“Structuration” by intellectual organization: the configuration of knowledge in relations among structural components in networks of science

Using aggregated journal–journal citation networks, the measurement of the knowledge base in empirical systems is factor-analyzed in two cases of interdisciplinary developments during the period 1995–2005: (i) the development of nanotechnology in the natural sciences and (ii) the development of communication studies as an interdiscipline between social psychology and political science. The results are compared with a case of stable development: the citation networks of core journals in chemistry. These citation networks are intellectually organized by networks of expectations in the knowledge base at the specialty (that is, above-journal) level. The “structuration” of structural components (over time) can be measured as configurational information. The latter is compared with the Shannon-type information generated in the interactions among structural components: the difference between these two measures provides us with a measure for the redundancy generated by the specification of a model in the knowledge base of the system. This knowledge base incurs (against the entropy law) to variable extents on the knowledge infrastructures provided by the observable networks of relations

"Meaning" as a sociological concept: A review of the modeling, mapping, and simulation of the communication of knowledge and meaning

The development of discursive knowledge presumes the communication of meaning as analytically different from the communication of information. Knowledge can then be considered as a meaning which makes a difference. Whereas the communication of information is studied in the information sciences and scientometrics, the communication of meaning has been central to Luhmann's attempts to make the theory of autopoiesis relevant for sociology. Analytical techniques such as semantic maps and the simulation of anticipatory systems enable us to operationalize the distinctions which Luhmann proposed as relevant to the elaboration of Husserl's "horizons of meaning" in empirical research: interactions among communications, the organization of meaning in instantiations, and the self-organization of interhuman communication in terms of symbolically generalized media such as truth, love, and power. Horizons of meaning, however, remain uncertain orders of expectations, and one should caution against reification from the meta-biological perspective of systems theory

Learning capability : the effect of existing knowledge on learning

It has been observed that different people learn the same things in different ways - increasing their knowledge of the subject/domain uniquely. One plausible reason for this disparity in learning is the difference in the existing personal knowledge held in the particular area in which the knowledge increase happens. To understand this further, in this paper knowledge is modelled as a 'system of cognitive schemata', and knowledge increase as a process in this system; the effect of existing personal knowledge on knowledge increase is 'the Learning Capability'. Learning Capability is obtained in form of a function; although it is merely a representation making use of mathematical symbolism, not a calculable entity. The examination of the function tells us about the nature of learning capability. However, existing knowledge is only one factor affecting knowledge increase and thus one component of a more general model, which might additionally include talent, learning willingness, and attention

From Computer Metaphor to Computational Modeling: The Evolution of Computationalism

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive (neuro)science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year

Biogeography of Southern Ocean Active Prokaryotic Communities Over a Large Spatial Scale

International audienc

Social Networking Tools for Knowledge Based Action Groups

Urban areas are administratively complex, and bureaucrats are often overburdened when they are working at or beyond what Herbert Simon called the bounds of their rationality [1]. Decisions impacting community group members may be based on little genuin