Introduction
The communication of meaning as different from the
communication of information can perhaps be considered as the differentia
specifica of social systems. Whereas biological systems are sometimes able
to provide meaning to information and thus shape a semantic domain (Maturana,
1978; Maturana & Varela, 1980), and human minds can reflexively change the
meaning of information, the capacity to communicate both information and
meaning can be considered as an evolutionary achievement in inter-human
languaging (Luhmann, 2002). When meaning can be communicated, this
communication can further be codified and discursive knowledge also developed.
The issue is beset with conceptual difficulties. First, the distinction
between information and meaning can be conflated by defining information in
terms insufficiently independent from meaning: as “meaningful information” or,
in Bateson’s (1972: 489) formulation, as “a difference which makes a difference.”
Shannon-type information is defined as a series of differences (in a
probability distribution), whereas such differences can only make a next-order difference
for a receiving (or observing) system that is able to provide the information
with meaning. Secondly, “meaning” in the sociological tradition is often
considered primarily as a subjective category and is not sufficiently understood
in terms of inter-subjective communication.
Third, the distinction between the communication of meaning and
the communication of knowledge needs further elaboration. I shall argue that
knowledge can be considered as “a meaning which makes a difference,” whereas
meaning is generated when first-order differences (Shannon-type information) make
a difference for a receiving system. However, the communication of knowledge
requires the relative closure of the discourse in terms of specific codes of
communication.
These issues and distinctions are particularly salient
nowadays, given the emergence of a knowledge-based economy (Foray, 2004;
Leydesdorff, 2006a). Unlike a political economy, which can be considered as
based on interactions among (i) economic exchange relations and (ii)
political arrangements, the additional dynamics of (iii) knowledge-based
communication requires the structural organization of the sciences (Whitley,
1984; Dasgupta & David, 1984). A third structured subdynamics can then be
added to inter-human communication at the level of society. This third
subdynamics potentially disturbs the relative stabilization of political
economies in nations and tends to globalize and meta-stabilize existing market relations
(Leydesdorff & Zawdie, 2010).
The distinction between
information and meaning
Shannon (1948, at p. 3) provided a lucid distinction of
meaning from information at the beginning of his paper entitled A Mathematical
Theory of Communication:
Frequently
the messages have meaning; that is, they refer to or are correlated
according to some system with certain physical or conceptual entities. These
semantic aspects of communication are irrelevant to the engineering problem.
The significant aspect is that the actual message is one selected from a set
of possible messages.
Two systems of reference are distinguished in this quotation:
the formal one of the electrical engineer or, in other words, Shannon himself
as a mathematician, and the possible meanings provided by substantive discourses.
Shannon considers the latter as irrelevant for the definition of information,
but he notes the significance of potentially different selection mechanisms.
Shannon-type information is defined as yet content-free
(Theil, 1972). This condition of “still-to-be-provided-with-meaning” by a
system of reference is also manifested in the units of measurement (e.g., bits
of information) which are dimensionless. Shannon-type information does not yet
contain meaning other than the mathematical definition of the expected
information value contained as uncertainty in a message as a finite series of
differences—in other words, a probability distribution. What the expected information
content of the distribution means can only be defined by an observing system using
its own selection mechanism. Meaning is defined “in use” (Wittgenstein, 1953). Note
that selection by an observing system is deterministic and system-specific,
whereas variation can be stochastic.
The meaning provided to the (Shannon-type) information can sometimes
reduce uncertainty. Reduction of uncertainty can be measured as negentropy
(Brillouin, 1962): this possibility originates from the difference which the
difference (or a series of differences, that is, a probability distribution)
can make for a receiving system. Thus, “a difference which makes a difference” (Bateson,
1972) can also reduce the uncertainty that prevails and be identified as
meaningful or observed information, given the specification of a system of
reference.
Weaver (1949, at p. 116) noted that Shannon’s abstract definition
of information as uncertainty might sound “bizarre,” but that this level of
abstraction might also be needed to develop a theory of meaning. Meaning is
generated in use by specific systems that are able to receive and/or process
meaning. This receiving system, however, does not have to be an observer, but
can also be a discourse. Information is then provided with meaning which may
contain a supra-individual and coordinating function. In other words, the
meaning is codified among human beings, that is, at the intersubjective
level.
The systems-theoretical tradition has focused on the
observer as the individual unit of analysis (e.g., Edelman, 1989). For example,
Von Foerster (1979) ascribed to Maturana as his “Theorem Number One” that
“Anything said is said by an observer,” and added his own corollary that
“Anything said is said to an observer.” He concluded that the two
observers share a language. From this perspective, however, language is
considered only as a meta-biological domain.
Maturana (1978, at p. 49), for example, noted that for an
observer a “second-order consensual domain […] becomes indistinghuishable from
a semantic domain.” However, understanding in language (as both an observer and
a participant; Giddens, 1979) was set aside by him as the language of a human “super-observer”
(pp. 58f.). From this biological perspective, languaging—linguistic behavior—can
be observed. The dynamics of human language as different from communication among
insects cannot properly be analyzed from these meta-biological perspectives (Epstein
and Axtell, 1996).
Human language—and more generally, communication—connects not
only observers, but also their observational reports, that is, the translation
of their observations into communication provides the messaging with
intersubjective meaning, and this codification allows for the communication of subjective
meaning at the supra-individual level (Mead, 1934; Pask, 1975). Distinguishing the
observational reports in language from the observers making these “utterances”
moves us from the realm of mathematical biology and psychology into the sociological
realm of communication as interhuman, that is, meaningful and potentially knowledge-based
coordination.
Language and symbolic
mediation
Meaning is generated in a system when different pieces of
information are related as messages to one another, for example, as words in
sentences (Hesse, 1988; Law & Lodge, 1984). The information is then
positioned in a network with an emerging (and continuously reconstructed) structure.
This positioning can be done by an individual who—as a system of reference—can
provide personal meaning to the events, but meaning can also be provided at the
supra-individual level, for example, in a discourse. In the latter case,
meaning is discursive, and its dynamics can therefore be expected to be
different from those of psychological meaning.
Whereas a psychological identity can be expected to strive to
integrate a plurality of meanings that could be provided to single events (for
example, in order to avoid “cognitive dissonance”), the social system—as a dividuum
(Fuchs, 1998, at pp. 225 ff.; cf. Latour, 2002; Nietzsche, 1878: I, §57)—can tolerate
the entertainment of different meanings, and has the additional option to
differentiate itself into subsystems which codify these meanings differently. This
plurality in rationalities can be functional to the processing of complexity in
a pluriform society (cf. Boudon, 1979; Bourdieu, 2004).
For example, politicians and economists can discuss “shortages
of energy” although among physicists “energy” is considered as a conserved
quantity. Codifications facilitate and speed up the communication by making the
communication system-specific. At the market, for example, one can simpy pay
the price of something without having to negotiate. The price codifies the
value of the commodity. Prices make it possible to abstract from the underlying
values in another semantic domain (for example, that of banking).
This possibility of functional differentiation in the codes
of communication and the potentially symbolic generalization of meaning was first
elaborated in the tradition of social-systems theory by the sociologists
Talcott Parsons and Niklas Luhmann. Building on Durkheim’s argument that norms
function as integrative at the supra-individual level, Parsons theorized the
possibility of functional differentiation among the roles of agents in
different subsystems of society. According to Parsons (e.g., 1961, at p. 41), collectivities
of roles can complement one another in fulfilling various functions in society.
However, Parsons himself did not make a connection to what I consider as his
other major contribution, namely, the theory of the symbolic generalization of
the media of communication (Parsons, 1968). In his scheme, differentiation remains
confined to four major meta-biologically derived functions (adaptation,
goal-attainment, integration, and latency).
Following Merton (1957), Luhmann ([1984], 1995) historicized
social functions and proposed that they develop as specific rationalities in
the different and historically variable processings of meaning in social
subsystems. Luhmann added the evolutionary perspective that new forms of
codification can be invented; for example, at first coins were used, then
banknotes, and much later credit cards. Each communication subsystem develops
further by overwriting and repositioning the previous versions of its coding.
The prime example of this cultural evolution of
communication has been provided by Kuhn’s (1962) notion of paradigm shifts.
“Phlogiston,” for example, was backgrounded in scholarly discourse once “oxygen”
was constructed as a new concept (Priestly, 1774-1777). The new paradigm (in
chemistry) opened domains for puzzle-solving and further communication with a
code that is different from the previous one. However, both “oxygen” and “phlogiston”
use a code of communication very different from exchange processes on the
market, which obey economic mechanisms of exchange. Analogously, the truth of a
scientific statement is different from the religious truth of a dogma. Other
dimensions of interhuman communication (e.g., affection, power) always also
play a role, although the institutional setting may facilitate the
functionality of specific codes among these symbolically generalized media more
than others. For example, it is transgressive to favour one scientific theory over
another for political reasons, or to bribe a judge.
Interhuman communication can thus be considered as a fabric woven
in many directions: each communication can be provided with meaning in terms of
power (Foucault, 1966), economic utility, affection, scientific truth, etc.
These latent dimensions of communication resound and operate selectively in all
interhuman communication. However, in specific communications some selections can
be expected to operate more strongly than others because of the functionality
of coding. Symbolically generalized codes enable us to be specific in our
communications and thus to process more complexity.
The selecting codes of communication are not a given, but enacted
and reconstructed in use as the culturally and therefore supra-individually
constructed dimensions of communication. Historically, the gradual
disintegration of the Holy Roman Empire at the end of the Middle Ages, first in
the Investiture Contest and then during the Reformation, generated an
additional degree of freedom in which the prevailing form of differentiation
among the codes was changed from a hierarchical (stratified) one to another in
which the codes could be reconstructed more freely as a function of the
communication.
The tight coupling between institutions and functions could then
gradually be loosened to the extent that institutions could also be reorganized
in terms of their functions. For example, during the Scientific Revolution of
the 17th century and after the trial of Galileo, scientific truth
could be differentiated from religious truth. Analogously, the trias politica—developed
during the 18th century (Montesquieu, 1748)—regulates the
differentiation among political discourses in modern constitutions. This
functional differentiation in the communication has been institutionalized in national
constitutions since the American and French Revolutions.
Herbert Simon (1973) hypothesized that any evolving system
can be expected to operate with an alphabet. Thus, one might hypothesize 20+ symbolically
generalized media of communication possible in interhuman communications. These
codes of communication should not be reified: they are historically constructed
and enacted bottom-up in interhuman communications, but as they are
reconstructed recursively over time, they can be expected to function as
control mechanisms at the level of society that enable us to enrich our
communication by allowing for greater precision. Note that the “top” or
next-order level is not yet defined or fixed in terms of these bottom-up (re)construction
processes.
The codes operate as selection mechanisms by enabling us to
focus the communication. Selection mechanisms can reinforce one another in
processes of mutual shaping (McLuhan, 1964). Thus, selection mechanisms can be
expected to shape historical trajectories that are relatively stable (for
example, in institutions). A next-order selection may drive a local
stabilization into global meta-stabilization, or into regimes which function
with dynamics that differ from—since they counteract as feedback mechanisms on—the
dynamics of historical developments. Such further differentiation among selection
mechanisms (stabilization and globalization) can uncouple the communication
reflexively from the historical process in which it emerges.
For example, communication with money first speeds up the
communication on the market to the extent that local forms of capitalism can be
shaped. Bank notes, stock exchanges, and credit cards provide means for
worldwide transactions with correspondingly increasing speeds and precision. Marx
already identified this emerging mechanism in capitalism as “alienating.”
Luhmann ([1984], 1995) proposed to study the dynamics of communication (cf. Marx’s
“exchange value”) as analytically different from the dynamics of human or group
behavior (“use value”). The systems of reference are altered by the change of
perspective caused by the newly emerging code of communication. This potential
globalization is an attribute of the communication and not of the communicators.
In the terminology of autopoiesis theory (Maturana
& Varela, 1980), the two dynamics of processing meaning—at the level of
agency and at the social level—remain “structurally coupled” and
“interpenetrate” each other reflexively (Luhmann, 2002). However, one can
expect the cybernetics of communication to be different from the dynamics of
human (group) behavior. Communications, for example, can travel worldwide
without the communicators as carriers having to move.
Horizons of meaning
Interhuman communication is based on interactions among both
communicators and communications. The meanings interact in a non-linear
dynamics which is not hardwired and therefore no longer necessarily subject to
the second law of thermodynamics. The redundancies generated by the processing
of meaning, for example, can structure and reconstruct the information
processing from the perspective of hindsight. The possibility of such
non-linear dynamics is enabled by language as an evolutionary achievement:
meaning can proliferate discursively at a speed much faster than its
instantiations in language (e.g., in fantasies and wishes; cf. Weinstein and
Platt, 1969) because of the possible feed-forward loops between individual
experiences and expectations, and communication in language.
Whereas the biological autopoiesis processes the
history of the communications in terms of their structural sediments—for
example, in terms of differentiations among organs or species—the orientation
toward horizons of possible communication is provided by the additional communication
of meaning in language. The linguistically or symbolically mediated
communication channels are changed by the historical communication in terms of
the communications possible thereafter. In other words, redundancies—sets
of possibilities—are generated. Unlike the biological autopoiesis of the
living (Maturana & Varela, 1980), meaning can be communicated reflexively
and with reference to and in anticipation of “horizons of meaning.” I use the
plural of this Husserlian concept in order to emphasize that one can expect the
horizons of meaning to be structured by symbolically generalized codes of
communication.
Luhmann (1986) criticized Schultz’s (1932; 1953)
interpretation of Husserl’s phenomenology because of the emphasis on observable
instantiations of the cybernetics of behavior in the life-world (cf.
Habermas, 1981; Habermas & Luhmann, 1971). Husserl himself had formulated his
philosophy as a “transcendental phenomenology” with an emphasis on expectations
(as different from observations). From the perspective of hindsight, this focus
on intentions, meanings, and expectations can be considered as “mathematical”
in the sense that it enables us to intuit other possible dimensions which
resound in the empirical events that have happened to occur historically (Derrida,
1974; Heidegger, 1962; Husserl, [1935/6] 1962). We have no access to the
possible other than by placing what exists reflexively between brackets.
Husserl used the Greek word epochè ( ’εποχη) for this “suspension” of all judgments about the
existence of an external world.
The analytical specification of expectations before
proceeding to actual observations enables us to specify whether observed
differences (variation) can be considered as significant. This is formalized,
for example, in the chi-square test of statistical significance using a theory
of measurement. Husserl (1935/6) noted that the positivistic focus on
observables had eroded this basis of the modern sciences, and that one should instead
return to the reflexive position of Descartes, but reconstruct it in order to
ground the social sciences in reflexivity.
Not incidentally, therefore, Husserl (1929) called the book
in which he explained his intersubjective “horizons of meaning” Cartesian
Meditations. Husserl’s reference is to Descartes’ distinction between res
extensa and res cogitans. The Cartesian Cogito knows
him/herself as uncertain and different from the external world. In this act of
doubt, the contingent Cogito finds the transcendent environment as the Other
or a personal God. However, Husserl doubted this next step: the cogitatum
of the Cogito is not necessarily God, but can also be considered as an
intersubjective domain to which we all have personal access: the horizons of
meaning that we share (to different extents). This domain is not in the res
extensa, but remains res cogitans. In other words, the meaning that
we provide to the events does not “exist” physically, but incurs on us as one
among a set of culturally possible meanings.
In the social sciences—as in the other theoretical
sciences—one can use models to specify the expectations. The specification of
expectations makes future states available in the present as potentially
meaningful. The model—as different from the individual intuition—enables us to
communicate about these future states with greater precision by invoking the
symbolic codes of scholarly discourse. A model thus is part of discursive
knowledge; it can be improved by argumentative contributions. However, this
providing of new meaning is highly codified; only those who understand the
model can contribute meaningfully. The model enables these participants to
entertain a communication among specialists in which further knowledge can be
developed and exchanged. In other words, the model is part of a communicative
reality.
This communicative reality that the communicators shape over
time and reflexively reconstruct cannot be considered as res extensa,
but belongs to the res cogitans; it is not stable like matter, but
remains in flux like language. It enables us to communicate in terms of
uncertainties (e.g., possibly relevant questions) and expectations. Husserl
(1929) recognized this realm as cogitatum, that is, the substance about
which the Cogito remains uncertain. Our mental predicates provided to
the world in intersubjective exchanges with intentional human beings, shape our
culture and therewith ground what Husserl also called a “concrete ontology” or,
in other words, “a universal philosophy of science” (1929, at p. 159).
This philosophy of science enables us to understand
scientific models and concepts as specifically coded meanings that we attribute
to an external reality at the intersubjective level. Note that from this
perspective, the external world is not a social construct as in post-modernism;
it is a cognitively hypothesized and highly codified construct that can be
accessed reflexively and perhaps partly reconstructed by individual agency, but
only in terms of further communications. In the res extensa, resources
can be mobilized (for example, at the institutional level), but such policies can
succeed only insofar as they enable us to access, deconstruct, and reconstruct the
self-organization of cultural constructs in the res cogitans.
The models and not the modeled substance shape the sciences
as cultural artifacts. According to Husserl’s (1935) The Crisis of the
European Sciences, however, an empiristic self-understanding prevails in
the modern sciences. In order to move the social sciences forward, one has to
stay with the transcendental that one can retrieve in one’s self and thus recognize
the sciences as part and parcel of a realm of cultural expectations that can be
communicated. The “naturally” observed or perceived at the individual level is shaped
by and rewritten in a realm of intersubjective expectations and their possible
communication. Note that this res cogitans is also res, that is,
real, and thus a possible subject of empirical investigation. The predictions
on the basis of the models, for example, can be expected to feed back (e.g.,
technologically) on our material life.
The structuration of
expectations
The scientific model as an exchange mechanism of cognitive
expectations can provide us with a heuristics to understand the communication
of other, for example, normative expectations. Normative exchanges can be
expected to shape, for example, political discourse. In political discourse,
events are provided with meanings that differ from those given be scientific
discourse. In other words, social order is not a given, but a set of variously codified
expectations that interact and self-organize in the res cogitans. This
order of expectations can be sustained by institutions which function as
instantiations (Giddens, 1984). Political discourse, for example, can be focused
in a parliamentary debate, whereas scholarly discourse can be retrieved in
scientific journals. The complex and internally differentiated order of
expectations remains latent; the instantiations can be considered as their
co-variations at specific moments of time.
The codes can be considered as the latent dimensions that
structure the discourses in analytically different directions. Two levels can
be distinguished in this structuration: the codes operate as the internal axes
of meaning-providing structures. This operation is recursive: meaning is
provided to the information contained in the events, and meaning can further be
codified—that is, provided with symbolic meaning—in the communication. Under
the condition of functional differentiation the axes can be expected to span
horizons of meanings in increasingly orthogonal directions. Some codes can in a
next selection be generalized symbolically or, from the perspective of their
stabilization along trajectories, be globalized as horizons of meaning that feed
back on the local meaning processing in interhuman communication.
Luhmann (1986) provided a thorough elaboration of Husserl’s
concept of “intersubjectivity” in sociological terms. Three levels were distinguished
in the communication of meaning: (i) local interactions, (ii) organization
of meaning in historical instances, and (iii) the self-organization of
the codes of communication. In modern, pluriform societies self-organization in
different directions can be expected to prevail—communications are no longer
coordinated at the center—while in pre-modern societies communication was
organized in terms of institutions. Organization integrates, while self-organization
tends to differentiate the functions of the communication. Organization
operates at specific moments of time—according to Luhmann (2000), by making decisions—and
self-organization of meaning operates over time as codification in fluxes of
communication.
The two cybernetic mechanisms of organization (integration
at interfaces) and self-organization (differentiation of codes) can be
considered as the woof and warp of the evolution of the cogitatum in a
multidimensional space. This evolutionary development is driven bottom-up by variation
in the interactions, while the codes operate in terms of selection mechanisms. For
example, economic exchanges are organized in terms of local markets, but can
self-organize a global market equilibrium if left sufficiently free to do so.
Scientific communication is organized within communities and institutions, but
these communities compete in hypothesizing and following the dynamics at the
level of scientific fields (Bourdieu, 1976, 2004). Love and affection can be
organized in terms of marriages, but also otherwise (Luhmann, 1982). Organization
of meaning is historically contingent.
The factor model and latent semantic analysis
Functionally differentiated codes of communication cannot be
observed directly, but their operations can be reconstructed reflexively as latent
variables. Not incidentally, it was Paul Lazarsfeld, Merton’s colleague at Columbia University, who first made the connection between the latency of communication and
the factor model. A communication network can be rewritten as a matrix. The
matrix representation of this network allows us to decompose the main
dimensions of a communication system using factor analysis. In other words, the
network is composed in terms of relations, but it contains a structure (technically,
a so-called “eigenstructure”) in which every relation can be positioned. Factor
analysis enables us to investigate this latent structure in terms of the
so-called eigenvectors of the matrix.
For example, words can be considered as meaningful information
when they are placed in relation to one another in sentences. The sentences can
be expected to contain meaning because information is specifically related.
(Note that this can also be done by an observer, but my focus here is on
language as communication.) A textual unit can be provided with specific
meanings, for example, in scholarly, political or other discourses. The
symbolically generalized codes operate as next-order selection mechanisms
shaping, for example, paradigms. Three selection mechanisms can thus be
hypothesized as operating in parallel: a first one positioning the relational information
in an observing system (at each moment), a second one coding meaning in
potentially different directions (over time), and a third one potentially
globalizing the stabilizations of meaning in terms of different codes of
communication (Lucio-Arias and Leydesdorff, 2009a).
At first, this problem may seem intractable: when both the
variations and the (factorial) structures change over time, one obtains a
system of partial differential equations with no easy solution. However, the
problem can be decomposed. The factor model can bring latent dimensions into
view. By reorganizing the data in this multi-dimensional space, one can draw a
semantic map in terms of the observables (e.g., words). The organizing
principles (that is, the dimensions) remain latent, but can, for example, be
penciled into the map.
Figure 1: Semantic map among 56 title words connected
at cosine ≥ 0.1 among 149 titles of documents in Social Science
Information 2005-2009.
Figure 1, for example, shows a semantic map using the title
words of 149 documents which were published in Social Science Information during
the period 2005-2009. In these titles, 69 words occurred three or more times,
of which 66 were related in the normalized word-document matrix
at a level of cosine > 0.1.
Factor analysis of this word-document matrix enabled me to color the main
dimensions into the map as shown here for the first three factors with red,
green, and blue, respectively. (These first three factors explain in this case only
10.8% of the common variance).
Note that this is not a relational map as in social network
or co-word analysis. The words “science” and “social,” for example, co-occur most
strongly by far in this domain of titles, viz., 12 times. However, the
similarity in the distributions of occurrences among “capital” and “trust”
(which co-occur only four times) is much greater than that between “science”
and “society”. In this dataset, the relation between “trust” and “capital” is more
important—as a correlation in the semantic space of correlations—than the
relation between “science” and “society.” Technically, this semantic space can also
be called a vector space, as against the relational space of the observable network
of relations. A vector space is generated when a network of relations is
spanned from which an architecture necessarily emerges.
One can take this reasoning one step further and also position
the factors (or so-called “eigenvectors”) in the vector space. Figure 2 shows
the result of this projection for the same data. The (orthogonally rotated)
factor matrix is used as input to a network visualization program. (Negative
factor loadings are indicated with dotted lines.)
Figure 2: Three-factor solution of 40 words in titles
of 149 documents published in Social Science Information 2005-2009 (words
with factor loadings between -0.1 and 0.1 excluded; Fruchterman &
Rheingold, 1991).
Figure 2 enables us to designate more easily the three
specialties involved: Factor 1 focuses on issues of organizational sociology,
Factor 2 on ethnological studies, and Factor 3 on migration studies and the
Mediterrenean. In other words, these clusters are reconstructed as
representations of the three dominant repertoires publishing during this period
in Social Science Information. Note that there are no positive correlations
between Factor 1 and Factor 2. Between Factor 1 and Factor 3, however, the
words “Role” and “Capital” provide articulation points; between Factors 2 and 3
the word “Ethology” is an articulation point between the star-formed graphs
representing the discursive differentiation. The various discourses are organized
within the context of this journal.
The knowledge-based dynamics
Factor analysis provides a static representation of the
multi-dimensional space in an instantiation; in the case above, this was the
aggregate of a five-year period. Dynamically, the next question is whether
these factors of meaning organization are integrated into a single body
of knowledge or whether differentiation is indicated. In the case of
differentiation, a synergy can sometimes be found despite a lack of integration
among the main dimensions. Thus, one can oppose coherence and synergy:
coherence is an indicator of organization, and synergy an indicator of self-organization.
Using this distinction, the variables (words) and structures (factors) are
considered as a system, and thus a systemic measure is needed.
Mutual information in three or more dimensions—sometimes
called “configurational information”—can be used as an indicator of the
potential synergy (e.g., Abramson, 1963; Ashby, 1964; Jakulin, 2005;
Leydesdorff & Sun, 2009; McGill, 1954; Ulanowicz, 1986). Although this
information measure is expressed in bits, it is not a Shannon-type information
(Krippendorff, 2009; Yeung, 2008, at p. 59). However, Garner & McGill
(1956, at p. 227) suggested that the information measure is more useful than
the interaction variance which behaves similarly, but assumes a normal
distribution.
Both interaction variance and mutual information in three
(or more) dimensions can be either positive or negative (Garner & McGill,
1956; Leydesdorff, 2010d). Negative values indicate a reduction of uncertainty at
the systems level. This reduction cannot be expected historically (because the
second law is equally valid for probabilistic entropy),
but has to be self-organized within the system against the axis of time. A decrease
in uncertainty (that is, a negative value for this entropy) means that redundancy
generated by evolutionary self-organization in the globally organized
knowledge base is prevalent over the historical organization. Note that
these are systems measures which cannot be attributed to individual components
without using aggregation rules (Leydesdorff et al., 2006; Leydesdorff
& Fritsch, 2006; Theil, 1972).
The mutual information among the three factors in the system
of title words studied above was +50.6 millibits. The positive value indicates
that the synergy among the three factors is not found in the historical
organization and integration in the titles of the journal. When the analysis,
however, is repeated for the 187 documents that cite one of the 149 documents
published in Social Science Information, I obtain Figure 3, and mutual
information in three dimensions of –106.2 millibits of information.
Figure 3: Three-factor solution of 40 words in the
titles of 187 documents citing Social Science Information 2005-2009
(factor loadings between -0.1 and 0.1 excluded; Fruchterman & Rheingold,
1991).
The citing articles belong to different literatures: Factor
1 indicates evolutionary theorizing, Factor 2 represents studies about
technology and society, and Factor 3 studies about evolving systems. “Digital”
and “Evidence” function as articulation points between Factors 2 and 3, and
only “Approach” between Factors 1 and 3. Although these three citing
literatures are not integrated, a synergy in their differentiation is indicated
by the negative value of the mutual information in three dimensions (Lucio-Arias
& Leydesdorff, 2009b).
In other words, the evolutionary differentiation is reduced
in the organizational integration of title words in Social Science
Information. In addition to this organization, the self-organization can be
made visible in a map as differentiation because it is instantiated. However,
the differentiation develops over time and has therefore to be measured algorithmically.
The dynamic development of the virtual structure can thus be indicated using these
information measures.
Figure 4: A layered process of codification of
information by the processing of meaning, and the codification of meaning in
terms of discursive knowledge. (Adapted from Leydesdorff (2010a), at p. 405.)
Figure 4 summarizes my argument hitherto. At the lowest
level (I), one can use the results of measurements. For example, one may use
data from questionnaires about what a stimulus means to respondents, or—closer
to our example—relations among articles such as citations, co-authorship
relations, or shared co-occurrences of words. The distributions of observable
relations contain expected information about the social and intellectual structures
operating upon this data. Organization reduces uncertainty in the data at each
moment of time; self-organization generates an overlay of expectations that can
feed back on the present and thus potentially reduce uncertainty within the system.
The structuring of the information processing is provided by
positioning the information as in a factor model (level II). Meaning is
generated in the recursive relations among information contents. Structuration
is provided in terms of (next-order) codes of communication (level III) which can
be used to relate different meanings. Whereas the positioning of the
information at level II takes place at each moment of time, structuration (at
level III) is based on the development of the organizing structure over time.
The operations over time can be structurating because the relevant data is
structured at each moment of time. Structures are reproduced and modified along
trajectories, and restructuring can be reinforced or counteracted upon at a
next-order regime level.
The algorithmic model of meaning processing
I noted above that meanings incur on events. In other words,
events are understood from the perspective of hindsight, but with reference to
possible future events. The time axis is thus a crucial dimension. It is often
pictured as an arrow flying from the past via the present to the future, but
the retrospective perspective of hindsight also operates in the processing of
meaning, and thus time can be considered as yet another dimension or a degree
of freedom. A model can provide us with a prediction about future states because
it remains in a res cogitans in which future moments of time can be simulated
in the present.
The mathematical biologists Rosen (1985) defined as
“anticipatory” a system that is able to entertain a model of itself. The model can
provide the system with one or more representations of future states in the
present. These representations can be used for the active reconstruction of the
system. Dubois (2003, at pp. 112f.) further distinguished between weakly and
strongly anticipatory systems: a system that is weakly anticipatory is able to
use its predicted states at future times for adaptation or intervention, while
a system which is strongly anticipatory can use its anticipated states at time t
+ 1, t + 2, etc., for its present reconstruction.
Within this category of strongly anticipatory systems,
Dubois (1998) further distinguished between incursive and hyper-incursive
systems. Incursive systems use both their historical states and present or future
states for their reconstruction, whereas hyper-incursive systems operate
exclusively on the basis of expectations. In Leydesdorff (2009, 2010b), I described
the cogitans as an embodied system that uses also historical states for
the computation of a next one (in the present), whereas the social system or
Husserl’s cogitatum uses only future states, that is, expectations and
their organization in systems of expectation. Such hyper-incursive cybernetics
would operate against the axis of time and thus reduce uncertainty. Note that
this is not yet a system, but a mechanism which requires anchoring in
historical time by other (incursive) mechanisms.
As noted, a cogitatum cannot be instantiated without
a cogitans to instantiate it; the two systems are structurally coupled
and intertwined by reflexive interpenetration. Thus, there is always historical
production of uncertainty in the “life-world” involved, but this forward arrow
is counteracted by a feedback arrow from the self-organization of the codes. The
latter is an evolutionary mechanism that operates in history, but against the
arrow of time because redundancies are generated.
A hyper-incursive system cannot exist and be observed in the
res extensa. The definitions are analytical and should not be reified in
an external world. In other words, these are relevant subdynamics for the
specification of the dynamics of communication of meaning and knowledge.
However, the domains to be studied from this perspective are very different
from biological or even psychological ones (Giddens, 1979). For example, it
could be shown (Leydesdorff & Franse, 2009)—using the logistic equation
(which can be used for modeling processes of growth and decline in biology) and
its equivalent formulation in the hyper-incursive domain—that the biological
and sociological domains are separated at the value of four of the so-called
bifurcation parameter, and that this separatrix can only be crossed by invoking
an incursive routine (that is, a psychological cogitans or human
agency).
This is not the place to repeat the derivation of the various equations
(Leydesdorff, 2009, 2010b). My crucial point is that the three cybernetic
mechanisms specified by Luhmann can be operationalized as different mechanisms
of incursion and hyper-incursion. When the various equations are solved, the
conclusions are the following:
Interaction—hyper-incursively
modeled as the interaction of two mutually expected selection mechanisms:
—leads to
turn taking in the communication of meaning and thereby variation from the
perspective of the social communication system. By extending this model parsimoniously
only with a single (third) selection mechanism, one obtains two options which
model self-organization and organization of meaning, respectively.
When three anticipatory sources of variance can operate selectively upon one
another, this might be modeled as follows:
(1)
Organization instantiates the interfaces historically, and one can analogously
model this as follows:
(2)
The difference between the organization of meaning (Equation 2) and its
self-organization (Equation 1) is provided in the third term: is the processing
bent back to the present, or does this term in the model remain a reference to
a future state? Organization can reduce uncertainty by instantiation in the
present.
Equation 1 (modeling self-organization) has two imaginary roots and
one real root. The real root can be considered as a constant operating in the
coding. Equation 2 models a system (organization of meaning) that can be
expected to perish after a finite number of historical instantiations. In other
words, organizations of meaning emerge and disappear historically. Long-term stability
is provided to the system meta-historically by the evolutionary mechanism of
self-organization in the communication of meaning. The third mechanism—interaction—provides
variation. Organization and self-organization are coupled to each other as
Equations 1 and 2, but the results of these two mechanisms operating provide
different solutions.
Conclusion: society as not a system
The metaphors of systems theory have been tainted by the
biological discourse from which they emerged. Unlike biological systems,
however, a social system cannot exist; the various cybernetics
reconstruct orders of expectations that can be accessed reflexively. In an
email conversation, Krippendorff (June 9, 2010; at https://hermes.gwu.edu/cgi-bin/wa?A2=ind1006&L=cybcom&F=&S=&P=1565)
suggested to distinguish between systems theory as a meta-biological
generalization and the specification of cybernetic mechanisms for explaining
social order and communication in language.
For example, the metaphor of “self-organization” suffers
from the biological origins of this theorizing (Maturana & Varela, 1980). The
“self” needs quotation marks because an order of expectations cannot be
expected to contain an identifiable self; it remains an order of distributions
that operate on one another. Luhmann ([1984], 1995), for example, proposed
distinguishing among the social, the temporal, and the substantive as three dimensions
in which these distributions can be extended (Latour, 2002). The uncertainties
contained in the distributions can over time be considered as expectations. Thus,
the modeling of the communication of meaning and information is deeply
entrenched.
My insistence on specifying expectations in the social
sciences is not meant to imply that sociology is an impossible—since
non-empirical—science about non-observables. Uncertainties can be hypothesized,
and we explored the various mechanisms that can be specified with reference to
expectations. The specification of expectations enriches the research design
beyond a behaviouristic focus on agency and institutions. Understanding and
interpretation of language and knowledge require reflexive communication.
These cybernetic mechanisms need to be specified in a
mathematical language that is yet content-free such as the language of
information theory and cybernetics, since otherwise one is easily deluded by the
biological or sometimes psychological connotations of the metaphors. One is in
need of fresh metaphors, such as Giddens’ (1979) notion of “structuration”
(Leydesdorff, 2010b). Sociology becomes fundamentally different from biology and
psychology as soon as one begins to focus on the communication of meaning and
knowledge. In this context, one should remember Luhmann’s dictum that society
is not made up of human beings, but constructed in terms of their
communications (Luhmann, 1996; cf. Marx [1858], 1973, at p. 265).
Unfortunately, in some of his later writing Luhmann (e.g.,
1997) departed from these sociological assumptions and succumbed to the
attraction of developing a general theory of observation. This is countrary to
the sociological project, in my opinion, because a meta-biological assumption
is introduced (Habermas, 1987; Leydesdorff, 2006b and 2010c). Following these
later writings of Luhmnann, one would leave the deeply humanistic appeal which
transpires from Husserl’s philosophy in favour of considering the social as an
automaton in which the Greek gods operate in the disguise of “performative
media of communication.”
I have wished to argue that symbolically generalized media
of communication codify our expectations and thereby empower our performances
reflexively in terms of handling complexity in terms of expectations. Performance,
however, remains an attribute of human agency. The reflexive understanding of
horizons of meaning made possible by communication provides us with access to a
social reality in which knowledge-based anticipations play an increasing role.
The (self-)organization of meaning at the above-individual level no longer “structurates”
only our actions (Giddens, 1979, 1984), but more importantly our expectations.
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return
. The routine for generating
these semantic maps can be found at