A Theory of Knowledge Growth by Gianluca Carnabuci

By Gianluca Carnabuci

Why do sure domain names of human wisdom improve swifter than others? This thesis units out to respond to this query by means of elaborating upon the thought that new rules spring from hybridizations of present principles. over the years, such hybridizations shape a path-dependent community constitution of interconnected and interconnecting wisdom domain names. This thesis develops a network-analytic method of version this constitution and to enquire its results. in accordance with all technological wisdom patented within the usa among 1975 and 1999, this thesis demonstrates that the community place an information area is embedded in at a given time limit impacts its next development customers in very important and predictable methods.

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Unique, classification. e. that portion of the disclosure that, in the opinion of the classifying examiner and according to a set of well-specified criteria, most comprehensively typifies the main inventive concept of the application (Earls et al. 1997). Patent classes have been extensively used to indicate domains of technological knowledge. Powell and Snellman (2004) observed longitudinal patenting rates across patent classes to describe the changing importance of technological sectors. Jaffe, Trajtenberg and Henderson (1993) modeled patents’ technological proximity on the bases of their primary patent classification, a methodology adopted also in Almeida (1996), Jaffe and Trajtenberg (1999), Frost (2001), and Hicks cum suis (2001), and refined by Thompson and Fox-Kean (2004).

I. TIME OF PATENT GRANT To instantiate my network model of knowledge growth, domains’ knowledge output can be measured by means of patent or citation counts, while domains’ hybridization patterns can be tracked back on the basis of patent citations (Chapter 2). However, there is no natural scale within which patenting and 44 USPTO patent and patent citations data citation activities can be quantified. Accordingly, knowledge indicators based on patent and citation counts are meaningful only when used comparatively, and to the degree that appropriate comparisons are made.

KNOWLEDGE OUTPUT Since patens indicate inventions, they are commonly used to measure the advancement of technological knowledge (Griliches 1984; Scherer 1965; Schmookler 1966). The distribution of individual patent contributions, however, is known to be highly skewed: while some patents are groundbreaking inventions, others are valueless. Hence, raw patent counts are but a rough approximation of knowledge output. Numerous authors have discussed the skewness of patent values (Griliches et al. 1987; Pakes 1986; Pakes and Schankerman 1984).

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