PowerPoint Presentation at the conference of International Society for Information Studies, Vienna, 3-7 June 2014; Session: Integration of the Philosophy of Information and Information Science

1. Mutual Redundancies and Triple Contingencies
among Perspectives on Horizons of Meaning
Loet Leydesdorff
University of Amsterdam,
Amsterdam School of Communication Research (ASCoR)
loet@leydesdorff.net; http://www.leydesdorff.net

2. Information, Redundancy,
and the Measurement of Meaning
1. Information as uncertainty (Shannon-Weaver);
expected information; bits  measurement
2. “A difference which makes a difference” (Bateson);
“A distinction which makes a difference” (MacKay)
→ potentially, reduction of uncertainty; “negentropy”
→ “meaningful information” for a system of reference
(e.g., an observer)
→ redundancy + information = maximal information
→ redundancy as the complement of information

3.  Replace “historically excluded” with “historically not yet
realized”
 (“adjacent others” -- Kauffman, redundancy -- Weaver);
Shift of focus to the instances that could have happened.
Brooks & Wiley (1986, at p. 43): The development of the maximum information
content and the historically realized information over time.

4. Shannon-Weaver model
Shannon (1948, p. 3): “Frequently the messages have meaning; that is they
refer to or are correlated to some system with certain physical or conceptual
entities. These semantic aspects of communication are irrelevant to the
engineering problem.”
Warren Weaver argued that Shannon’s “bizarre” distinction between
information and meaning “has so penetratingly cleared the air that one is
now, for the first time, ready for a real theory of meaning” (Shannon &
Weaver, 1949, p. 27).

5. Cultural domain; Husserl’s “cogitatum”
Correlated in “language”; codingCorrelations: sharing of meaning
Weaver (1949, p.26): “Similarly, one can imagine another box in the diagram
which inserted between the information source and the transmitter, should
be labeled “semantic noise”. (…) And the problem of semantic decoding must
take this semantic noise into account.”
SEMANTIC
NOISE
SEMANTIC
NOISE
Symbolic coding of
the communication
Symbolic coding of
the communication
translations
Relations: transfer of information
SEMANTICSSEMANTICS

6. Luhmann
(Husserl, Parsons, Maturana, etc.)
• Symbolically generalized codes of
communication (Parsons, 1963; 1968):
– For example: money, power, truth, love, etc.;
• Functional differentiation of the codes as the
latent dimensions (“eigenvectors”) in the
communication matrix of senders and receivers
(von Foerster); (→ social system)
• Second-order dynamics because of three layers;
the loops can reinforce each other into
auto-catalysis (Ulanowicz; Padgett & Powell).

7. Layer 1
Networks of communications among communicating agents:
Historical proliferation of the uncertainty (entropy)
Layer 2
Interactions among communications provide meaning to the
communications at the supra-individual (i.e., systems) level
Layer 3
Codes of communication structure the interactions,
at the (next-order) “global” level
Bottom-up:
Historical construction
Top-down:
Evolutionary control
Variation and
selection

8. 1. Selections at
specific moments of
time  networks
3. Some stabilizations are
selected for globalization
2. Some selections are
selected for stabilization
over time
→ Triple Helix of selection mechanisms

9. Two selection mechanisms operating upon each other generate a historical
variation (T12 ≥ 0). Three selection mechanisms operating upon one another can
generate a positive (T123 ≥ 0) or “negative” (missing) variation (T123 < 0).
Correlations are added to the relations.

10. Additive and subtractive color mixing
→ three different perspectives;
→ sharing of meaning; → generation of synergy
relationalpositional

11. Demand Supply
Control
innovation
Source: elaborated from Ulanowicz (2009, p. 1888).

12. Configurational Information:
T123 = H1 + H2 + H3
– H12 – H13 – H23 + H123
→ TUIG is potentially negativeR123 < 0 : reduction of uncertainty; synergy;
R123 > 0 : historical development; exploration
Mutual Information:
T12 = H1 + H2 – H12
T12 ≥ 0; always positive
𝑹 𝟏𝟐 = 𝐻1 + 𝐻2 − (𝐻12+2𝑇12)
= 𝐻1 + 𝐻2 − ([𝐻1+ 𝐻2 − 𝑇12] + 2𝑇12) = −𝑻 𝟏𝟐
R123 = T123
Loet Leydesdorff and Inga A. Ivanova, Mutual Redundancies
in Inter-human Communication Systems: Steps Towards a
Calculus of Processing Meaning, Journal of the Association
for Information Science and Technology 65(2) (2014) 386-399:

13. Supply
(knowledge)
Control
(governance)
Demand
(market)
P
Q
Ps
Pc
Pd
P and Q can be considered as vectors rotating in the three-dimensional space of
supply, demand, and control; as component functions of innovation.
Source: Ivanova, I. A., & Leydesdorff, L. (2014). Rotational Symmetry and the Transformation of Innovation
Systems in a Triple Helix of University-Industry-Government Relations. Technological Forecasting and Social
Change, 86, 143-156.

14. Semantic map among 56 title words connected at cosine ≥ 0.1
among 149 titles of documents in Social Science Information
2005-2009.

15. 1. The mutual information among the three factors in the
system of title words is T123 = +50.6 millibits.
No synergy among the three main factors in the
historical organization of the titles in a journal.
2. When the analysis is repeated for the 187 documents that
cite one of these 149 documents, T123 = –106.2 millibits;
Evolutionarily self-organization of the citing documents
into three disciplinary groupings:
(i) organizational sociology; (ii) ethnology; and
(iii) migration studies.

16. Indicator of synergy
in innovation systems
• The Triple Helix provides us with a model for
measuring the knowledge base of an innovation
system in terms of synergies
• Three sources of variance:
– geographical diversity (endowment);
– technological capacity (infrastructure);
– industrial structure
• Firms as units of analysis

17. (with Øivind Strand,) The Swedish System of Innovation: Regional Synergies in a
Knowledge-Based Economy, Journal of the American Society for Information Science
and Technology 64(9) (2013) 1890-1902.
Statistics Sweden:
N = 1,187,421; November
2011
 48.5% of the regional
synergy is provided by the
three metropolitan areas
of Stockholm,
Gothenburg, and
Malmö/Lund.

18. Chongqing
Beijing
Shanghai
Tianjin
The distribution of 339 second-level administrative units in the PRC compared in
terms of their contribution to the synergy among technology, geography, and
organization.
(with Ping Zhou), “Measuring the Knowledge-Based Economy of China in terms of Synergy among
Technological, Organizational, and Geographic Attributes of Firms.” Scientometrics 98(3) (2014) 1703-
1713.

19. Conclusions
• Relations in the network space versus correlations in the vector space;
dyadic relations versus triple contingencies (→ “triadic closure”).
• Historical relations versus Evolutionary functions
– Historical footprints; variation; observable retention
– Evolutionary differentiation; exploration of new dimensions; R > 0
– possible synergy among selection mechanisms → R < 0
• The selection mechanisms are not given, but socially constructed as
symbolic codes of the communication (res cogitans). They can be
operationalized as evolving eigenvectors of the consecutive matrices.
• The advantages of this approach are operationalization and
measurement!