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      The Evolutionary Dynamics of Discursive Knowledge : Communication-Theoretical Perspectives on an Empirical Philosophy of Science 

      Knowledge-Based Innovations and Social Coordination

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      Springer International Publishing

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          Abstract

          Three themes have been central to my research program: (1) the dynamics of science, tech-nology, and innovation; (2) the scientometric operationalization and measurement of these dynamics; and (3) the Triple Helix (TH) of university-industry-government relations. In this introductory chapter, I relate these three themes first from an autobiographical perspective to ( i)) Luhmann’s sociological theory about meaning-processing in communications with ( ii) information-theoretical operationalizations of the possible synergies in Triple-Helix relations, and with ( iii) anticipation as a selection mechanism in cultural evolutions different from “natural selection.” Interacting selection mechanisms can drive the development of redundancy; that is, options that are available, but have not yet been used. An increasing number of options is crucial for the viability of innovation systems more than is past performance. A calculus of redundancy different from and complementary to information calculus is envisaged.

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          A Mathematical Theory of Communication

          C. Shannon (1948)
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            Emergence of Scaling in Random Networks

            Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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              Technical Change and the Aggregate Production Function

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                Author and book information

                Book Chapter
                2021
                January 01 2021
                : 1-35
                10.1007/978-3-030-59951-5_1
                6b016d5d-0be3-4e63-8075-792ddf457df3
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