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<nettime> More on Bruno Latour 2/2
Felix Stalder on Sun, 7 Sep 1997 00:40:47 +0200 (MET DST)


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<nettime> More on Bruno Latour 2/2


__3. Neighbouring Theories__

The following section will examine  some aspects of those neighbouring
concepts to further sharpen the ideas of the Actor-Network Theory and to
position them in broader trends in current thinking concerned with
questions about networks, complexity and self-organization.


_Systems Theory and the Concept of the Border_

One set of questions that remains somewhat vague in the Actor-Network
Theory is how to limit the analysis; where does one network end and the
next one begin? Michel Callon refers simply back to empirical studies in
concluding "a network's boundary can be related to its degree of
convergence. We will say that element Y is outside of a network if
locating the links between it and the actors (A, B, C ...) significantly
decreases the network's degree of convergence.". Not of much more help
are Wiebe Bijker and John Law stating that "in effect it rests on a bet
that for certain purposes some phenomena are more important than others.
It simplifies down to what it takes to be essential."  For Bruno Latour,
the description of a network is simply finished when it is "saturated"
and an explanation emerges. In other words, the question of how to limit
the analysis can only be addressed on a empirical level. However, other
theoretical traditions have long been concerned with similar questions
and their conclusions can be of help to clarify this question, for
example the so-called systems theory.


A general definition of systems has been provided by Parsons who refers
to "both to a complex of interdependencies between parts, components,
and processes that involves discernible regularities in relationship,
and to a similar type of interdependency between such a complex and its
surrounding environment." What marks the difference between the internal
and the external interdependencies of a system is its boundary. The
boundary is at the same time separating the system from its environment
and connecting it to it by serving as a filter to translate stimuli into
input that can be dealt with by the system's internal structure.  "The
boundary, therefore, separates and unites and, as such, illustrates the
dynamic characteristic of every distinction." Establishing and
maintaining such a boundary is always an active process. This process of
establishing a boundary, of defining who is included in and who is
excluded from a network, can be used as one of the indicators of how to
limit the extension of a network.

The boundary separates and joins two set of relationships: those of the
system (internal) and those to the environment (external). An
influential theory of those processes-the difference between the
internal structure, the boundary and the environment-has been developed
by the Chilean cell-biologists Humberto Manturana and Francisco Varela
(1980) as the theory of "autopoiesis" (self-formation). They defined
autopoietic systems  as "self-contained unities whose only reference is
to themselves." To qualify as an autopoietic system they developed six
criteria:
- The system needs a border to make the it identifiable.
- It needs components so that the system is analyzable.
- The interaction of the components can be described in general physical
laws, in other words, the system a natural system.
- The boundary is self-maintained by preferential neigbourhood
relations. The system, therefore, can stabilize its own boundary.
- The systems is contained within and producing the boundary.
- The system is self-productive. The system uses only its own components
or transformed imported components.


The concepts-autopoiesis and actor-network-have some overlap in the
notion of self-formation and the idea of mutual constitution between
actor and network. A network is composed of actors which define the
network, or, the network forms and maintains itself out of its own
components, it is in this sense autopoietic. The criteria of
self-production can be applied to define the limits of a network. It
then includes all elements which are necessary to achieve and maintain
objectives of the network. Environments are, then, all elements that
influence the network but are themselves not actively involved in
maintaining the network. In other words, the environment can only
influence a network insofar as it is translated into the network by one
or more components of the network. The communication, therefore, between
the network and the environment is always indirect and determined by the
characteristics of the network itself.  

The place where this translation takes place is the border. The border
in a social system is evidently not physical, as in a cell, but logical
or functional. For example, the members of a family are determined be
the relationship to one another, a company is an entity even if it is
dispersed around the globe or entirely virtual, and, a doctor does not
leave the system of the hospital system as long as he/she is connected
with a beeper.

The notion of the border develop so far can be used to clarify that any
element which is directly needed to achieve a certain goal is inside the
network. Any element that is able to influence an actor but is not
inside the network belongs to the environment. The separation and
connection between the two done at the border is maintained by the
network itself.


_Evolution and the Dynamics of Systems_

Why does an actor act and why are networks dynamic?

The Actor-Network Theory has paid to little attention to those
questions. The acting actor and the dynamic network are presupposed as a
given, they develop simply "deliberately or otherwise". The concept of
autopoietic systems which served as an inspiration to conceptualize the
limits of a network provides also an interesting idea to understand the
dynamics of such systems: "structural coupling". The changes which a
system can undergo are determined by the Organization10 of the system,
which is maintained within and through the border. In this sense, the
system is organizationally closed because it can only change according
to its inner Organization. These changes may preserve a system's
Structure or they may alter it radically, e.g. a seed growing into a
tree. A Structure exists within a environment that perturbs it and can
trigger changes. The environment  does not determine the changes, rather
the environment selects changes made possible by the system's
Organization. An acorn can or cannot grow into an oak which can be
taller or shorter, however, it can never grow into a pine. Because the
environment can trigger changes, the system is interactively open.

In an environment characterized by recurring states (as opposed to total
randomness or invariability) and natural selection those structures
which are suitable for that environment are most likely to survive. As a
result, any such (self-referential) unit is structurally coupled with
the environment . Since the environment consists of other units that
function according to similar principles, autopoietic systems are
structurally coupled. The units provide the environment for and stand in
constant relationship to one another. Through this interdependence,
change can ripple through the large number of systems: several systems,
arguably all living (and non-living) systems on earth co-evolve.

Co-evolution expresses in biology the idea that at every observable
level (e.g. cells, organs, individuals, or species) evolutionary
adaptation in one system changes the conditions for all other systems to
which it is (part of) the environment. Therefore even previously stable
systems come under adaptive pressure if their environment changes.
Adaptation or more general adaptive capacity is a central concept in
systems theory. For Talcott Parsons, standing within the concept of 
functionalism, it served as the indicator of development and progress of
a system. The higher the adaptive capacity of a social system the higher
it is developed.

The equivalent of  structural coupling in the actor-network is the
conception that all actors are participating in several (sometime
conflicting) networks. In this sense, even networks which are extremely
closed and stable-black boxes-are connected to an ever changing
environment. A change in one of the networks in which an actor is
involved might alter him/her/it in such a radical way that he/she/it is,
as an effect, ripped out of another, previously sealed network. The
Copernican Revolution, for example, not only changed astronomy but also
had considerable influence on the catholic church which up to this point
had maintained its network of meaning (theology) with the earth as the
actor at the center of the universe, a stable platform upon which the
celestial ladder was firmly planted.

Networks, while maintaining themselves, are therefore interrelated with
one another across their boundaries. While networks constitute
themselves in the circulation of intermediaries among actors which are
themselves defined by the same circulation of intermediaries, no actor
is exclusively defined by one such network. The case can be made even
stronger: the reason why a particular actor is included in a given or
emerging network is precisely because he/she/it brings along all the
actors of the other networks to which he/she/it belongs. In Latour's
analysis this is one of the reasons why scientists refer to classics. By
including those cherished pillars of the discipline into their text
authors try not only to include a specific piece of information but also
its reputation, i.e. all the other actors which also positively refer to
this source (Latour, 1987).

Networks develop because they are in inseparable dynamic interaction
with other networks and self-reproduction requires adaptation to an ever
changing environment.


_Complexity and the Evolution of Order_

Why do networks have a tendency to attempt convergence not only
internally but coordinated with other networks? 

These are very much the questions of the recently much publicized
science of complexity. Why is there such apparent order on all levels
when one might expect randomness in the absence of an overarching plan
or deterministic laws? How do simple local rules and strategies give
rise to global patterns? I want to focus here on one aspect of the
complexity theories as applied to biology by Stuart A. Kauffman: the
self-organized, critical state of a system.

The concept of the self-organized, critical state of a system joins two
major ideas together. First, order in such systems emerges only out of
the interrelations of the elements of the systems. As a result the
patterns on the macro level can be more complex than the patterns at the
micro level.  Second, this order is critical in the sense that is
unstable between two stable states; the state of "frozen" stability
where nothing moves and the state of random change which exhibits not
stable pattern. In both stable states, evolution is not possible. This
idea is best demonstrated in the model of an "correlated fitness
landscape".

Biological fitness of a system, its ability to reproduce itself, can be
modeled as a landscape where the peaks indicate high and the valleys low
reproductive ability. The position of a unit on this landscape indicates
it adaptation to the reproductive constraints imposed by the
environment. A unit within such a landscape can move from whatever site
it is located at a given point in time to any of the neighbouring sites,
one step at a time. Moving upwards means that the next generation
inherits better reproductive ability, moving downwards means that the
next generation inherits a less well adapted set of abilities. Each
generation takes one step12 until it reaches either the top of the peak
or becomes extinct. This movement is thought to be blind, a trial and
error procedure, a "random walk". However, over the course of
generations natural selection "pushes" the units upwards.

So far, it is a very unrealistic model. Without any further
specification, we have to think of this landscape as random, very much
like the surface of the moon: stable and whimsically rugged. Local peaks
and local valleys soaring and plummeting everywhere. In such a landscape
evolution cannot develop very far because it would get stuck very soon
on a local peak where all neighbouring states are lower, but the local
peak itself might not be very high. The opposite of such a rugged
landscape is a smooth one with one or a very few high peaks, resembling
the landscape of the Fuijama. However, with random assignment of
fitness, such a landscape is highly unlikely. Moreover, fitness
landscapes in biological system are dynamic or correlated because living
units are structurally coupled, providing the environment for each
other. This means that every adaptive change in one unit changes the
fitness landscape for others because it changes the environment to which
they have to adapt to. The peaks and valleys are shifting constantly. In
random, rugged landscapes, the variables that influence survival change
constantly, evolution is impossible, i.e. the fitness of all units is
low and threatened by unpredictable developments. 

This is evidently not the case in real life. Because selection favours
the fitter, it favours not simply the one which is best adapted to the
environment at a given instant, but the one which can also sustain an
environment in which its own development is optimized. The co-evolving
units influence the conditions of their own evolution, the landscape
develops along dynamic patterns and in a more or less smooth topology.
Evolution evolves! Talking about the development of cells, Kauffman
concludes that "we plausibly believe that selection can alter organisms
and their components so as to modify the structure of the fitness
landscapes over which those organisms evolve. By taking genomic networks
form the chaotic to the ordered regime, selection tunes the network
behavior to be sure. By tuning epistatic coupling of genes13 tune
landscape structure from rugged to smooth."

The units order themselves and at the same time their evolutionary
environment into patterns, they self-organize on the micro and the macro
level. The state of this self-organized system is critical because it is
between the two poles which allow no evolution, absolute stability and
total randomness. In order to evolve, a system must stabilize itself in
this unstable critical state.

On a very general level, this means that a system can only develop at a
certain pace and in a certain direction which  must be related to the
environment. Wrongly paced or directed development of one system will
diminish its chance for survival because the larger system is tuned into
a certain pattern. However, this does not mean stability. The pattern
can be rapid change. In this case, maintaining a invariant pattern would
mean to slide down  the slope in the fitness landscape. 

The similarity to actor networks is that the rate of change a network
can introduce or must adopt is related to the overall dynamics of the
environment. Introducing too radical change would mean that the
environment becomes very disturbed, which in turn might rip the network
apart. Not adopting change at the right moment might mean that the
actors become involved in an increasing number of conflicts between the
different networks to which they belong as they go "out of tune". In
this sense, because actors and networks are multiply determined, they
are involved in constant processes of tuning themselves to one another.

__4. Conclusion__

I do not propagate any kind of straight continuity to or natural laws in
socio-technological development. This would be utterly inappropriate
for, at least, two reasons. First, even on the level which these ideas
have been so far developed, they are nothing more than intriguing,
promising, and contested hypotheses. Kauffman, for example, regards his
own theories as "proto-science". Second and more important is, dynamics
on one level can not determine dynamics on levels above15. Even within
one field it is impossible to deduct the development on one level from
the development of the one underlying. Molecular biologist P.A. Weiss 
remarked that "moving down" from the cell to its components one finds:
"(a) rather well defined and stable complexes of functional and
structural properties which are embedded in and naturally related
through, (b) matrices of much less well-defined, more fleeting
configurations, allowing their constituent parts a much higher range of
freedom than could be reconciled with the macromechanical concept of a
cell."

The patterns on each level are self-organized according to the dynamics
arising in that particular level and are not determined but related to
the dynamics of the levels below. If that holds true for two
neighbouring levels of complexity-the cell and its components-it is even
more significant for levels which far away from each other-the cell and
the human society. The autonomy of social reality can be understood in
"double, paradoxical sense" : Every (natural) system is hierarchical, it
is made up of different interlocking levels of integration. However, it
is necessary to formulate the autonomy of each level based on the
principle of circular causality.  In an organism, for example, the laws
of physics leave a large degree of freedom to the individual elements.
This basic indeterminacy is reduced by the constraints exercised by the
whole, constraints which are themselves the result of the composition of
elementary activity. The whole and the parts are mutually determined,
and this co-determination explains the complexity of living systems. In
other words, each level is constrained but not determined by the level
below and the level above. Each higher level arises out of the
activities of elements at the level below but has to be analyzed in its
own right and with its appropriate conceptual tools.

For the analysis of socio-technological development, such a tool is the
Actor-Network Theory. It examines how competences are distributed within
heterogeneous networks composed of human and non-human actors. Actors
and networks are mutually constitutive in the sense that a network
shapes and defines the actors who align themselves into a network.
Intermediaries are passed among the actors to assure a certain degree of
convergence among them. This convergence allows the heterogeneous
network to act in a coherent way, that is to translate one actor's
objectives through a number of different actors to achieve a goal. The
prominence and the potential of an actor is defined by he/her/its
position within the network and by the size and degree of convergence of
the network. The higher the degree of convergence within a network-the
better, easier and more reliable the translation process works-the more
powerful it becomes. Convergence is always (potentially) contested. In
cases of very high convergence the network itself becomes so stable that
it can be treated as a black box,. Its complexity can be factored out of
the equation because the input-output relation is stable regardless of
the heterogeneity of the network it incorporates. Black boxes can take
on different forms, they can be artifacts, facts, norms, traditions, or
structures. They allow the reduction of the complexity of
socio-technological reality, in everyday life as well as in social
theory. We do not need to know the intimate details of mechanics to
drive a car. All we need to know is how to connect input (steering) with
output (the motion of the car), and, whom to call when the car breaks
down.... We do not need to know the personality of the clerk at the cash
register in order to trust him or her to hand over the money to pay for
our shopping. We do not have to take into account everything down to
every component. Whole sets of black boxes can be integrated purely on
the level of their in- and out-put because they remain stable.

To transfer the tools of the Actor-Network Theory from the study of
science and physical technology, for example, a high speed transit
system (Latour, 1996) to the study of process in and around
computer-based communication networks, we have to expand the concepts
into specific directions to better capture the particular dynamics of
this setting. These dynamics arise out of the structural properties of
the communication networks, which as powerful actors have to be
integrated into the actor-networks which are set up and maintained for a
specific goal within those communication networks. Their complexity and
interrelatedness, their evolutionary character and the stunning absence
of any overarching plan, all indicate that there are different dynamics
at play than in other sectors of technology. Latour demonstrates how
different elements shape the development and the plans for a transit
system, however, the systems failed at last because it was not possible
adopt one general plan. During the development of the product the actors
and the plan changed, but not the plan's centrality. The science of
complexity, as nascent as it is, can help us to develop tools to think
about settings that have no overall plan but nevertheless show
regularities.

The extraordinary pace of the development adds further dimensions to the
process. Not only the fixed artifacts or procedures are important, but
their evolutionary character moves into the foreground as one of their
integral, regular patterns. McLuhan pointed this out decades ago when he
claimed that change is the only constant in the electronic environment.
Theories of evolution are well experienced in accelerating time,
especially now that they can be tested in computers where 10 000
generations can develop in a single day. They can sharpen the intuition
to see in compressed time the structure of change which become central
in the immediate feedback loops of the electronic environment.

Amidst all those changes, the elements show a great ability in
maintaining their identity, in differentiating themselves from their
environment by constantly interacting with it. The concept of the border
in the theory of autopoietic systems is one example of how to think
organizational closure and continuity at once with interactive openness
and structural plasticity.

Whatever the credibility of these biological theories in their field may
be, for the study of socio-technological processes they are nothing more
(and nothing less) than inspiring. Their explanatory reputation cannot
be transferred from one field to another. It has to be gained in each
field anew. However they serve well as inspiration to further develop
the tools we have to conceptualize our techno-cultural environment and
foster cross-disciplinary thinking.



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