Loet Leydesdorff

Inga Ivanova, Oivind Strand, Duncan Kushnir et al.

The Economic Complexity Index (ECI; Hidalgo & Hausmann, 2009) measures the complexity of national economies in terms of product groups. Analogously to ECI, a Patent Complexity Index (PatCI) can be developed on the basis of a matrix of nations versus patent classes. Using linear algebra, the three dimensions: countries, product groups, and patent classes can be combined into a measure of "Triple Helix" complexity (THCI) including the trilateral interaction terms between knowledge production, wealth generation, and (national) control. THCI can be expected to capture the extent of systems integration between the global dynamics of markets (ECI) and technologies (PatCI) in each national system of innovation. We measure ECI, PatCI, and THCI during the period 2000-2014 for the 34 OECD member states, the BRICS countries, and a group of emerging and affiliated economies (Argentina, Hong Kong, Indonesia, Malaysia, Romania, and Singapore). The three complexity indicators are correlated between themselves; but the correlations with GDP per capita are virtually absent. Of the world's major economies, Japan scores highest on all three indicators, while China has been increasingly successful in combining economic and technological complexity. We could not reproduce the correlation between ECI and average income that has been central to the argument about the fruitfulness of the economic complexity approach.

Inga Ivanova, Oivind Strand, Loet Leydesdorff

The knowledge base of an economy measured in terms of Triple Helix relations can be analyzed in terms of mutual information among geographical, sectorial, and size distributions of firms as dimensions of the probabilistic entropy. The resulting synergy values of a TH system provide static snapshots. In this study, we add the time dimension and analyze the synergy dynamics using the Norwegian innovation system as an example. The synergy among the three dimensions can be mapped as a set of partial time series and spectrally analyzed. The results suggest that the synergy at the level of both the country and its 19 counties shoe non-chaotic oscillatory behavior and resonates in a set of natural frequencies. That is, synergy surges and drops are non-random and can be analyzed and predicted. There is a proportional dependence between the amplitudes of oscillations and synergy values and an inverse proportional dependence between the oscillation frequencies' relative inputs and synergy values. This analysis of the data informs us that one can expect frequency-related synergy-volatility growth in relation to the synergy value and a shift in the synergy volatility towards the long-term fluctuations with the synergy growth.

Tobias Hecking, Loet Leydesdorff

Using the 6,638 case descriptions of societal impact submitted for evaluation in the Research Excellence Framework (REF 2014), we replicate the topic model (Latent Dirichlet Allocation or LDA) made in this context and compare the results with factor-analytic results using a traditional word-document matrix (Principal Component Analysis or PCA). Removing a small fraction of documents from the sample, for example, has on average a much larger impact on LDA than on PCA-based models to the extent that the largest distortion in the case of PCA has less effect than the smallest distortion of LDA-based models. In terms of semantic coherence, however, LDA models outperform PCA-based models. The topic models inform us about the statistical properties of the document sets under study, but the results are statistical and should not be used for a semantic interpretation - for example, in grant selections and micro-decision making, or scholarly work-without follow-up using domain-specific semantic maps.