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The International Conference on Decision Economics

DECON 2019: Decision Economics: Complexity of Decisions and Decisions for Complexity pp 134–141Cite as

Mapping the Geography of Italian Knowledge

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1009))

Abstract

Nowadays, economic geography and cognitive fields are has besetted with knowledge input and output measurement instead of assessing the quality of the knowledge produced. In this paper, we measure the complexity of knowledge, and map the distribution and the evolution of knowledge in the Italian regions through a neural network approach, based on non-linear clustering with the use of the Self-Organizing Map, and exploring in which way spatial knowledge could be linked to complexity via a relatedness measurement, the Knowledge Complexity Index. Our measurement is based on the analysis of patent data from the European Patent Office, focusing on the Italian context, between 2004 and 2012. We find that knowledge complexity is not homogeneously distributed across Italy. The implementation of this new research line could be helpful in terms of Italian regional diversification because its implementation is still at an early stage at both nation and regional levels and, at the same time, the reduction in the divergence between Regional Innovation Systems in Northern and Southern of Italy will continue to represent the major policy challenge.

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Notes

  1. 1.

    The emergent phenomena could be identified as aggregate economic patterns such as economic growth and inflation, because they derive from the interactions of heterogeneous agents with heterogeneous expectation (e.g. [10]) and bounded rationality (e.g. [18]). As a consequence of these characteristics, this type of economic agent is limited because, according to Simon [15, 16] (i) only limited, frequently unreliable information is available with regard to possible alternatives and their uncertain consequences, (ii) the human mind has a limited capacity to articulate and process the information available; and (iii) only a limited amount of time is available to make one or more decisions.

  2. 2.

    This dilemma affirms that the search for technological rent pushes regions to develop more complex knowledge. However, at the same time, complex technologies remain unattainable.

  3. 3.

    There is a new literature field which considers the patent data system as an obstacle for the innovation economy. According to Mazzucato [11], this obstacle is present in four principal areas: (i) what a patent is, (ii) the patent protection length, (iii) the ease with which patents can be obtained; and (iv) the reasons for seeking patent protection.

  4. 4.

    This index is based on the methods of reflection, developed by Hidalgo et al. [7]. Following their method, we have analyzed the design of the Italian region-tech knowledge network, showing that a region could have a complex technological composition if it generates knowledge that relatively few other regions can imitate.

  5. 5.

    The graph has been build and visualized using the yEd software.

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Authors and Affiliations

  1. Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University G. d’Annunzio of Chieti-Pescara, Viale Pindaro 42, 65127, Pescara, Italy

    Daniela Cialfi & Emiliano Colantonio

Authors
  1. Daniela Cialfi
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  2. Emiliano Colantonio
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Corresponding author

Correspondence to Daniela Cialfi .

Editor information

Editors and Affiliations

  1. Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, Section of Economics and Quantitative Methods, University of Chieti-Pescara, Pescara, Italy

    Edgardo Bucciarelli

  2. AI-ECON Research Center - Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  3. Edificio Parque Científico Universidad de Valladolid, AIR Institute, Salamanca, Spain

    Juan Manuel Corchado

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Cialfi, D., Colantonio, E. (2020). Mapping the Geography of Italian Knowledge. In: Bucciarelli, E., Chen, SH., Corchado, J. (eds) Decision Economics: Complexity of Decisions and Decisions for Complexity. DECON 2019. Advances in Intelligent Systems and Computing, vol 1009. Springer, Cham. https://doi.org/10.1007/978-3-030-38227-8_16

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