アライアンス・ネットワークと革新

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

R&D Subsidization effect and network centralization. Evidence from an agent-based micro-policy simulation

This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support

Latent Ties: Reconnection of Organizations to Boost Innovation

This paper highlights the temporality dimension of ties. Doing so we respond to recent calls for a more dynamic and processual understanding of networks. We proposed a model in which two antecedents of latent ties, network similarity, and length of the tie, will be tested. Also, our study addresses the relationship between tie latency and innovation. The model tests the impact of the interaction between the number of latent and strong ties on organizations innovative output will be studied. The study highlights the significance of latent ties in coping with the problems of redundancy of dense network and overloading of new weak ties

A Bayesian Approach to Graphical Record Linkage and De-duplication

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate transitive linkage probabilities across records (and represent this visually), and propagate the uncertainty of record linkage into later analyses. Our method makes it particularly easy to integrate record linkage with post-processing procedures such as logistic regression, capture-recapture, etc. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously record linkage approaches, despite the high-dimensional parameter space. We illustrate our method using longitudinal data from the National Long Term Care Survey and with data from the Italian Survey on Household and Wealth, where we assess the accuracy of our method and show it to be better in terms of error rates and empirical scalability than other approaches in the literature.Comment: 39 pages, 8 figures, 8 tables. Longer version of arXiv:1403.0211, In press, Journal of the American Statistical Association: Theory and Methods (2015

Knowledge flow across inter-firm networks: the influence of network resources, spatial proximity, and firm size

The objective of this paper is to analyze the characteristics and nature of the networks firms utilize to access knowledge and facilitate innovation. The paper draws on the notion of network resources, distinguishing two types: social capital – consisting of the social relations and networks held by individuals; and network capital – consisting of the strategic and calculative relations and networks held by firms. The methodological approach consists of a quantitative analysis of data from a survey of firms operating in knowledge-intensive sectors of activity. The key findings include: social capital investment is more prevalent among firms frequently interacting with actors from within their own region; social capital investment is related to the size of firms; firm size plays a role in knowledge network patterns; and network dynamism is an important source of innovation. Overall, firms investing more in the development of their inter-firm and other external knowledge networks enjoy higher levels of innovation. It is suggested that an over-reliance on social capital forms of network resource investment may hinder the capability of firms to manage their knowledge networks. It is concluded that the link between a dynamic inter-firm network environment and innovation provides an alternative thesis to that advocating the advantage of network stability