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Register a new userIndividuals, Institutions, and Innovation in the Debates of the French Revolution
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The French Revolution brought principles of liberty, equality, and brotherhood to bear on the day-to-day challenges of governing what was then the largest country in Europe. Its experiments provided a model for future revolutions and democracies across the globe, but this first modern revolution had no model to follow. Using reconstructed transcripts of debates held in the Revolutions first parliament, we present a quantitative analysis of how this system managed innovation. We use information theory to track the creation, transmission, and destruction of patterns of word-use across over 40,000 speeches and more than one thousand speakers. The parliament as a whole was biased toward the adoption of new patterns, but speakers individual qualities could break these overall trends. Speakers on the left innovated at higher rates while speakers on the right acted, often successfully, to preserve prior patterns. Key players such as Robespierre (on the left) and Abbe Maury (on the right) played information-processing roles emblematic of their politics. Newly-created organizational functions---such as the Assemblys President and committee chairs---had significant effects on debate outcomes, and a distinct transition appears mid-way through the parliament when committees, external to the debate process, gain new powers to propose and dispose to the body as a whole. Taken together, these quantitative results align with existing qualitative interpretations but also reveal crucial information-processing dynamics that have hitherto been overlooked. Great orators had the publics attention, but deputies (mostly on the political left) who mastered the committee system gained new powers to shape revolutionary legislation.
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Research institutions provide the infrastructure for scientific discovery, yet their role in the production of knowledge is not well characterized. To address this gap, we analyze interactions of researchers within and between institutions from millions of scientific papers. Our analysis reveals that the number of collaborations scales superlinearly with institution size, though at different rates (heterogeneous densification). We also find that the number of institutions scales with the number of researchers as a power law (Heaps law) and institution sizes approximate Zipfs law. These patterns can be reproduced by a simple model with three mechanisms: (i) researchers collaborate with friends-of-friends, (ii) new institutions trigger more potential institutions, and (iii) researchers are preferentially hired by large institutions. This model reveals an economy of scale in research: larger institutions grow faster and amplify collaborations. Our work provides a new understanding of emergent behavior in research institutions and how they facilitate innovation.
In this paper, we propose a spatially constrained clustering problem belonging to the family of p-regions problems. Our formulation is motivated by the recent developments of economic complexity on the evolution of the economic output through key interactions among industries within economic regions. The objective of this model consists in aggregating a set of geographic areas into a prescribed number of regions (so-called innovation ecosystems) such that the resulting regions preserve the most relevant interactions among industries. We formulate the p-Innovation Ecosystems model as a mixed-integer programming (MIP) problem and propose a heuristic solution approach. We explore a case involving the municipalities of Colombia to illustrate how such a model can be applied and used for policy and regional development.
The statistical relationship between structural capital and innovation in Indonesian manufacturing industries is presented. The correlation is constructed using recent survey data on the contribution of structural capital to the innovation processes in the industries. The correlation is represented quantitatively using the recently developed Intellectual Capital and Innovation (ICI) index involving all components of intellectual capital and its role to enable innovation in a manufacturing industry. However, the paper is focused only on the contribution of structural capital component. Using the available data it is shown that the correlation is highly depending on the scale and characteristics of each manufacture. It is also argued that the ICI index is able to quantitatively prove the dominant components in innovation processes for each class of manufacturing industries.
We study the Japan and U.S. patent records of several decades to demonstrate the effect of collaboration on innovation. We find that statistically inventor teams slightly outperform solo inventors while company teams perform equally well as solo companies. By tracking the performance record of individual teams we find that inventor teams performance generally degrades with more repeat collaborations. Though company teams performance displays strongly bursty behavior, long-term collaboration does not significantly help innovation at all. To systematically study the effect of repeat collaboration, we define the repeat collaboration number of a team as the average number of collaborations over all the teammate pairs. We find that mild repeat collaboration improves the performance of Japanese inventor teams and U.S. company teams. Yet, excessive repeat collaboration does not significantly help innovation at both the inventor and company levels in both countries. To control for unobserved heterogeneity, we perform a detailed regression analysis and the results are consistent with our simple observations. The presented results reveal the intricate effect of collaboration on innovation, which may also be observed in other creative projects.
Taylors law quantifies the scaling properties of the fluctuations of the number of innovations occurring in open systems. Urn based modelling schemes have already proven to be effective in modelling this complex behaviour. Here, we present analytical estimations of Taylors law exponents in such models, by leveraging on their representation in terms of triangular urn models. We also highlight the correspondence of these models with Poisson-Dirichlet processes and demonstrate how a non-trivial Taylors law exponent is a kind of universal feature in systems related to human activities. We base this result on the analysis of four collections of data generated by human activity: (i) written language (from a Gutenberg corpus); (ii) a n online music website (Last.fm); (iii) Twitter hashtags; (iv) a on-line collaborative tagging system (Del.icio.us). While Taylors law observed in the last two datasets agrees with the plain model predictions, we need to introduce a generalization to fully characterize the behaviour of the first two datasets, where temporal correlations are possibly more relevant. We suggest that Taylors law is a fundamental complement to Zipfs and Heaps laws in unveiling the complex dynamical processes underlying the evolution of systems featuring innovation.
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