Can the Socio-Cognitive Process of Science be Simulated?

Can the Socio-Cognitive Process of Science be Simulated?

Loet LeydesdorffUniversity of Amsterdam,

Amsterdam School of Communication Research (ASCoR)loet@leydesdorff.net

Context of Discovery;

Social Organization of the Sciences

Context of Justification (Popper)

Intellectual Organization of the Sciences (Whitley)

knowledge claims

validationContexts of Discursive Mediation

People; institutions

Texts; journals

Content; theories

Socio

logy

of sc

ience

Scientometrics

Philosophy of science;artificial intelligence

knowledge claims; variation

selection

• Discursive knowledge is developed in communications

codified knowledge• Reflected in texts

potentially entertained by agency• Reproduced in terms of knowledge claims

An order of expectations

Proceed to the simulation

• Computation of Anticipatory Systems - weak versus strong anticipation

- incursive and hyper-incursive(Daniel Dubois; Sander Franse)

• Potential Generation of Negative Entropy in Triple-Helix Relation

(Klaus Krippendorff; Inga Ivanova)

recursive: x(t) = a x(t -1) [1 – x(t -1)]incursive: x(t) = a x(t -1) [1 – x(t)]

hyper-incursive: x(t) = a x(t +1) [1 – x(t +1)]

Example of an incursive system: Technologies develop historically with reference to their previous state;

but are selected on the market in the present

Example: The logistic curve

)1( 11 ttt xaxx 2

11 ttt axaxx 01

21 ttt xaxax

0/12

1 axxx ttt In general, this equation has two solutions: xt+1 = ½ ± ½ √[1 – (4/a) xt]

xt+1 = ½ ± ½ √[1 – (4/a) xt]

xt+1 = ½ ± ½ √[1 – (4/a) xt]

NetSci07

)1)(1)(1( 111 tttt xxxax

)1)(1( 11 ttt xxaxInteraction of meaning: one interface

Self-organization of meaning two anticipatory interfaces

)1)(1)(1( 11 tttt xxxax

Organization of meaning one anticipatory interface and a historical retention mechanism

simulation

simulation

simulation

IG

U

I UI

University

Government

Industry

UG

UIG

G

The three-dimensions of the measurement in a Triple Helix configuration:

eight (= 23) discrete values

Relation to the measurement ?

at http://www.leydesdorff.net/th

local

Mutual Information:

Tij = Hi + Hj - Hij

Tij ≥ 0; always positive

Configurational Information:

TUIG = HU + HI + HG – HUI – HIG – HUG + HUIG

TUIG is potentially negative(cf. spurious correlation)

time time

𝑸(𝚪) = (−𝟏)𝟏+ ȁ�𝚪ȁ�−ȁ�𝑿ȁ�𝑯(𝑿)𝑿⊆𝚪

Krippendorff, K. (2009). W. Ross Ashby’s information theory: a bit of history, some solutions to problems, and what we face today. International Journal of General Systems, 38(2), 189-212.

(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 (in press).

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.

Chongqing

Beijing

Shanghai

Tianjin

Figure: 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.

𝑹𝟏𝟐𝟑=𝑯𝟏+𝑯𝟐+𝑯𝟑+𝑻 𝟏𝟐+𝑻 𝟏𝟑+𝑻 𝟐𝟑+𝑻 𝟏𝟐𝟑

Count the overlaps twice (or more) mutual redundancy

𝑯𝟏𝟐𝟑=𝑯𝟏+𝑯𝟐+𝑯𝟑−𝑻 𝟏𝟐−𝑻𝟏𝟑 −𝑻 𝟐𝟑+𝑻𝟏𝟐𝟑

𝑹𝟏𝟐=𝑯𝟏+𝑯𝟐+𝑻 𝟏𝟐=𝑯𝟏𝟐+𝟐𝑻 𝟏𝟐

𝑹𝟏𝟐=𝑯𝟏+𝑯𝟐−(𝑯¿¿𝟏𝟐+𝟐𝑻 𝟏𝟐)¿

¿𝑯𝟏+𝑯𝟐−¿ ¿

Mutual Redundancy in Two Dimensions

R123 = T123 R1234 = – T1234

R12 = – T12

Source: Brooks & Wiley (1986: 43).

Weaver (1949, p. 26): “Similarly one can imagine another box in the diagram which, inserted between the information source and the transmitter, would be labeled “semantic noise,” the box previously labeled as simply “noise” now being labeled “engineering noise.”

SEMANTIC NOISE

SEMANTIC NOISE

Codes of communication

Codes of communication

P Q

t

t

P P PQQ Q QP

1

2

3

PP P

P

1

2

3

QQ Q

Q

;

t

t

P P P QQ Q Q P

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

1 101 201 301 401 501t →

P1Q1R1

Figure 3: Rotation of the two vectors P and Q for the first component (U = 1) and the consequent development of the contribution of this component to the redundancy R1 (= P1

2 – Q12).

Figure 6: Three components in the generation of redundancy with noise in the fuzzy interval of (0, 3π/2r) added to the third component Rf3: initial values as in Figure 3.

Figure 7: Summation of the three components R123 ( ) = - 3.82 with and without noise in the fuzzy interval of (0, 3π/2r); the coupling coefficient g = 0.2; initial values as in Figure 3.

simulation

REFERENCES:• Ivanova, I. A., & Leydesdorff, L. (2014, in press). Redundancy

Generation in University-Industry-Government Relations: The Triple Helix Modeled, Measured, and Simulated. Scientometrics. doi: 10.1007/s11192-014-1241-7; http://www.leydesdorff.net/redundancy/figures.xlsx

• Leydesdorff, L., & Ivanova, I. A. (2014). 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), 386-399.

• Leydesdorff, L., Johnson, M., & Ivanova, I. A. (2014; in press). The Communication of Expectations and Individual Understanding: Redundancy as Reduction of Uncertainty, and the Processing of Meaning. Kybernetes.

• Ivanova, I. A., & Leydesdorff, L. (2013, in press). Rotational symmetry and the transformation of innovation systems in a Triple Helix of university–industry–government relations. Technological Forecasting and Social Change. doi: 10.1016/j.techfore.2013.08.022.

Conclusions• The scientific model (or paradigm) operates as a

system of rationalized (that is, codified) expectations;

• Codified knowledge can be considered as “a meaning that makes a difference”;

• The only system that can be reconstructed in terms of expectations (counter-factually) is the social system of communications;

• This generates redundancies; other options;• The mechanism of generating redundancy is

further codified in scientific communication.