An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization

A. L. Alfeo, Francesco Appio, M. G. C. A. Cimino, A. Lazzeri, A. Martini and G. Vaglini
Additional contact information
A. L. Alfeo: UniPi - University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie]
M. G. C. A. Cimino: UniPi - University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie]
A. Lazzeri: UniPi - University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie]
A. Martini: UniPi - University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie]
G. Vaglini: UniPi - University of Pisa [Italy] = Università di Pisa [Italia] = Université de Pise [Italie]

Working Papers from HAL

Abstract: Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results.

Date: 2019-09-19
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:halshs-02292365

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