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:[EMAIL PROTECTED]> [EMAIL PROTECTED] ; <http://www.leydesdorff.net/> http://www.leydesdorff.net/ Now available: <http://www.universal-publishers.com/book.php?method=ISBN&book=1581129378> The Knowledge-Based Economy: Modeled, Measured, Simulated. 385 pp.; US$ 18.95 <http://www.universal-publishers.com/book.php?method=ISBN&book=1581126956> The Self-Organization of the Knowledge-Based Society; <http://www.universal-publishers.com/book.php?method=ISBN&book=1581126816> The Challenge of Scientometrics
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