Luhmann's
Sociological Theory:
Its
Operationalization and Future
Perspectives
Elaborating
on the work of neurophysiologists like Maturana (e.g., 1978), Luhmann (1984)
has proposed a second-order social systems theory. This theory considers communications among human beings as units
of analysis, and thereby explicitly contextualizes human actors to function as
the carriers of a network of social relations.
On the one hand, this de-humanization seems a radical departure from
sociological theorizing, which has aimed at "Verstehen" and
sociological enlightenment about human intentionality. On the other hand, Luhmann's (1984) theory
differs from Parsons' (1937) systems theory of social action to such an extent
that Luhmann's contribution has been characterized as a
"second-order" systems theory.
But what does "second-order" mean in this context?
In
my opinion, second-order systems theory should be considered as a methodology:
second-order systems can be defined only in terms of distributions. While first-order systems can be identified
theoretically, distributions are expected to contain an uncertainty. Thus, second-order systems cannot be
delineated clearly in terms of empirical observables, although one is able to
specify an expectation. However, a
hypothesis requires a substantive specification. Thus, a second-order theory of social systems requires the
combination of the methodological perspective with a sociological theory.
By
considering the network of communication among people as the subject of
sociology Luhmann has indeed provided us with a theory that takes distributions
as its units of analysis. However, his
theory has hitherto remained speculative; if one has both theory and methods,
one should in principle be able to specify testable hypotheses. I shall argue below that the
operationalization of concepts from Luhmann's sociological theory of
communication in terms of a mathematical theory of communication enables us to
clarify semantical confusions to the extent that theoretical questions can be
made empirically researchable.
The
distinction of "the social system of reference"
At a time when an almost
"incommensurable" divide between social systems theory and symbolic
interactionism had been noted in sociological theory (e.g., Grathoff, 1978;
Giddens, 1979 and 1984), Luhmann (1984) submitted a theory which combined
elements from both these traditions. On
the one hand, Luhmann's theory shares a reference to Husserl's phenomenology
with symbolic interactionism. Luhmann,
however, proposed that the constitution of "meaning" be
operationalized in terms of codification in systems of communication. On the other hand, Luhmann elaborated on
Parsons' systems theory by stressing (functional) differentiation in systems of
communication, but he distinguished more clearly than Parsons between society
and the aggregate of human actors.
Parsons considered this distinction as a focus of organization, and he
defined the role of the actor as the conceptual unit of the social
system (e.g. Parsons & Shils 1951, at p. 190). A social role, however, remains an attribute of an actor.
Luhmann
(1984) extended Parsons' methodological distinction into a difference at the
epistemological level: he proposed to consider society and human beings as
different systems of reference.
Although the social system and the personality are both contructed in terms
of (inter‑)actions, the dynamics of the aggregates, i.e. their
"life-cycles", are expected to be different. Elsewhere, I have provided an
operationalization of this model in terms of "parallel and distributed
processes": each individual runs its own psychological cycle as a local
processor, and the psychological systems disturb one another by communicating
at the level of the network (Leydesdorff 1993a). Let me here elaborate on the relevant statistics in order to
further clarify these issues.
In
a standard (i.e. first-order) research design, actions can be attributed to
actors as variables. For example, one
may score the behaviour of actors in a social setting. The actors are then considered as the cases,
and the attributes as variables. Conventionally,
one sorts the cases as the rows and the variables as the columns of a
matrix. However, the columns of the
resulting matrix are a different frame of reference than the rows. The columns teach us about the distribution
of a particular variable (e.g., "right- and left-handedness in
writing") over the relevant social system ("the domain"), while
the rows inform us about the properties of individual actors. In other words, changes in the attributes of
observable cases are expected to have an effect on the distributions over the
columns. In the co-variation
between rows and columns, (Shannon-type) information is communicated between
the units (e.g., actors) and the system of their relevant distributions (cf.
Theil 1972).
One
is able to define "structural coupling" and "operational
closure" at this level of abstraction.
For example, each potential difference in a cell value on the occasion
of a second measurement informs us in the vertical dimension of the matrix
about a development at the level of the distributed system under study, and at
the same time from the other perspective about a change at the observable
actor level. Each single cell value is
an event that teaches us about a momentary and local coupling; another
row or column can be relatively unaffected by this change! Thus, the two systems of reference are
coupled at specific sites of their structure. Over time, the co-variation in the cell-values can be taken as an
indicator of their mutual "interpenetration" (Parsons, e.g. 1968) or
"structural coupling" (cf. Maturana 1978): the distribution at the
level of the network does not contain information without the actors
acting. However, the relative weight of
a co-variation in relation to the total variation over the columns and over the
rows is expected to be different. The two systems, i.e. the first-order
observable units of analysis and the distributions, are expected to develop on
the basis of their total variation. If
co-variation is repeated over time, structurally coupled systems may exhibit a
co-evolution.
Figure
1 illustrates how
co-varying systems by definition determine one another in the
(symmetrical) co-variation, while they condition one another in the
remaining variation (Leydesdorff 1995a).
In other words, to the extent that two systems do not disturb eachother
in terms of a co-variation, they leave one another "free" to follow
their own dynamics over time. Giddens
(1979 and 1984), for example, identified this relation between mutual
determination and the conditioning of two interacting systems as the
"enabling and constraining" functions of structure for action.
|
1Figure 1 Relations of variation (expected
information content H), co-variation (mutual information T), and remaining
variation (conditional entropies H(x|y) and H(y|x))
between two variables x and y (Attnaeve 1959).
|
In
summary, we have operationalized "action" as an attribute of the
actor which at the same time can be considered as a communication with
reference to the distributed system under study. Communication systems are distributed
systems; they contain an uncertainty, and therefore their boundaries remain an
expectation. The social system can be
considered as such a distributed system.
But hitherto, our analysis has been so abstract that the reasoning can
be applied to any research design, for example, to plants in a biological
research design about the properties of plants. We shall thus have to specify what turns a communication system
into a human communication system.
On
the basis of this operationalization, we are able to understand what it means
that distributed (i.e. second-order) systems are "operationally
closed" because of their structural coupling in terms of variables. Variables are specific: things (or beings)
to which one cannot meaningfully attribute a notion like "left- or
right-handedness" cannot be included into this distribution. (Perhaps, they communicate in another
dimension.) The column of a research
design contains a substantive selection.
Operational closure is thus a consequence of the research design. By substantive specification, the researcher
provides the uncertainty with a meaning: one specifies what will count as a
signal and what as noise. Although it
remains uncertain what delineates the system, a selective criterion is provided
by specifying the substance of the communication.
Note
that we have introduced the concept of substantive "meaning":
variation can only be interpreted as meaningful information if one has a
(sometimes implicit) expectation of what will count as a signal. Events in this specific dimension can then
be used for the update. In a research
design, one explicitly specifies this expectation, and thus the update can be
made reflexively. Meaning requires the
introduction of an a priori system of reference (e.g., "an
observer") in terms of which the information that has become available in
the event ("the measurement") can be provided with a meaning.
If
one studies human relations, the meaning specified by an analytical observer at
the level of the research design can be different from the one constructed by
each of the observed participants. Human
communication involves both the variation ("uncertainty") and
what this information means. If an
analyst maps into a matrix the various meanings as attributes of the observers
and/or participants, one obtains another matrix containing the communication of
meanings over the columns. (The two
matrices may co-vary.) Thus, if the
subjects under study are themselves reflexive, the research design
"doubles" reflexively, and one obtains a design of second-order
cybernetics.
Meaning and human communication
Reflection is not yet a sufficient
criterion for human communication. An
ordinary reflector or mirror turns an incoming light beam into an outgoing
one. A reflexive system, in general,
generates an output as a function of an input.
In other words, a reflexive system can also be considered as providing
an observed variation with a meaning.
As against ordinary reflexive systems, human observers can be considered
as hyper-reflexive: in addition to providing all observed variation with
meaning by attributing categories to it, human beings are able to communicate
about these reflections, e.g. by using language as a communication medium. Thus, human observers are able to
distinguish the two matrices specified in the previous section in relation to
one another, and if they wished, they could revise either of them by making
other selections. Meaningful
communication contains two kinds of information: information in the sense of
uncertainty and in the sense of meaning. In my opinion, natural languages among other things codify the
relation between information and meaning in human communication: a statement
can have a meaning, and it may contain information.
In
other words: a reflexive system uses its a priori state (i.e., its
momentary structure) as a frame of reference to assess the relevance of the
observable events. By doing so, the
system turns into an a posteriori state with reference to the
observation. This may, in turn, lead to
further actions. Thus, in the case of
reflexive systems we have to extend the two dimensions of the above matrix of
rows and columns by adding a time axis as a third dimension. Matrices at each moment in time add up to a
cube of information over time. The
succession of the reflections may exhibit an observable trajectory within this
three-dimensional geometry (see Figure 2).
Figure 2
An observable trajectory of a
(potentially complex) system in three dimensions
Figure 3
Selection among representations of
the past using a fourth degree of freedom
If
the system itself develops in the way it reflects over time, it contains one
more degree of freedom or, in other words, it has become hyper-reflexive. A hyper-reflexive system operates in four
dimensions; it can revise the meaning given in the past to a disturbance with
hindsight, since it develops in the present (see Figure 3). Both its elements and its operations are in
flux (cf. Swenson 1989). In my opinion,
Luhmann's thesis can be understood as the hypothesis that the social system
evolved to reach this fourth degree of freedom during the individual
revolutions of the 16th and 17th centuries, and that the social communication
system has been developing its hyper-reflexivity with increasing speed ever
since. This hyper-reflexivity
enables the social system to functionalize the communications of which it
consists with reference to its present state and its future options. While a High Culture is presumed to have a
static centre for the reflection, the modern social system is based on the
assumption that reflections are distributed and can be communicated
reflexively.
In
summary, uncertainty is generated when two systems (e.g. an actor and the
network) relate; each co-variation is local and therefore occupies a structural
position in either system at each moment in time. The communication of uncertainty can be provided with a meaning
over time to the extent that the receiving system is reflexive. If the system has an additional degree of
freedom in a fourth dimension (time and space), it can change its perspective
on the position of a local event with hindsight.
The concept of "uncertainty" or
variation in a cell value is equivalent to the definition of information in
Shannon's mathematical theory of communication. However, Luhmann (1984)-in accordance with other
sociologists (cf. Bailey 1994)-focused on the concept of meaningful
information. Although human
communication is specific in its capacity to communicate about meaning, this
should not obscure the difference between the communication of meaningful
information and information that is not yet meaningful. Otherwise, one might lose sight of the basic
operation of non-social, or not yet social communication. The sender generates variation of which only
the part that is communicatable is selected by the network. The receiver selects by providing this
uncertainty with meaning. However, the
messages can be meaningful at some places in the network, and meaningless at
others. Thus, the social system
receives messages in a distributed mode.
The
distinction between information and meaning can be expressed in terms of
"variation" and "selection". The systems disturb one another in their co-variation, which is
observable in terms of events (e.g., actions).
The disturbance, however, is local-it affects some rows and columns,
and not others-and thus it occupies a position in each
system of reference. Because all
distributed systems are constructed, and therefore operationally closed (see
above), some disturbances will be selected and others discarded. Selections can operate on one another: some
momentary selections are sometimes selected for stabilization over time in the
geometry of a three dimensional space.
Each stabilization is local and provisional.
In
a four-dimensional hyper-space, some stabilizations can be selected for
globalization. A global system is
hyper-reflexive since it operates in a hyper-space: it has an additional degree
of freedom with reference to the historical contingency of its genesis, and to
all its instantiations (cf. Giddens 1984).
Consequently, a hyper-reflexive social system provides each event not
with a single meaning, but with a distribution of meanings. Instantiations can be considered as local
stabilizations. They are the momentary
analogues of the longitudinal trajectories or, in other words, they constitute
another geometrical representation of the complex and dynamic system. The hyper-reflexive system, however, is by
definition in flux.
Two
such systems are of particular interest to sociological theorizing: individual
identities and social regimes. In
summary, we have explained how these co-evolving systems are reproduced as
different systems by giving other meanings to the relations in terms of which
they couple structurally.
The observables in sociological theorizing
Unlike a trajectory or an
instantiation, a regime is not observable.
A regime can be considered as a distribution of identities which remain
in flux. Sometimes, the one picture is
more important than the other. In
general, one can observe only its instantiations or its historical genesis from
a perspective, i.e. by using a geometrical metaphor (cf. Haraway 1988). Discursive metaphors, however, may use
different axes for reflection; to the extent that these perspectival axes are
orthogonal, the windows of reflection will be incommensurable. Thus, the semantic confusion in sociology is
predictable following Herbert Simon's (1969) expectation that an emerging
system will be evolutionarily more successful if it achieves nearly
decomposability into orthogonal dimensions.
The
absence of a tangible substratum poses a methodological problem for
second-order sociological theorizing.
While a psychologist may be able to identify a human identity as a unit
of analysis, the sociologist has to be reflexive about the distributed character
of observations. Distributed observations
contain an uncertainty, and therefore one can specify only an expectation. In other words, an empirical account teaches
us about the case which historically occurred, but it does not yet specify the
range of cases which could have occurred.
The account enables us to specify a hypothesis (or perhaps a
heuristics). In this sense, the
second-order programme is radically anti-positivist.
But
how can one test a thesis about a non-tangible substratum? How can one distinguish between mere
speculation and statements with testable consequences? The problem of the inherent constructedness
of the social system is amply reflected within the sociological tradition. Each sociological construction can be
deconstructed by changing the system of reference, for example in terms of the
boundaries of the relevant domains in space and time. In addition to the noted possibility of (nearly orthogonal)
perspectives, the post-modern scepticism about "grand theorizing" has
stimulated a proliferation of partial perspectives. Partial perspectives, however, tend to proliferate the semantics
without methodological control.
Consequently, a major problem in sociology today is one of parsimony: a
methodology should help us to be selective vis-à-vis the wealth of narratives.
Second-order
systems theory provides us with such a criterion. The geometrical metaphors which specify the observations
positively can be formulated in terms of selective-i.e. negative-operations on otherwise noisy data. The selections are expected to change the
shape of the distributions. Thus, one
is able to predict the probability distribution of possible events on the basis
of a theoretical statement, although one is not able to predict any single
event. Additionally, selections can be
formulated as conditions in computer code, and then the systems under study can
be simulated, in principle. The
algorithmic approach of second-order systems theory provides us with options to
combine the various geometrical perspectives in terms of their relative weights. This simulation allows us to specify an
expectation with respect to the possible further developments of the
system. Of course, such a prediction
will be probabilistic, and thus as fallible as a weather forecast. However, the simulation results and the
observed variations may allow us to improve the theoretical specifications
which go into the simulations in a subsequent round.
As
noted, an evolutionary expectation can be specified with reference to the
further development of the various discursive reflections. Scientific discourses reflect on the social
systems under study. Inasmuch as these
systems of discursive reasoning ("the paradigms") are evolutionarily
developing communication systems, they can be expected to develop along the
trajectories set by their historical genesis, and to differentiate along the
different axes of the system under study.
There is no expectation of a "meta-paradigm," since one is not
able to stabilize a geometrical metaphor in four dimensions. But the lowest, i.e. most parsimonious
number of nearly incommunsurable paradigms which can be stabilized in relation
to one another may sometimes prove to be specifiable (Leydesdorff 1994a and
1996).
From observable identities to computable distributions
"Complexity",
"chaos", "noise", etc., continuously irritate all systems
at the places of their local carriers.
Variation is continuously generated in the network by its lower-order
carriers which are expected to operate according to their own dynamics. But only the co-variation with the network
is selected. If this uncertainty can be
provided with a meaning, the uncertainty may be reduced by a further
selection. Reduction of the uncertainty
by a second-order selection contributes to the system's (provisional)
stabilization. Codifications through
recursive selections provide higher-order stabilizations. Each higher-order stabilization remains
provisional, since the configuration develops over time (as in a life-cycle).
In
principle, the constructed distributions are in flux; they do not exist
objectively, but as an expectation of their further operation. In other words, the distribution of a system
itself contains an expected information, i.e. a prediction about the system's
future operation. (This prediction can
be improved if more, e.g. previous, states of the system are known.) The second-order systems under study do not
have to be identified; they can be specified as a difference or a distribution
of a variable to be attributed to cases which can be observed.
For
example, one is able to observe the first-order systems which span the
second-order systems as distributions, and then to study the behaviour of these
distributions over time. By studying
the observable rows of the matrix as sub-dynamics ("actions") of the
communication system, one obtains an expectation at the level of the emerging
system. In other words, the
sub-dynamics correspond to the "genotypes" of the system, while the
second-order results are "phenotypical." Only sub-dynamics can be discursively reflected into a
geometrical narrative without too much confusion.
Of
course, the "phenotypical results" can again be interpreted, and a
second-order matrix can be constructed reflexively. Thus, the systems are constructed in layers of reflexivity. When first-order systems operate, they
produce disturbances which can be provided with meaning by each of these
systems (if they are evolutionarily able to do so). In doing so, they generate a second-order layer of communication. If the systems are hyper-reflexive, they can
shift the focus of their reflection back and forth from the first-order to the
second-order system under study, and thus reconstruct their
reconstructions. Selection upon a
pre-selected distribution potentially closes the system by stabilizing the
representation locally. A further
selection reopens it globally, for example as an expectation of its potential
developments in the future.
The
crucial yardstick for scientific communications-as opposed to integrated belief
systems-lies in the capacity of scientific
discourses to specify such expectations reflexively. This possibility exists in principle since a distributed system
consists only of expectations. At this
stage of cultural evolution, specification of the expectations contained in the
discursive system seems to provide the highest level of reflexivity the social
communication system is able to achieve (Leydesdorff 1994b).
Consequences: Luhmann and beyond
The introduction of a formal
perspective allows us to clarify, for example, theoretical debate between Münch
and Luhmann about "interpenetration" in terms of operational
concepts. However, I wish first to
address a flaw in Luhmann's work about uncertainty and the reflexive communication
of information. This operationalization
enables us to disentangle the various concepts of "interpenetration"
in terms of structural and operational couplings. In a final section, I address "technology" as an
inter-systems relation.
a. the communication of information and meaning
The wealth of heuristic semantics
which Niklas Luhmann has developed over the last decade cannot hide the fact
that the formalization of his systems theoretical perspective has remained
underdeveloped. The various concepts
are not operationalized, and the codification is at some places confused. Luhmann appeals to systems theory to
introduce counter-intuitive arguments, but he warns against the unreflexive
application of biological metaphors from systems or evolution theory to social
systems (cf. Leydesdorff 1993b). In short,
Luhmann has not provided us with means to test whether his statements are more
true than others (e.g., Maturana's), and therefore the reader often has no
option other than to trust the author, or not.
At certain places, Luhmann himself signals this problem. For example, Luhmann (1990) mentions that a
more elaborated systems theoretical notion is needed to clarify inter-system
dependencies. I shall return to this
issue in a later section.
As
noted, the introduction of second-order cybernetics as a methodological
apparatus is not theoretically neutral: it changes the epistemological status
of theorizing. Theories provide us with
"genotypical" specification of sub-dynamics, i.e., with partial
perspectives. In order to proceed from
discursive speculation to analytical models and empirical research, the
substantive theories have to be formulated as conditional statements. In principle, these conditions can be
combined as "do while" and "if then" statements in
algorithmic code, making the observable events testable against a distribution
of possible events. Thus, the analysis
of the history of the current system is only a first step: it provides us with
a description of the co-variations that have been the case. The next step is to specify theoretical
hypotheses about the co-varying systems that potentially explain why the
observed events occurred, and then to develop means to test such hypotheses
against simulations of other possible operations of the assumed system. Only thereafter, one is able to check systematically
(as contrasted to incidentally) for flaws and mistakes in discursive
metaphors.
Luhmann,
for example, has not wished to distinguish between communication, as the
transport of information, and human communication which implies information,
message, and understanding. On the
contrary, he has defined these three operations as the very unit of
communication. But as argued above,
meaning is reflexive upon the information contained in the variation. It requires a second operation by an observing
system, while the observed variation itself is the consequence of a first-order
operation. In human communication,
these two operations are coupled, yet this coupling is not complete but
reflexive, i.e. selective in the time dimension. Some information is provided with meaning, while other
information is discarded as noise.
Sometimes a message can be rich in information, but one is nevertheless
not able to understand it. At other
times, we give meanings to things that are not (yet) based on human
communication.
In
order to "understand" a communication, the receiver needs to
decompose the incoming variation into a signal-i.e. the meaningful information-and noise.
Additionally, human receivers are complex enough to distinguish between these
two dimensions and the contextual position of the information at each
moment. Thus, each reconstruction is
one among a range of possible reconstructions, since the decomposition can be
revised; the understanding is one among a range of possible understandings. But the distributedness of the understanding
should be attributed to the distributed system.
Luhmann
has focused on the (local) understanding of the communication. Thus, he has chosen the reconstruction of
the signal at the receiving actor as his frame of reference. However, actor's understanding is local and
contingent. What does it mean for the
distributed system ("the social network") that the communication is
understood locally? The social system
"understands" the information in the messages in a distributed way,
i.e. not as a (provisionally) stable meaning, but as a global
hyper-meaning. Thus conceptualized, the
social system is one layer more complex than it would be without this
conceptualization. It operates with
four degrees of freedom, not with three.
While Luhmann has distinguished three operations in addition to
self-organization (variation, selection, and stabilization), self-organization
can be understood in our model as the next-order recursion of the
selection. Some local stabilizations
are selected for globalization into a prevailing regime.
In
other words, where Luhmann (1984, at p. 103) has defined information as "a
difference that makes a difference" (Bateson 1972, p. 489), we maintain
that this is a definition of meaningful information. The more parsimonious concept of information
as "a difference" (or a distribution) allows us to understand
distributed systems as themselves containing an expected information. The recursion of the selection should then
be understood in an orthogonal model: the different layers build upon one
another as (nearly) decomposable dimensions without necessary hierarchies. In general, the next level of communication
should not be understood in terms of aggregates of lower-level units, but in
terms of their interactions. The
hierarchical model of relations describes only the special case that order has
(provisionally) been stabilized.
The
relevant differences define the systems under study as distributions. A distribution, however, can be considered
as a probability distribution; variation in probabilities presupposes a
selection from randomness. The crucial
point is the recursivity of the selective operation: a probability distribution
can have a probability, a selection can be selected, etc. What is being communicated is
system-specific ("operationally closed"); how it is communicated is
the subject of a second-order cybernetics. The systems drive one another by
(sometimes even stochastic) variation generated in their relations. This noise can be "locked" (Arthur
1988) into a next-order systems level as a signal if the communicating systems
are able to process the uncertainty with reference to a previous state. Reflexive systems may then be able to
communicate by bouncing the information back and forth, and thus a next-order
level of systems can be generated.
Maturana (1978) has called this evolutionary event the generation of
"a consensual domain."
Hyper-reflexive systems, however, gain one more degree of freedom which
allows them to adjust internally to the further development of the emerging
(next-order) systems level, i.e. to develop further according to their own
rules. Such systems are able to
self-organize their development given disturbances at lower levels and
selective hyper-cycles.
Why
is the distinction between information (uncertainty) and meaning so
important? A systems theoretical
conceptualization which is not sufficiently sophisticated may easily lead to
semantic confusion and scholastic debates.
The partial perspectives may contradict one another. The specification of rational expectations
requires a rich and free discourse, in which there is no place for
"right" and "wrong" on a priori grounds. One should not fight about speculations, but
instead elaborate them into testable hypotheses. (Of course, testing in this context implies the use of
simulations.)
Luhmann
tends not to specify expectations, and to use systems theory without sufficient
precision. For example, Luhmann (1993,
at p. 446) recently specified the relations between sub-systems of society as
"structurally coupled," and therefore he expects them to be
operationally closed. However,
sub-systems belong structurally to a system, and thus they can be operationally
coupled within this system. Additionally,
they are structurally coupled to the carriers of the distribution, i.e. in this
case the actors involved. While the
latter (structural) coupling is operationally closed, operational coupling
between sub-systems requires two kinds of operation, namely one with respect to
the super-system (the network) and another with respect to each of the other
sub-systems (via the network). This
coupling between sub-systems remains a historical variable dependent upon
developments at the system's level.
Consequently, the further development of an emerging operation at an
interface, e.g. in the case of technological developments, can never be
excluded.
b. the problem of "interpenetration"
Luhmann (1984) has indeed specified
the relations between the social communication system and what he calls
"individual consciousness systems" (i.e. actors) as
"structurally coupled:" the social communication system cannot
operate without individuals who communicate, but only the message-the action-is communicated and not the actor. Actors and social (communication) systems
exchange information through interpenetration.
The interpenetration is an event which can be attributed to the actor as
action and to the social system as communication because of the structural
coupling between these systems. As
noted, the social system then has its own dynamics.
In
reaction to this redefinition of "interpenetration," Münch
(1982/1988) has emphasized with reference to Weber's sociology of religion that
"interpenetration" refers primarily to the interpenetration of
subsystems among one another (e.g., to the interpenetration of cultural meaning
and power in society), and with the social system at large, since particularly this
type of interpenetration should be considered as constitutive of Western
modernity (cf. Münch 1982, at pp. 480f.; Münch 1988, at p. 204; cf. Leydesdorff
1993b). In the previous section, I have
specified the interaction between sub-systems as "operational coupling."
Following
Weber, Parsons assumed that "interpenetration" (the relations
between subsystems of society, and the internalization of cultural and social
objects into the personality) can be understood in terms of the same cybernetic
relations among all stable systems of social interaction. For example, as Parsons (1968, at p. 437)
put it:
"The phenomenon that cultural
norms are internalized to personalities and institutionalized in collectivities
is a case of the interpenetration of subsystems of action, in this case social
system, cultural system and personality (...).
Here the critical proposition is that institutionalized normative
culture is an essential part of all stable systems of social
interaction. Therefore, the social
system and the culture must be integrated in specific ways of their
interpenetration."
The
discussion between Münch and Luhmann has revealed that the relations between
the social system and the cultural system differ in important respects from
those between the social system and the personality. Although Parsons noted the specificity of interpenetration among
systems, he did not distinguish sufficiently among the various kinds of
interpenetration. The relations between
subsystems (e.g., the social system and culture) are based not on structural
coupling, yet they are structural: the subsystems are expected to be contained
within a system. Coupling is therefore
of another nature, namely operational: subsystems are expected to update in
relation to one another if this is functional for the system which contains
them.
Münch
(1982/1988) defined the social system as the Parsonian action system. From this perspective the social system
operates by actors taking action. If
one additionally accepts Luhmann's distinction between actors and the social
communication system, action is in itself already a form of
interpenetration. If in action the
subsystems of the social system have additionally to be coupled, the two
subsystems have to be made relevant for one another in the same event. Thus, the communication itself is differentiated,
in addition to being a communication in the two structurally coupled
systems. The event integrates the
specified internal dimensions of the relevant systems operationally, and
the two systems which are coupled structurally; the explanation requires a
cross-tabulation.
One
expects all subsystems of society potentially to resonate in all human
communication. For example, the truth
of a message, or what it may mean emotionally, is often relevant in the
background of a communication. Thus, a whole
distribution of dimensions of the communication should be declared in each
action/communication, although a number of these may have a value of
approximately zero in specific communications.
If we add subsequently the time dimension to this complex, different
frequencies may be involved for the self-referential update within each (sub‑)system. Furthermore, not all actors are expected to
be involved in each update. In other
words, the distributed systems may update with a spectrum of different frequencies
(Leydesdorff 1995c). For example,
relatively small economic transactions can have a cumulative impact on change
in political power-relations, but the latter may go (temporarily) unnoticed for
some of the actors involved. In such a
case, the actors couple structurally with the communication system by acting in
one dimension, but their communications fail to couple operationally to a
second dimension of the social system.
c. Technological developments as inter-system dependencies
Interpenetrations among sub-systems
of the social system may begin to co-evolve if the signal can be bounced back
and forth at the system's level. Thus,
a specific sub-system ("consensual domain") may evolve. However, the constitution of new sub-systems
at the interfaces may change the whole configuration, while sub-systems are
supposed to be functional to the further development of their
super-system. Luhmann (1990, at p. 340)
has indicated that this may have happened more recently in the case of
"technological developments":
"The
differentiation of society changes also the social system in which it occurs,
and this can again be made the subject of scientific theorizing. However, this is only possible if an
accordingly complex systems theoretical arrangement can be specified."
If the categories of the
differentiation themselves change historically, one has to attach a suffix with
a time indicator to the categories.
Soon a calculus becomes increasingly necessary, one that makes it
possible to change both the values of the variables and the categories
themselves, for example by declaring the variables (x) as fluxes (dx/dt). Particularly when studying new technologies,
one needs such more abstract models (cf. Blauwhof 1995). The technological trajectories and regimes (Dosi
1982) can then be considered as consequences of non-linear interactions at the
interfaces between the sciences ("supply") and markets
("demand"). Thus, the
question of the relations between algorithmic modelling and discursive
understanding is most prominent in the discussions about the relevance of
evolutionary economics for technology studies (Leydesdorff & Van den
Besselaar 1994).
What
does the stabilization of an interaction between functionally differentiated
sub-systems mean? At the level of a
single system, stability requires a form of integration. Indeed, an important condition for the
development of modern high-tech sciences seems to be the increasing integration
of political, economic, and scientific orientations in research practices (Gibbons
et al. 1994). Integration in the
sense of de-differentiation, however, would be evolutionarily unlikely, since
the social system would lose its capacity to handle complexity.
Technological
developments can also be considered as the result of inter-systemic resonances
which have been stabilized as new functions of the social system during the
last century. The stabilization of
interfaces and the construction of integration can then be considered in terms
of the sub-dynamics of an emerging higher-order communication system. This higher-order communication, however, is
expected to contain a new epistèmè
as it regime: in addition to the communication of substantive novelty and
methodologically warranted codification ("truth"), high-tech sciences
are able to translate representations from other sub-systems of society into
scientific knowledge, and vice versa, to legitimate research results in
"trans-epistemic" cycles of communication (Knorr 1981). In other words, one is institutionally
warranted to change the code of the communication (for example, because of a
flexible division of labour within the research group).
What
has changed during the last century?
The epistèmè of the modern sciences has been distinguished from
those of pre-modern times in terms of its functional differentiation and the
universalistic orientation of communications.
The modern sciences have been differentiated as quasi-autonomous
sub-systems of society since the 17th century (e.g., Merton 1938 and 1942;
Luhmann 1990). The new systems of
reasoning have distinguished themselves from integrated belief system as
discursive systems of rationalized expectations. Differentiation, however, requires at least two levels of
communication: substantive novelty ("context of discovery") and
metholodogical warrant ("context of justification"). Accordingly, the substance of the
communication has both a value in itself and a function for the emerging
higher-order system of universalistic communication. (In the modern sciences, this super-system is also considered a
universal system.)
From
this perspective, the 18th century can be considered as the age of the
establishment of modern culture and its universalistic discourse (cf. Foucault
1972; Luhmann 1982). The Napoleonic
wars tried to impose the universalistic discourse of the French Revolution onto
the European social system. The early
19th century, however, witnessed the institutional differentiation between
civic society ("the political economy") and the various modern states
(e.g., Marx 1857; Gouldner 1976). After
the revolutions of 1848 and 1870, the national systems became fully
institutionalized and stabilized.
The
"scientific-technical revolution" of the period 1880-1900 (e.g.,
Habermas 1968; Braverman 1974) can then be considered as an inter-systemic
resonance between these functionally and institutionally differentiated
sub-systems of the sciences and modern industry. "Lock-ins" between interacting differentiations are
expected to emerge for merely stochastic reasons (Arthur 1988). These "lock ins", however, occur
locally, and therefore their pattern is socially distributed. Thus, the "wedding of the sciences and
the useful arts" (Noble 1977) was not based on a deliberate design, but
was rather the unintended outcome of a distributed process (Giddens 1984).
If
these processes can additionally be stabilized into co-evolutions, the
resulting patterns can be expected to drive the system into yet higher-order
complexities of communication (cf. Maturana 1978; Nelson 1994). The emerging epistèmè is based on
this interaction of the older differentiation with the institutionally
organized differentiation between the political economy and the post-Napoleonic
state. After the
"scientific-technical revolutions" (1880-1900), however, the patterns
of interaction had still to be established.
From this perspective, the history of science and technology in the
twentieth century can be considered in terms of the exploration of the
potentials for recombining the various sub-dynamics of the emerging system.
While
functional differentiation requires (as noted) the codification of the
communication at two levels, the "high-tech" sciences require
hyper-cycles of communication in at least three dimensions. "Triadic" communication systems (ijk)
are able to encompass the functionally differentiated ones, and to translate
them into one another (ij 6 ik). Thus, they are able
to change the code internally, and by evolutionarily alternating between codes
they may develop stable resonances that reconcile what was considered
irreconcilable from the perspective of the previous epistèmè. If the modern epistèmè was based on
the geometrical metaphor of a univeral panopticum, the new epistèmè is
based on a multitude of partial perspectives, and therefore it requires
algorithmic modelling and dynamic representations (e.g., video-clips).
The
emerging patterns of the high-tech sciences are not expected to replace the
older models, but to encompass them and to guide their future development. The next-order regime entrains the
trajectories on which it builds (Kampmann et al. 1994). In other words, the "big science"
communities are a result of the "epistemic drift" of translations
between economic innovations and research questions; and vice versa, of
the possibility to merge fundamental and applied research questions in terms of
selections of relevant representations (Elzinga 1992). These communication systems contain more
than a single codification, and additionally they are able to translate between
these codifications internally by using a spiral model of communication. Consequently, developments should be
analyzed in terms of processes of representation and communication within
relevant scientific-political-economic communities: high-tech sciences develop
by communicating in terms of recursive selections on interactively constructed
representations (Ahrweiler 1995).
For
evolutionary reasons, one would expect that the regime of the emerging
higher-order network of communications will prove to be more viable than that
of the less complex ones if specific resonances can be sustained
institutionally. As noted, the
emergence of "big science" during the 20th century can be considered
as the institutional acculturation of the new epistèmè of
science-technology-economy. The
reflexive organization of these institutional patterns in new forms of S&T
policies was apparently delayed until the second oil crisis of 1979, when the
post-war system entered into a serious crisis at the level of the global
economy. The gradual emergence of
stable patterns of scientific reproduction in fields like "artificial
intelligence", "biotechnology", and "advanced
materials" in the 1980s indicates the viability of the triadic model of
communication (cf. Van den Besselaar & Leydesdorff 1993; Ahrweiler 1995;
Leydesdorff & Gauthier, forthcoming).
In future work, I hope to elaborate on the co-evolutionary model of this
Triple Helix (Etzkowitz & Leydesdorff 1995).
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