subsystems, since, according to Ashby's (1964) law of requisite
variety, its own variety (complexity) would need to be at least as great as the variety of
all the subsystems combined. In order to minimize the complexity of its own decision-
making, the control system should as much as possible delegate decision-making to the
subsystems, that is, make them autonomously responsible for carrying out their
function. The only thing that needs to be controlled is whether the subsystems carry out
their function; how they carry it out is up to them. This defines functional autonomy.
The same principle underlies efficient organization: the hospital management should
not be concerned with the precise way the rooms are cleaned; it should only ensure that
they are clean. Therefore, there is no reason for the hospital to keep tight control over
the cleaning department: this would only burden its regulatory abilities. The only
control it needs is being able to tell the cleaners which rooms should be cleaned and up
to what standard of cleanliness. Therefore, it can delegate the implementation of cleaning
procedures to an outside organization.
It is only when the subsystems are incapable of making the right decisions that the
higher-order system must intervene and tell them what to do. This means that the more
autonomous subsystems are, that is, the more control they have over the way they
carry out their function, the less hierarchical supervision they need. This is the essence
of Aulin's (1982) law of requisite hierarchy: the required number of hierarchical levels
decreases with increasing capacity for control in the subsystems. Since friction
decreases and overall efficiency and control increase in all components of society, this
means that present organizations and society at large can function with a strongly
reduced number of hierarchical levels, thus making perception and action much more
efficient (Heylighen & Campbell, 1995). This explains the present trend towards the
flattening of hierarchies. The towering pyramids of hierarchical levels in traditional
bureaucracies merely reflected the poor regulatory ability of the individuals and
subsystems in such a bureaucracy (and the tendency to institutionalize an intricate
pecking order).
Hierarchies not only tend to flatten, but to turn into heterarchies, that is, networks
of mutual influence without subordination. Let us go back to the hospital example.
Initially, the cleaning department is subordinated to the hospital management. After
outsourcing, the specialized cleaning firm may now service many different hospitals,
without being subordinated to any one of them. Neither are the hospitals subordinated
to the cleaning firm: their relation is one of reciprocity, or exchange among equals. The
only system to which these various organizations remain subordinated is society as a
whole: only the superorganism can exert general control on the transactions between its
THE GLOBAL SUPERORGANISM 22
subsystems (e.g. ensuring fairness and honesty, and precluding the exchange of drugs or
nuclear weapons).
In conclusion, the improvement in communication, processing and control in all
components of global society has a far-reaching impact on the structure of that society:
the number of organizations performing the same function tends to decrease because of
mergers and competitive exclusion, whereas the number of organizations performing
different functions tends to increase, because of outsourcing, innovation, specialization
and the discovery of new niches. At the same time, hierarchies are flattened or turned
into heterarchies, and organizations become more autonomous in how they perform
their functions, while becoming more dependent on society as a whole to determine
which functions are in demand. Also, organizations become less dependent on specific
individuals or geographic regions, and more defined by their activity or function.
Thus, society increasingly resembles a complex organism, with its specialized cells,
organs and tissues, that are functionally autonomous, but tightly integrated in a global,
self-organizing network of mutually feeding processes. This is in clear contrast with the
more traditional view of society as a bunch of essentially interchangeable individuals,
groups and subgroups, separated by geographic distance or historic accident, that are
jostling for power, while making temporary alliances. An important remaining difference
is that cells in an organism tend to specialize early and irreversibly, whereas individuals
and organizations in society remain able to switch from one function to another as
demand or opportunities change, keeping the overall system very flexible.
6. Development of the Global Brain
6.1. Automation of nervous system functions
Whereas the previous section mainly discussed the development of the components and
metabolic functions of the social superorganism, the present section will focus on the
concomitant evolution of its nervous system, that is to say, the specialized subsystem
responsible for the processing of information. Like the other functions, this subsystem
becomes more efficient through automatization, that is, the use of artefacts such as
archives, cables, and computers to extend the capabilities of the human nervous system
for the storage, transmission and processing of information. Just like the increasing
efficiency of the metabolic functions of production and distribution has led to a
globalization of the economy, the automatization of information-processing is leading to
a globalization of humanity's cognitive and decision-making mechanisms. The most
direct support for this global nervous system is the Internet, the network that connects
most computers on this planet. The following discussion will focus on the present and
future development of this network, arguing that it forms an embryonic “global brain”
for the social superorganism.
The issue here is not the specific implementation of the Internet: most of its
functions could probably be implemented in other media and communication protocols,
such as fax, cellular phone, digital TV or rival types of computer networks (such as the
no longer existing BITNET or CompuServe systems). The Internet's main strength is its
overall flexibility and the fact that it has very quickly become a standard. This made it
attractive to integrate competing methods of information exchange into the Internet so as
to make them all accessible through a single interface. As a result, the historical accidents
which created particular standards for particular types of communication are becoming
less and less important in shaping the overall organization of the global nervous system.
In the society as superorganism metaphor, telecommunication channels play the
role of nerves, transmitting signals between the different sensors and effectors (Turchin,
1977). In more advanced organisms, the nerves develop a complex mesh of
interconnections, the brain, where sets of incoming data are integrated and processed.
23 HEYLIGHEN
After the advent in the 19th century of one-to-one media, like telegraph and telephone,
and in the first half of the 20th century of one-to-many media, like radio and TV, the
last decades in particular have been characterized by the explosive development of
many-to-many communication networks. This has led to the metaphor of the world-
wide computer network as a ‘global brain’ (Russell, 1995; Mayer-Kress & Barczys,
1995; Heylighen & Bollen, 1996).
In organisms, the phylogenetic evolution of the nervous system is characterized by
a series of metasystem transitions producing successive levels of complexity or control
(Turchin, 1977; Heylighen, 1995). The level where sensors are linked one-to-one to
effectors by neural pathways or reflex arcs is called the level of simple reflexes. It is
only on the next level of complex reflexes, where neural pathways are interconnected
according to a fixed program, that we start recognizing a rudimentary brain. I will now
argue that the present global computer network is on the verge of undergoing similar
transitions to the subsequent levels of learning, characterized by the automatic
adaptation of connections, and thinking. Such transitions would dramatically increase
the network’s power, intelligence and overall usefulness.
The present global network already automatizes Miller's functions of channel and
net (distribution of information), memory (storage of data), sensor (collection of data,
e.g. through web cameras, keyboards, counters, etc.), effector (use of the net to activate
processes from a distance, e.g. remotely controlled robot arms, electronic ordering of
goods to be shipped, etc.), and decoder (processing of data to make them more
meaningful, e.g. “mining” of client ordering data in order to find relevant patterns) (cf.
Heylighen, 1999b; Heylighen & Bollen, 1996). Apart from memory and channel-and-
net, most of these functions are still supported only marginally on the network—at
least in comparison to their presence outside the electronic medium. However, it should
be clear that the large-scale migration of these functions to the Internet is only a
question of time, as there are plenty of benefits and no apparent technical obstacles to
the implementation of more sensing, decoding and effecting devices.
6.2. Learning and thinking
Less obvious is the automation of the functions of associator and decider, which
correspond to the higher cognitive functions that we normally associate with
intelligence. Yet, recent work by my colleagues and me (e.g. Heylighen, 1999b; Bollen &
Heylighen, 1998, 1996; Goertzel, 1999) provides evidence that such forms of high-level,
creative intelligence can be directly supported by the network, without need for human
supervision. This does not even require sophisticated artificial intelligence programs: it
suffices to support the self-organization of the information streams on the network,
thus giving rise to a collective intelligence that is much more than the sum of the
individual intelligences of the network's users (Heylighen, 1999b). The present paper
does not intend to discuss the technical details of a possible implementation, as these
can be found elsewhere. It will suffice to outline the general principles, thus showing
how the increase of efficiency by automatization that accompanies the self-organization
of the superorganism extends to its highest cognitive functions.
The main function of the associator is for the network to learn new associations
between data or concepts. The World-Wide Web, through its distributed hypermedia
architecture (Heylighen & Bollen, 1996), already connects associated documents by a
mesh of links. Until now, the creation of the links is done manually, by the authors of
the documents who decide which other documents are relevant to their text. Given the
hundreds of millions of potentially relevant documents that are available on the web,
this process is very inefficient, and will catch only a fraction of what is really relevant.
This makes it very difficult for a user browsing the web to find the most relevant topics
on any given subject. Search engines that return documents containing particular
THE GLOBAL SUPERORGANISM 24
keywords only partially alleviate this problem, as typical keywords will return far too
many “hits”, submerging the most interesting documents in noise, while many relevant
documents remain elusive because they use different keywords.
In the brain, learning is based on the rule of Hebb for neural networks: neurons that
are activated subsequently within a short time interval become more strongly connected.
The equivalent of neurons in the web are documents or pages, and the equivalent of
subsequent activation is being read or used by the same individual within a short time
interval. The more people attentively read document B shortly after they have
attentively consulted document A, the stronger the link between A and B should
become. For linked documents that are rarely used together, the link should weaken and
may eventually disappear. The more people “surf” the web, moving from page to page
by following links or by doing subsequent searches, the faster the web would be able to
create good associations, creating strong links between documents that most users
would consider mutually relevant. Since every page is indirectly linked to every other
page in the web, this means that strongly related pages will sooner of later establish
direct links, however far apart they initially were in web space. Such methods would
transform the web from a huge collection of weakly connected documents into a
coherent associative network, similar to the neural network that constitutes our brain
(Bollen & Heylighen, 1996, 1998).
Given such associations learned from users, the next function of the associator is to
use these links in order to solve problems or answer queries. This process may be called
“thinking”. It can be implemented on the Web by the automation of another neural
mechanism: spreading activation. If in the brain certain concepts (or the corresponding
assemblies of neurons) are activated—because of perception or previous thought about
the issue—then this activation will spread to neighboring concepts, following the links
in proportion to their associative strength. This will activate new concepts, which in
turn may activate further related concepts, and so on, sustaining a continuous train of
thought. Spreading activation can be implemented on the web through a software agent,
a program that takes an input of concepts defining the problem, locates the pages most
relevant to these concepts, and then explores the links from these concepts, activating
neighboring pages in proportion to the initial activation and the strength of the
intervening links.
The advantage of this approach is that a problem does not need to be defined by
precise keywords, since the activation will automatically spread to pages that contain
different keywords but are still closely related to the initial problem formulation. For
example, in our prototype learning web (Bollen & Heylighen, 1998), the activation of
the concepts “building”, “work” and “paper” would automatically bring forward
“office” as most relevant concept. This is much closer to the way our brain solves
problems by intuition and association than to the way traditional artificial intelligence
programs solve problems by logical deduction.
6.3. The decider function
The last critical function that needs to be automated is the decider. Given the
information about the situation produced by the sensor and decoder, and the overall
goals or values of the organism, and using the process of thinking, the decider should be
able to select the most adequate sequence of actions that would lead from the initial
situation to the goal. We have argued that for the social superorganism, the “goal” or
value system emerges from the aggregate demand by the public. The market is a system
of transactions that manages to translate this fuzzily defined “demand” variable together
with the supply into a concrete action signal: the price.
Obviously, network technology can support the market mechanism in determining
the optimal price for a commodity. Software agents have been developed to
25 HEYLIGHEN
automatically compare prices for any given item in different on-line stores and thus find
the best deal for the consumer. This forces suppliers to quickly align their prices
downward for goods that are plentiful. On the other hand, automatic auctioning systems
have been created where consumers from all over the world can bid for desirable goods.
This forces the price up for scarce goods where the demand is higher than the supply.
The two mechanisms together, one surveying supply and one surveying demand, can
accelerate the adjustment of prices so as to optimally reflect the balance between
supply and demand. The easy accessibility over the web of prices for the most diverse
goods and services will stimulate suppliers to invest in those commodities where the
difference between demand and supply is highest.
Not all values can be expressed in terms of price, though. Many valuable things
(friendship, ideas, beauty spots, ...) are free, but still it may be difficult to find them, or
to decide which out of several attractive options to choose. But here too the web can
support the decision process. The mechanism can be illustrated most simply with
documents that offer information. Suppose you find a number of pages that suggest
different ways to treat a cold. Which should you take most seriously? In society, this
problem is normally solved by relying on authority: some sources of information (e.g.
your doctor, or the medical encyclopedia) are considered more trustworthy than others
(e.g. your neigbor, or a family magazine). On the web, where the supply of information
is huge, extremely diverse in origin, and ever changing, traditional ways of establishing
authority (academic degrees, reputation, etc.) are not very efficient. Yet, the linking
pattern of the web itself can be used to automatically determine authority.
The main idea is that a document or website is considered authoritative if it is
referred to by other pages that are authoritative. Although this definition may seem
circular, it can be implemented by a recursive algorithm, which uses a number of
iterations to determine the overall authority of a page. Two variations of this approach
have been developed: PageRank (Brin & Page, 1998) calculates overall authority,
whereas HITS (Kleinberg, 1998) determines authority within a specific problem context
(e.g. all documents about colds and respiratory diseases). Both seem to work
surprisingly well in practice, and are likely to work even better if the linking pattern in
the web would automatically learn from its usage, as discussed earlier.
In principle, similar algorithms could determine the “authority” not just of pages or
sites but more generally of ideas, people, services or organizations to whom reference is
made via the web. As such, they potentially propose an automated means of
determining the value of something to its users, without requiring people to offer money
for it. However, the disadvantage is that some kind of average value is established for
the group, which may be very different from the specific value for a particular user. For
example, your taste in music may be very different from the one of the average person,
and therefore you would find little value in the list of most popular records. However,
you would be interested to hear the recommendations from people whose taste is
similar to yours. Such personalized recommendations too can be automated, by using
the set of techniques known as “collaborative filtering” (Shardanand & Maes, 1995;
Heylighen, 1999b). The basic principle is that the system records the personal
preferences of a great variety of people, and then uses the preference profiles that are
similar to yours to determine options (e.g. music records, web pages, movies) that you
are likely to appreciate as well. Such a system could even be used to help create
personal relations, under the simple assumption that people who have tastes and friends
similar to you are likely to get on with you as well.
Merely determining which products or services are valuable is a rather trivial aspect
of the decider function. The core of that function is to use data about the perceived
situation and about goals and values in order to infer an adequate sequence of actions.
This is the most difficult part to implement on the network, although some examples
can already be found. For example, a search engine like Google (www.google.com) could
THE GLOBAL SUPERORGANISM 26
be used to enter a number of keywords describing symptoms of a problem. It would
find documents that not only discuss these symptoms, but that have a high value
according to the PageRank algorithm. Thus, the returned documents would have a high
probability to contain a reliable solution to the problem—if such a solution is known,
and if the problem is accurately described, using the right keywords. The requirement of
accurate keyword description could be relaxed if an algorithm based on spreading
activation, as described earlier, would be included into the system.
The problem-solving system would become even more intelligent if the web would
be organized in the form of a semantic network, where pages and sections of pages are
classified and linked according to an ontology of concept types and link types, as
conceived in the new XML standard for the web. This would allow the system to make
logical inferences, deducing aspects of the problem situation that were not entered by
the user (Heylighen & Bollen, 1996; Heylighen, in press, a). For example, if a user
would describe the disease symptoms of his poodle, the system would automatically
infer that since poodles are dogs, it should look for the same symptoms in documents
that describe dog diseases, even if they do not mention poodles.
Ideally, the decider function on the web would connect directly to the effector
function, so that actions are not only chosen automatically, but executed as well. For
example, a shopping agent might not only gather data about available products and
available prices in order to select the “best buy” option, but might actually order the
chosen product.
6.4. Integrating individuals into the global brain
Metabolically, most individuals are already strongly integrated into the superorganism:
they are wholly dependent on society for shelter, energy, food, water, health and waste
disposal. Even the birth of a new human being nowadays is difficult to imagine without
a complicated socio-technical infrastructure of hospitals, doctors, nurses and machinery.
Intellectually too, individuals get most of their information, knowledge and values from
the surrounding social system. However, the latter information exchanges between
individual and superorganism are relatively slow and inefficient, at least compared to the
speed of the individual's own nervous system. In contrast, the time needed by an
individual to get food from the superorganism (e.g. by visiting a fast-food restaurant) is
not longer than the time needed for that food to be digested by the individual's own
metabolism.
This relative inefficiency of information transfer is likely to vanish in the near
future. In order to use the cognitive power offered by the global brain effectively, the
barrier between internal and external brain should be minimized. The explosive spread of
wireless communication, portable devices and, soon, “ubiquitous computing” (Weiser,
1993; Gellersen, 1999) heralds the constant availability of network connections,
whatever an individual's location. An emerging research domain is that of “wearable
computers” (Starner et al., 1997): small but powerful processors which remain available
continuously, for example integrated into clothing. Users could wear special glasses
which allow them to see the information from the computer superimposed on a normal
view of the surroundings. Thus, the computer can constantly provide them with
information about the environment, and warn them e.g. when an important message
arrives.
Such computers would use sophisticated multimedia interfaces. This would allow
them to harness the full bandwidth of 3-dimensional audio, visual and tactile perception
in order to communicate information to the user's brain. The complementary tech-
nologies of recognition of speech, gestures, facial expressions, or even emotional states,
make the input of information by the user much easier. For example, the wearable
computers would be connected to a small microphone, in which the user can speak, and
27 HEYLIGHEN
a glove or sophisticated trackball kept in a pocket, with which the user can steer a
cursor or manipulate virtual objects.
Even more direct communication between the human brain and the Web can be
conceived. First, there have already been experiments (Wolpaw et al., 1991) in which
people steer a cursor on a computer screen simply by thinking about it: their brain
waves associated with particular thoughts (such as “up”, “down”, “left” or “right”) are
registered by sensors and interpreted by neural network software, which passes its
interpretation on to the computer interface in the form of a command, which is then
executed. Research is also being done on neural interfaces, providing a direct connection
between nerves and computer (Knapp & Lusted, 1992).
If such direct brain-computer interfaces would become more sophisticated, it really
would suffice for an individual to think about a problem in order to see recommended
solutions pop-up on the screen (or spoken into an earplug), and the corresponding
actions executed in reality. For example, it might suffice for you to think “It's time to go
home” to have a cab automatically directed to the place where you are, pick you up, and
bring you to your home address, while being paid from your electronic account, your
software agent having made sure to select the cab that would provide the service most
quickly and inexpensively. Thus, the boundary between individual cognitive processes
and processes inside the global brain would be minimized, integrating the individual into
the superorganism not only physically, but mentally (Heylighen & Bollen, 1996).
7. Issues Raised by the Superorganism Model
As noted in the introduction, the view of society as an organism has elicited many
worries, objections and general questions that need to be addressed. A discussion of
these recurring issues will allow us to argue that global integration is not only likely, but
moreover desirable, and, in fact, inescapable.
7.1. Totalitarian control, collectivism or freedom?
The most common objection to a superorganism model is that people tend to interpret it
as a thinly disguised way of promoting a totalitarian, collectivist system. Especially the
use of words such as “control” and “collective” evokes immediate associations with
Stalinism and the brutal oppression of individual liberties. These negative connotations
may be understandable, but they are wholly misdirected. The societal evolution I have
sketched is mostly an extrapolation of existing trends, and these show an on-going
increase in freedom, individualism, democracy and decentralization rather than a
decrease (cf. Heylighen & Bernheim, 2000a). These trends can be explained
straightforwardly by the postulated mechanisms of differentiation, which opens ever
more possibilities for an individual to choose a role, education, or occupation, of reduced
friction, which increases the general freedom of movement, of expression, and of
consumption, and of increasing autonomy, which reduces the need to tightly control or
monitor an individual's activities.
The complementary mechanism of integration could be seen as a source of new
constraints or limitations, but these are likely to restrict the freedom of powerful
individuals—such as a Stalin-like dictator or a robber baron—and organizations to abuse
the system for their own ends, rather than the freedom of ordinary people to realize
their individual ambitions. Global integration means an increasing mutual dependency of
various organizations, and therefore an increasing difficulty for any one organization to
dominate the others. This is understandably resented by those who have most power to
lose, but should be welcomed by the less powerful. For example, this anticipated loss of
THE GLOBAL SUPERORGANISM 28
power may explain the common distrust of global institutions, such as the United
Nations, in the presently most powerful nation, the USA.
Historically, totalitarian regimes, such as Hitler's Germany, Stalin's Soviet Union,
or Saddam Hussain's Iraq, were the result of an individual or select group's desire to gain
and maintain power and privileges at the expense of the larger population, by
suppressing their freedom to question those privileges. The underlying mechanism is
simply individual selfishness augmented by social power structures (cf. Heylighen &
Campbell, 1995). There is nothing particularly modern about such social systems: apart
from more sophisticated methods for propaganda and control, the same type of
ruthless, centralized organization can be found in the kingdoms and empires of
Antiquity and the period before the French and American revolutions.
Insofar that totalitarian societies were based on an ideological or political system,
such as Soviet communism, this system was very different from the self-organizing,
cybernetic, “organism-like” system that this paper proposes. As discussed by the
cybernetician and Soviet dissident Valentin Turchin (1981), the Soviet system lacked
the most crucial component of cybernetic control: feedback. Instead of a distributed
feedback loop, constantly adapting to the changing circumstances, the Soviet economy
was based on a rigid, mechanical, top-down command structure, with little regard for the
effect of those commands in the real world. This led to the well-known “calculation
problem”, where the central planning agency would find it impossible to determine
exactly how many shoes would need to be produced to satisfy the needs of a given
population. The resulting economic inefficiency contributed to the eventual collapse of
the Soviet system.
The emphasis of the present paper on distributed control is not meant to imply
that centralization is necessarily bad: concentrating control knowledge in a separate
subsystem has a number of benefits (Heylighen, 1995). The main advantage is that by
giving the control system an explicit, physical form it becomes more open to scrutiny
and improvement. For example, the control of a cell is centralized in the DNA in its
nucleus. This makes it easy for evolution to try out new forms of organization by
making small changes in the DNA. A cell where control would be distributed over the
whole of the participating molecules, as assumed by autocatalytic cycle models for the
origin of life (Kauffman, 1993), might seem more flexible, but appears less likely to
evolve a complex organization.
Similarly, there is a role in society for some form of government: although the
market mechanism can solve many problems, it has certain intrinsic shortcomings (e.g.
speculative bubbles or disregard for “externalities”, such as pollution) which cannot be
corrected by replacing its components (e.g. producers or consumers). Since the market
reacts as a whole, it can only be steered by an outside system (the government) that
imposes constraints (such as regulations, taxes or subsidies) on all participating actors
(cf. Heylighen, 1997). The advantage of such a separate system is that if it does not
function well, it can be replaced by a different one (as when an unsuccessful government
is voted out), unlike the global market.
The absence of centralization is at the base of another nightmare vision associated
with the superorganism model: the true “collective”, where everybody thinks the same
and does the same, and where there is no room for individual initiative or decision-
making. This vision is more inspired by insect societies, such as beehives or ant nests,
than by existing political systems. Its most popular recent instantiation is the “Borg”,
the race of cyborgs imagined by the creators of the science fiction series “Star Trek”.
Again, from a cybernetic point of view a Borg-like organization would be most
inefficient. As noted earlier, Ashby's and Aulin's laws imply that the global organism, in
order to maximize its own control over its environment and its chances for survival,
should maximize the capacity for autonomous decision-making among its components.
Moreover, it should maximize the diversity or variety of the strategies used by its
29 HEYLIGHEN
components. This can only be achieved by stimulating individuals to develop
themselves freely (cf. Heylighen, 1992), and as much as possible choose their own path,
rather than merely conform to the collective point of view.
Even for ants, it can be shown that the colony will be most efficient in finding food
if individual ants do not merely follow the paths laid down by their fellow ants, but
regularly deviate and create a path of their own (Heylighen, 1999b). If people
understandably dislike the analogy between human societies and insect societies, it is
not so much because insect societies are organized in an intrinsically more totalitarian or
collectivist manner, but because insects are simply very dumb, characterless creatures
compared to humans. An isolated insect, whose behavior is governed by a few simple
and rigid rules, is not intrinsically more free or more creative than an insect living in a
colony. If you would have to choose, would you rather be a (social) termite, or its
individually living cousin, a cockroach? Would you rather be a “collectivist” bee, or an
“individualist” fly? Neither of these alternatives seems particularly attractive.
7.2. Dropouts, conflicts and shared values
Another recurring issue brought forth by the superorganism model is whether all
individuals and groups will agree to become part of such a global system. In principle,
an individual, nation or group of nations could refuse to be “integrated” into the
transnational social system.
On the individual level, the phenomenon has always existed of tramps, hermits or
adventurers who were in practice living “outside” of society. This phenomenon has
always been marginal and is likely to remain so. In principle, there is no reason why the
social organism should not tolerate the existence of such individuals or small groups (e.g.
communes or isolated monasteries) that do not really contribute to society and do not
follow its rules. The only condition will be that such outsiders should not harm or
endanger those inside, as may be the case for criminals or people with mental
disturbances. In practice, though, it seems unlikely that many people would choose that
option. The benefits of belonging to society, such as security, comfort, companionship,
knowledge, medical support, etc. are so great that it will be very difficult to resist their
lure. These benefits are likely only to increase as the superorganism further develops.
On the other hand, the common idea that what you lose in comfort by dropping
out, you gain in freedom, is based on a misunderstanding of what “freedom” means.
Without technology and social support systems, life is basically a struggle for survival,
where most energy and time must be directed towards finding the necessary food and
shelter. By removing these requirements, society has given us the real freedom of doing
what we want, where we want it, and (most of the time) when we want it, without
having to worry whether we will be able to survive. Especially technology, such as the
transport and communication systems, has enormously expanded our freedom of
movement and of communication. The more the superorganism increases its
differentiation and integration, the more options we will have to choose our occupation,
or go wherever we want whenever we want.
Of course, belonging to an encompassing system does impose certain constraints,
aimed at maximizing the synergy between interacting components and minimizing
mutual obstruction. However, such constraints do not generally reduce overall freedom.
This may be illustrated by the traffic code. Being able to travel with your car wherever
you want is a great freedom, which people from previous ages could hardly even
imagine. However, for many people to drive safely and with a minimum of interference
on the same roads, traffic rules must be followed. Though some of these rules, such as
speed limits, are self-evident ways to avoid danger to self and others, others may seem
largely arbitrary. For example, there is no intrinsic reason why cars should stop at a red
light rather than at a green light, or that they should drive on the right side of the road
THE GLOBAL SUPERORGANISM 30
rather than on the left. Yet, these arbitrary conventions become useful ways of
regulating traffic if everybody follows them. Without these seeming restrictions on your
freedom to drive on the side of the road that you prefer, driving would become much
more difficult and dangerous, effectively limiting your freedom of movement. The
freedom lost by following the rules is more than compensated by the freedom gained
because of a fluid and safe flow of vehicles.
The problem with such rules is that everybody should agree to follow them. Since
the content of these rules is in part arbitrary, different cultures or traditions tend to
evolve different rules (cf. Heylighen & Campbell, 1995). For example, cars in Great
Britain drive on the left side of the road, unlike cars in most other parts of the world.
Changing an established rule is difficult, costly and stressful, and will be resisted by the
groups that traditionally follow them—especially if they have (real or imagined) reasons
to believe their rules are superior. Yet, global integration entails an eventual
harmonization of rules, so as to make the free exchange of goods, services, people and
information as fluid as possible. This also implies a reduction of the freedom of certain
groups (e.g. governments) to set rules that differ from the rules used by the rest. This
provides a strong motive for such groups to resist integration. For example, the
European Union, the until now most successful attempt at transnational integration,
experiences constant pressures to block harmonization of laws and standards, e.g. in the
creation of a common currency.
The possibility seems real that some groups or countries will effectively want to
remain outside the emerging global society. It is also conceivable that different
federations of countries will be formed, each following their own set of rules, while
minimizing exchanges with each other. This happened to some degree during the Cold
War when capitalist countries were politically and ideologically separated from the
communist block. At the moment, the more important divisions perhaps oppose
developed countries and developing nations, or countries with a Christian tradition and
countries with an Islamic tradition. A deepening of such divisions could in principle lead
to the creation of separate, competing superorganisms.
Yet, there are several reasons why this scenario appears unlikely. The first reason
is similar to the reason why individual drop-outs are rare. A country that would decide
to leave the international community with its systems and rules, would immediately
lose a great many benefits: resources, products, services, information, new technologies,
solidarity, etc. This would significantly slow down or even reverse its development
compared to other countries. This can be illustrated by the fate of “pariah” states, like
Iraq, North Korea, or Albania before the fall of communism. Such growing
backwardness would provide an increasingly strong incentive for the regime to change
its policies.
The negative effects of disconnecting from the rest of the world could be mitigated
if a large number countries would “drop out” together, forming a rival block. However,
the Cold War has shown that two competing blocks, even if they seem roughly matched
in size, resources or military power, are unlikely to remain at the same level of
development. Because economic and technological progress is an ever accelerating,
exponential process, small differences in initial conditions or speed of development will
lead to increasing gaps, until it becomes clear for everybody that the one block is more
successful than the other one. This will put increasing pressure on the less successful
block to open up towards the more successful one, in order to assimilate its successes.
A second reason why a splitting up of the superorganism seems unlikely is the
homogenizing effect of global communication on preferences and standards. If people
have to choose between competing, but similarly valuable, alternatives, they tend to
choose the one they encounter most often. This reinforces the lead of the most common
one, in a positive feedback loop (the “law of increasing returns”) that quickly drives out
all alternatives but one (“lock-in”, see Arthur, 1989). In a situation where
31 HEYLIGHEN
communication between groups is limited, this may lead to different standards in
different groups (cf. Campbell, 1982; Heylighen & Campbell, 1995), but in an era of
fast, global communication, all groups will tend to converge on a single standard in a
rather short time.
The third reason is that the basic values underlying different political and ethical
systems are effectively universal. The above scenario assumes that the competing
options are about equally valuable (e.g. VHS vs. Betamax standards for video). But what
if different cultures or groups disagree about fundamental values? For example, some
nations consider capital punishment to be intrinsically barbaric, while others believe that
certain crimes must be punished by death. Such differences have led postmodernist
thinkers to argue that values are intrinsically relative or culture-dependent, and therefore
there cannot be a rational mechanism for reaching a consensus. Even if we forget about
the “irrational” mechanism of increasing returns discussed above, there are good grounds
for consensus. Although different religions and ideologies may disagree about certain
concrete dos and don'ts (such as taboos against eating pork, respectively against eating
beef), most of their basic values are shared. All ethical systems condemn murder, theft,
rape, lying, incest, etc. On the positive side, people from all cultures basically agree
about the value of health, wealth, friendship, knowledge, honesty, safety, equality,
freedom, etc. In fact, such universal values can be derived empirically, by examining
which socio-economic factors correlate with people's happiness or life-satisfaction
across different groups (Heylighen & Bernheim, 2000a), and supported theoretically, by
examining which conditions are conducive to evolutionary fitness on the individual and
the social level. The resulting list is remarkably similar to the Universal Declaration of
Human Rights, showing that universal standards can be rationally agreed upon, even
though the practical implementation in most cases remains open to discussion. The
emerging global network can only intensify and accelerate that on-going discussion.
Another recurrent worry is that the kind of socio-technological developments we
have sketched may increase the gap between haves and have-nots, and more particularly
between those that have access to information and those that have not, thus creating an
“underclass” of people excluded from the benefits of the superorganism. Although the
“global brain” technologies that we sketched will be adopted most quickly by the
wealthiest and best educated populations, this will not stop the poorer regions from
joining a little later. Internet technologies are relatively inexpensive to install, compared
to e.g. roads, electricity or running water, and are becoming ever less expensive.
Moreover, as the interface becomes more intelligent, it will become ever easier to use,
requiring an ever lower education level for entry. Speech technologies will soon make the
web available even for illiterates, and may teach them to read and write in the process.
Thus, the emerging global brain is an inexpensive and efficient medium to increase the
education level, access to information, and economic competitiveness in all regions of
the world, helping Third World countries to bridge the gap with the wealthiest
countries.
As Stock (1993) suggests, if regions such as Central Africa still suffer from the
wars, famines, epidemics and other atrocities that have become inconceivable in the
developed world, it is because the superorganism's nervous and circulatory systems
have not yet really implanted in those regions, making them vulnerable to lack of
resources, diseases and other perturbations that would otherwise be under tight control.
However, as it is in the superorganism's interest to suppress perturbations not only in
its core but also in its periphery, there is an on-going pressure to extend these systems
even to the most remote regions.
THE GLOBAL SUPERORGANISM 32
8. Conclusion
This paper has proposed a first sketch of an evolutionary-cybernetic model of society
and its development, seen as the emergence of a global superorganism. The reasoning
underlying the model can be summarized as follows.
Complex systems composed of a variety of interacting subsystems, such as
chemical networks, ecosystems, or societies, tend to evolve towards more coherence and
interdependence, as the subsystems mutually adapt. This makes the system as a whole
less dependent on its environment, and thus increasingly “closed”. Once there is a
sufficient degree of organizational closure, the system can be seen as autopoietic, and
therefore “living” in the abstract sense. All such “living” or “organismic” systems
combine organizational closure, realized through a network of internal feedback cycles,
with thermodynamic openness, entailing the input of low entropy resources and the
output of high entropy waste. This allows us to conceptually divide the system into
functional components responsible for the different stages of the processing of incoming
matter and energy (metabolism), and for the processing of information needed to
maintain cybernetic control over this mechanism (nervous system). As the system
continues to evolve, on-going adaptation and division of labor lead to an increasingly
diverse, complex, and efficient organization, consisting of ever more specialized
components.
This general model of complex, self-organizing systems can be directly applied to
the present development of society. Since society is an organismic system consisting of
organisms (individual people), it can be viewed as a “superorganism”. Conspicuous
trends such as globalization, automation, and the rise of computer networks can be
understood as aspects of the general evolution towards increasing efficiency and
interconnectedness which makes the superorganism ever more robust. In particular,
increasing efficiency explains the growing economic productivity and the decrease of
friction, which facilitates all material and informational exchanges. The accompanying
differentiation and integration explain the seemingly opposite trends towards
outsourcing and mergers, and the growing importance of supranational rules, standards
and institutions. Increasing efficiency of communication and control moreover explains
the increasing functional autonomy of components (individuals or organizations), and
the concurrent flattening of hierarchies and rise of heterarchies.
Although the effects of these trends are mostly positive, for both individuals and
society as a whole (Heylighen & Bernheim, 2000a), some of the side-effects can be
detrimental (Heylighen & Bernheim, 2000b). Reduced friction in particular increases the
risk that positive feedback processes would get out of control. It also leads to
increasingly complex causal chains of interconnected events, augmenting the need for
information gathering and processing. Controlling these dangers requires a strengthening
of the superorganism's nervous system. This control system has both centralized and
distributed components. Centralization is exemplified by the growing importance of
global institutions, responsible for the formulation and implementation of international
standards, rules and laws.
Distributed control can be exemplified by the “invisible hand” that mutually adjusts
supply and demand. Its effectiveness is boosted by the emerging global computer
network. The increasing reach, capacity, and intelligence of this network allow it to
automate more and more functions of the superorganism's nervous system. This will
transform the World-Wide Web into a “global brain”, capable of sensing, interpreting,
learning, thinking, deciding, and initiating actions. Individuals are likely to become more
and more intimately connected to this intelligent network, through ubiquitous, intuitive
interfaces, and eventually a direct brain-to-web connection.
The traditional view of society as an organism is controversial, as it seems to imply
a restriction of freedom and diversity, and a subordination of individuals to a faceless
33 HEYLIGHEN
collective. The present model, on the other hand, sees the emerging superorganism as a
further step in the emancipation of humanity, increasing individual autonomy, diversity,
and various freedoms of choice, movement, education, career, expression, etc., while
decreasing the power of governments, corporations, or dictators to control society for
their own purposes. The integration of individuals and organizations into an efficient,
coherent supersystem, though, will require the agreement about a number of universal
standards and rules for the exchange of goods, services and information. However,
because of the greater flexibility and efficiency of a self-organizing, “global brain”-like
system, these rules are likely to be less constraining than existing national laws and
regulations, generally increasing the diversity of options and freedom of initiative
available to individuals.
In conclusion, the picture of an emerging global organism that I have sketched, like
the one of Stock (1993), is an optimistic one: although the increasing complexity and
accelerating changes that accompany this social evolution may temporarily add to
existing stress, conflicts and confusion, overall developments are for the better,
increasing people's wealth, freedom, sense of belonging, level of knowledge, equality of
opportunity, and overall quality of life (cf. Heylighen & Bernheim, 2000a,b), while
creating a more flexible, efficient and sustainable society. Moreover, because of the
underlying selective pressures and feedback cycles, this development appears quite
robust, and can probably be arrested only by a major catastrophe such as a nuclear war
or an asteroid impact.
The model throws new light on several contemporary issues such as globalization
of markets, computer networks, and the information economy, and thus may help us to
understand better what is going on in our complex and rapidly changing society.
Moreover, it makes a number of general, qualitative predictions, such as further
reduction of friction, restructuring of organizations, long-term improvement of control
over the economy, increasing efficiency in production, information processing and
services, greater integration and differentiation in the global socio-economic system, and
the emergence of a sophisticated collective intelligence for decision-making and problem-
solving supported by the computer network.
The question can be raised in how far a true organismic model is really necessary to
explain these developments. Most of them could be probably be derived from a weaker
evolutionary or developmental theory of society or of globalization. The strength of the
superorganism model is that it allows a very detailed analysis, zooming in on specialized
functional components, such as immune system, distributor, or associator, that have no
obvious counterpart in non-living systems. Applying the general logic of network
evolution to each of these functions allows us to produce specific predictions, such as
the creation of a computer immune system, a fully automatic distribution network, or a
world-wide web that autonomously learns new associations. There is no obvious way
to infer such predictions from a more general model, except by including a number of ad
hoc hypotheses.
Of course, proposing falsifiable predictions is not yet sufficient to make this into a
good model: the predictions must also be tested and verified. The problem is that we
cannot do experiments with an encompassing system such as global society. We can
only wait and observe. It will take many years before any of these predictions can be
convincingly confirmed or refuted. In the meantime, the model itself will undoubtedly
have evolved, taking into account factors that have been ignored until now. The
refutation of any specific prediction should therefore not be interpreted as a falsification
of the model as a whole, but rather as an admonition to reflect more deeply about the
exceedingly complex interactions within global society. The refutation of several
predictions, on the other hand, would be sufficient ground to abandon the model, and
look for a better one. Although the time scale is usually the most error-prone aspect of
any futurological prediction, I would venture that most of these developments will have
THE GLOBAL SUPERORGANISM 34
taken place within the next 10 to 20 year, whereas the global superorganism itself
should have taken a shape clear enough for everybody to recognize it by the next half
century.
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Acknowledgments
I thank Jan Bernheim for his helpful and detailed annotations on this paper, and my
Principia Cybernetica colleagues Valentin Turchin, Cliff Joslyn and Johan Bollen,
together with the late Donald Campbell and the members of the Global Brain mailing
list, for inspiring and discussing many of these ideas. This work was supported by the
Fund for Scientific Research - Flanders.
37 HEYLIGHEN
About the author
Francis Heylighen is associate director of the Center "Leo Apostel" for
transdisciplinary research at the Free University of Brussels (VUB). The main focus of
his research is the evolutionary development of complex, cybernetic systems. He is
editor of the Principia Cybernetica Project (http://pcp.vub.ac.be/), an international
organization devoted to the collaborative development of an evolutionary world-view,
and chair of the Global Brain Group. Dr. Heylighen has authored some 70 scientific
publications in a variety of domains.
... Accordingly, the identification of the global brain which was specified as a distributed and open-ended intelligent structure would address that such matter through setting up a globally democratic order (Last, 2017). The global brain characterized in a flat network of associated with artefacts and determinants, the internet of thing, which related to each other and could involve in the system in an uncontainable way at various points of entry, particularly in brief of the scale of the global network (Heylighen, 2007;Heylighen and Lenartowicz, 2017). Building on these abovementioned, the global brain reflective accounting practices could be treated as a collective mind which offered the enlightenments on the accounting practices and therefore became the potential determinants driven the distributed superintelligence, monetary and non-monetary value generation (Heylighen, 2017) as well as non-sustainability issues resolutions (Goertzel and Goertzel, 2017;Lenartowicz, 2017). ...
... The global brain has been regarded as an implied comparison for the whole connectivity that typically evolved across the global world and has been reflective of evolutionary cybernetics (Heylighen and Lenartowicz, 2017). Notably, the global brain characterized in a flat network of relevant artefacts and factors, the internet of thing imperatively, which related to each other and could participate in the system in the unmanageable way at a variety points of entries, especially in brief of the volume of the large-scale network (Heylighen, 2007;Heylighen and Lenartowicz, 2017). The global brain has been well-acknowledged to be an indispensable component of the Fourth Industrial Revolution (Bonilla et al., 2018). ...
... The global brain has been supposed to be constituted on the notion of evolutionary cybernetics which prerequisite searched for accomplish targets through forecast, with the final objectives being survival and proliferation (Al-Htaybat et al., 2019). In term of the global brain, people and their technological determinants could cooperate via the internet (Heylighen, 2007). Hence, the global brain has been regarded as a selforganizing, adopting network generated by people and machines connected by means of information technologies in a cohesive system (Heylighen and Lenartowicz, 2017). ...
Article
The current research conceptualizes and validates a model concentrating on how policy initiatives foster the big data management capabilities (BDMC) to achieve sustainability. Additionally, it also pursues to delve into the mediation mechanism of Global brain reflective management accounting practices (GBAP) in the linkage between BDMC and sustainability. Outstandingly, it makes several endeavors to deepen insight on whether the extent of the effect of BDMC on GBAP and the effect of GBAP on sustainability vary resting on specific degree of innovation human resource management (IHRM). The statistical data of a convenient and snowball sample of 612 participants was gathered from a structured and close-ended questionnaire survey. In order to bring forth the proposed hypothesized interconnections, the fundamental analytical instrument utilized was structural equation modeling (SEM). Additionally, multi-group SEM analysis was also applied to corroborate the moderating effects of IHRM. Beyond ameliorating the insight into how intersection of accounting practices and new technologies could make a huge contribution to BDMC enhancement to reach the sustainability paradigm, the observations of this research gave rise to the practical implications for the practitioners in organizational management and policy-makers in promulgating rules in relation to digital transformation implementation within small and medium enterprises.
... This is a perfect parallel of the basic functions between the individual organism and the organized society. 21 The political forms of the latter also evolve depending on the degree of development of the productive forces. 22 The productive forces themselves are, in essence, technologies for utilizing nature in the interests of society. ...
... The societies themselves possess obvious features of a super-organisms in which each active individual using his/her PC strives to integrate into Internet and telecommunications networks like in a «nervous web» of developing «global mind». 21,23 From the concept of Geo-Solaris, i.e. a view of the Earth embraced with the evolving living matter as an intuitively thinking brain bringing about the bio-technological mind of Noosphere. 3,18 A characteristic feature of this Noogenesis 47 is in the reproduction of personal intellectual potential on the level of mankind: the world population is approaching the number of nerve cells in an individual brain while the World-Wide Web is acquiring the structure of a neural network. ...
Article
Full-text available
The evolutionary mechanisms of anthropogenesis as well as the development of social organisms are analyzed. The concept of Geo-Solaris, treating the Earth, encompassed by evolving living matter, as an intuitive thinking brain, is outlined. The problems of modern civilization arising from the aggressive nature of man, exploited by authoritarian leaders, are examined. The role of space technology in planetary megasynthesis is noted. The hypotheses of cosmic expansion of E-creatures and cyborgs are critically examined. The conclusion is substantiated that any evolutionary process will reproduce a human entity adapted to the parent planet.
... Maturana and Valera's discussion of communication and 'languaging' (Maturana and Varela 1980) as coordination of coordination (through the coupling of different type systems, in our case), and to the mainstream of Semiotics of Systems (Rocha 2000;Lemke 2000) and Second Order Cybernetics (Heylighen 2003 ), yet so far with surprisingly few consequences for information system design: ...
Preprint
Given that theoretical analysis and empirical validation is fundamental to any model, whether conceptual or formal, it is surprising that these two tools of scientific discovery are so often ignored in the contemporary studies of communication. In this paper, we pursued the ideas of a) correcting and expanding the modeling approaches of linguistics, which are otherwise inapplicable (more precisely, which should not but are widely applied), to the general case of hypermedia-based communication, and b) developing techniques for empirical validation of semiotic models, which are nowadays routinely used to explore (in fact, to conjecture about) internal mechanisms of complex systems, yet on a purely speculative basis. This study thus offers two experimentally tested substantive contributions: the formal representation of communication as the mutually-orienting behavior of coupled autonomous systems, and the mathematical interpretation of the semiosis of communication, which together offer a concrete and parsimonious understanding of diverse communication phenomena.
... Cyberneticists consider colony organisms as useful models of algorithmic computation (Holldobler and Wilson 2009, ch.3) and point out their relevance to multiagent systems in artificial intelligence (Bianchini 2013). On the other side, cyberneticists also draw analogies between human social systems and superorganisms (Heylighen 2007). Thus, the concept of the superorganism forms a bridge between artificial superintelligence and the state, highlighting in particular how systems for communication and control can be layered on top of local, heterogeneous systems to produce behaviors too complex for any individual agent. ...
Article
Full-text available
Debates about the development of artificial superintelligence and its potential threats to humanity tend to assume that such a system would be historically unprecedented, and that its behavior must be predicted from first principles. I argue that this is not true: we can analyze multiagent intelligent systems (the best candidates for practical superintelligence) by comparing them to states, which also unite heterogeneous intelligences to achieve superhuman goals. States provide a model for several problems discussed in the literature on superintelligence, such as principal-agent problems and Instrumental Convergence. Philosophical arguments about governance, therefore, provide possible solutions to these problems, or point out problems in previously suggested solutions. In particular, the liberal concept of checks and balances, and Hannah Arendt’s concept of legitimacy, describe how state behavior is constrained by the preferences of constituents that could also apply to artificial systems. However, they also point out ways in which present-day computational developments could destabilize the international order by reducing the number of decision-makers involved in state actions. Thus, interstate competition not only serves as a model for the behavior of dangerous computational intelligences but also as the impetus for their development.
... In communication networks, there is cybernetics tradition which is a complex system consisting of several sub-systems that are interconnected with each other. If it is associated with a communication network in the tradition of cybernetics, communication is a system whose elements influence each other, form, and control to achieve the goals of balance and change (Heylighen, 2007). Social Network Analysis (SNA) is one of the right solutions to analyze the relationship formed by the hashtag #SaveSangiheIsland on Twitter. ...
Conference Paper
Full-text available
The extractive industry in Indonesia has become a priority to attract investors. The presence of an exploitative industry is feared to cause ecocide and the destruction of natural resources and ecosystems. This ecocide also occurred in the Sangihe Islands. The government gave gold mining exploitation to PT. Tambang Mas Sangihe (TMS). These concessions led to rejection and resistance from the people of the Sangihe Islands. The resistance was through demonstrations and Twitter, with the main actor being The Network of Mining Advocacy (Jatam) through the @Jatamnas account. This study aims to analyze @Jatamnas’ digital activism against ecocide in the Sangihe Islands. This study used the qualitative approach with the virtual ethnography method. The data used is the content of the @Jatamnas account on Twitter in the period June-December 2021. The results show that the campaign with the hashtags #SaveSangiheIsland provoked netizen reactions to support the Sangihe communities. The online conversations at #SaveSangiheIsland on the Jatamnas account revealed the dynamics of the need to embed local communities from the clutches of the oligarchs who hide behind the terminology of investment and employment. Furthermore, the conversation also tends to weaken the local community struggle because of the many accounts suspected to be pro-government which enter into the discussion with local wisdom-biased. However, the #SaveSangiheIsland serves as a public space for open debates on ecological issues.
... One of the most prominent researchers in this field is the Belgian Francis Heylighen. The research goals of his 'Global Brain' project, which he co-founded, include nothing less than the development of algorithms that are intended to further the World Wide Web into a self-organising, learning network with collective intelligence in the sense of a 'Global Brain' (Heylighen, 2007). ...
Book
The book, deliberately written in generally understandable language for all interested readers, paints a unique, transdisciplinary overall picture of resilience as a national and international social factor of our time. It shows that in terms of socio-political significance, the concept of resilience is in no way inferior to the older, hitherto dominant concepts of sustainability and development; indeed, it actively complements them, in some cases contradicts them, but also completes them. Resilience as a societal factor involves all sectors, such as politics, the economy, science and civil society, and thus represents an indispensable frame of reference in the overarching recent debate on the "learning society".
... Pero antes de embarcarse más en la analogía, es necesario aclarar que aquí se parte del supuesto de que el ser humano es un ser social por condiciones prerracionales, que no es más que remitir a que su condición como especie radica en la formación de conglomerados, similar a las hormigas o abejas; esta perspectiva no es propia, sino que se ha heredado de diversas investigaciones en la materia (de Melo, 2020;Heylighen, 2007;Spencer, 1898;1922;Wiener, 1985), por lo que es importante señalar que, desde la perspectiva prerracional de la comunicación social (Luhmann, 1996;2010;1992) la formación de grupos es lo primero que acontece antes que el imperio de las normas de comportamiento racionales. ...
Article
Full-text available
Las instituciones son fundamentales para delimitar la sociedad, ya que permiten la legitimación de valores y principios desde las afinidades que puedan tener los individuos que la componen. Teniendo en cuenta lo anterior, en este trabajo se explora la hipótesis de que estudiantes universitarios con valores similares son afines entre sí. Para probarla se aplican dos instrumentos: primero, el Estudio de valores de Allport, Vernon y Lindzey, y segundo, una encuesta de percepción de afinidad de elaboración propia. Esto con la finalidad de explorar la formación de microsociedades, así como del rol que cumplen los valores e instituciones mediante un análisis de afinidad de valores en estudiantes universitarios. A partir de un procesamiento de modularidad se examinan las relaciones sociales de dos grupos de estudiantes que cursan una carrera universitaria y se compara su composición de valores. La evidencia permite concluir que no existe una composición de valores sustancialmente distinta en estudiantes, por lo que sus valores pueden surgir de un contexto más allá del universitario, por ejemplo, su localidad, región o un fenómeno generacional. Los resultados señalan que las afinidades de los participantes fueron recíprocas, indicando que las percepciones entre ellos coinciden, por lo que se abona a la noción de conciencias supraindividuales.
Chapter
This chapter explores in a structured way different aspects of collective intelligence, such as collectives, systems, and infrastructures, as well as their orchestration. It aims to contribute to the existing body of knowledge and provide a practical and actionable conceptual framework. The benefits of this study extend beyond academia to organizations and policymakers with the potential to drive innovation, improve decision-making, and address complex challenges in various domains. Globalization and technological change, increasing interdependencies, and optimizing some variables, such as resource extraction or consumption, have diminished the resilience of the global (eco)system. In the long run, the system is only as strong as its weakest element. Thanks to collective intelligence, these shocks could allow us to resize the global vision of solidarity and boost true resilience and sustainability.
Book
As evidence of our global survival crisis continues to mount, the expression 'too little, too late' comes to mind. We all live in an interdependent world which has an increasingly shared fate. We are participants in an emerging global 'superorganism' that is dependent on close cooperation. Indeed, positive synergy (cooperative effects) has been the key to our evolutionary success as a species. However, our ultimate fate is now in jeopardy. Going forward, we must either create a more effective global society (with collective self-governance) or our species will very likely be convulsed by mass starvation, waves of desperate migrants, and lethal social conflict. The greatest threat we may face is each other, and a regression into tribalism and violent conflict. This Element has a more hopeful prescription for a new global social contract. It is based on the many examples of superorganisms – socially organized species – in the natural world, and in evolution.
Article
Full-text available
this paper by the Fund for Scientific Research-Flanders (FWO), as a Senior Research Associate.
Book
Cultural evolutionism ("survival of the fittest" in terms of cultural and social forms); society as organism (heavy organic analogy); evolution from homogenous state to heterogeneous state, increasing differentiation, specialization, division of labor and interdependence; society has reality beyond sum of individual parts; progress is driven by man’s innate adaptability to higher states of perfection
The selfish gene The Macroscope The Symbiotic Man
  • R Dawkins
  • Oxford
  • J De Rosnay
Dawkins, R. (1989) The selfish gene (2nd edition), Oxford University Press, Oxford. de Rosnay, J. (1979) The Macroscope. Harper & Row, New York. de Rosnay, J. (1986) Le Cerveau Planétaire. Olivier Orban, Paris. de Rosnay, J. (2000) The Symbiotic Man. McGraw-Hill
Le Phénomène Humain. Seuil, Paris. (translated as: The Phenomenon of Man
  • P Teilhard De Chardin
Teilhard de Chardin, P. (1955) Le Phénomène Humain. Seuil, Paris. (translated as: The Phenomenon of Man (1959). Harper & Row, New York).