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

TL;DR

This paper introduces an adaptive, biologically-inspired system using stigmergy and differential evolution to analyze and assess technological innovation trends in regional patent data for smart specialization policies.

Contribution

It presents a novel adaptive stigmergy-based software system that dynamically evaluates technological indicator trends, improving understanding of innovation dynamics for policy support.

Findings

Effective trend assessment in regional patent data

Adaptive system improves detection of critical phenomena

Differential evolution optimizes parameter tuning

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.