By Gianluca Carnabuci

ISBN-10: 1423785371

ISBN-13: 9781423785378

ISBN-10: 9056294032

ISBN-13: 9789056294038

Why do definite domain names of human wisdom increase quicker than others? This thesis units out to reply to this query by way of elaborating upon the concept that new rules spring from hybridizations of latest 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 a data area is embedded in at a given cut-off date impacts its next development customers in very important and predictable methods.

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11 Cumulative distribution of backward citation lags Source: Hall et al. 12 shows the frequency distribution of forward citation lags for patents from selected cohorts. The figure provides a useful visual representation of both problems associated with forward citation lags. Because of truncation, the frequency of citations received drops, regardless of cohort, as the distribution approaches the maximum lag possible. Moreover, the more a patent is granted towards the end of the observation period, the more severe is the artificial decline induced by data truncation.

Another reason is that I estimated that models of knowledge growth based on five-year intervals are nearly as accurate as those based on the entire observation period. To reach this conclusion, I reckoned that temporal truncation could induce two kinds of measurement error in my network model. The first kind pertains to the USPTO patent and patent citations data 47 representation of network configurations, and occurs insofar as domains’ citation patterns to older patents are directed to systematically different domains than their citation patterns to more recent patents.

The resulting representation is a time series of directed, valued, and cyclic networks. The number of citations from domain i to domain j determines the weight of the corresponding directed tie in these networks7. Moreover, these networks admit reciprocated ties and self-referential loops (the former denote mutual hybridization between pairs of domains, and the latter denote a domain’s hybridization of ideas belonging to its own knowledge base). As a result of hybridizations, domains advance. In this thesis, I measure domains’ knowledge output following the two most widely diffused approaches in the literature.

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A Theory of Knowledge Growth by Gianluca Carnabuci

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