Dear Pedro and collegaues,
Anyhow, my general opinion on the problem of social complexity is that,
like its homonymous biological counterpart, it stands beyond formal
approaches, at the time being. Let us remind the recent exchanges on
"biological computation"... If so, requests to directly algorithmize it,
are ill posed directions: without new approaches to info it cannot be done
meaningfully.
In my opinion, this conclusion is drawn much too early and too much based on
modelling the social system from the perspective of a biologist. Two
important steps (since most relevant for the algorithmic approaches) have
been suggested in which social systems differ from biological systems, at
least in terms of the relative weights of subdynamics:
1. the unit of analysis. Unlike biological systems, social systems are not
aggregates of individuals. Thus, the individual or the aggregate of
individuals (e.g., in people) are not the proper unit of analysis, and the
corresponding requirement of micro-foundation (prevalent in neo-classical
economics) should not be accepted at forehand. The coordination mechanisms
among human beings are generating the complexity. Therefore, communication
(or another mechanism of social coordination?) should be considered itself
as the unit of analysis.
This makes the analysis more complex and more simple. Communications cannot
be directly observed, but one can observe their "footprints". However,
communication systems can be hypothesized and then the specified
expectations can be tested against the data. Furthermore, we have an elegant
apparatus in the mathematical theory of communication (and its elaboration
into non-linear dynamics) for the operationalization. Communications are
distributed, both socially and temporarily. The distributions can be
expected to contain information (which is communicated when the systems
operate).
2. the nature of the operation has to be specified. While
information-processing proceeds with the axis of time, meaning is provided
from the perspective of hindsight. Thus, the axis of time has to be inverted
locally in the model. The inversion can lead to stabilization. This
inversion is reinforced when meaning can also be communicated. This
next-order inversion may lead to globalization.
How does the probabilistic entropy evolve when these feedback mechanisms are
operating on the information-processing. This is studied in computing
anticipatory systems (Rosen, 1985; Dubois, 1998). It is clear by now that
the mechanisms of anticipatory systems are very different from those without
anticipation and that anticipation can be specified in terms of strong and
weak anticiation, leading to different equations.
For example, the anticipatory formulation of the logistic equation does not
lead to chaotic phenomena when the bifurcation paramater approaches the
value of four, as it does in population dynamics. Thus, meaning-processing
systems (like studied in psychologies or sociologies) should not be studied
using a biological model without further reflection.
This is not to say that in mathematical biology, one is not interested in
anticipation and communication. On the contrary, Robert Rosen's work is to
be celebrated! However, one is often not sufficiently aware that at the
level of weakly anticipatory systems like human beings (who can entertain
models of themselves and their environments and make predictions on this
basis) and at the level of strongly anticipatory systems like social systems
which are under specific conditions able to restructure their future (e.g.,
using technosciences), other mechanisms prevail in the complex communication
dynamics then the ones which can be derived from biological systems. The
latter, for example, may exhibit a life-cycle, while a social system is not
born: it emerges using a mechanism different from the underlying one or in
other words as a structural coupling (Maturana).
With best wishes,
Loet
_____
Loet Leydesdorff
Amsterdam School of Communications Research (ASCoR)
Kloveniersburgwal 48, 1012 CX Amsterdam
Tel.: +31-20- 525 6598; fax: +31-20- 525 3681
<mailto:loet@leydesdorff.net> loet@leydesdorff.net ;
<http://www.leydesdorff.net/> http://www.leydesdorff.net/
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Received on Sat Dec 16 15:25:40 2006
