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

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.Comment: mail: [email protected]

Differential evolution with an evolution path: a DEEP evolutionary algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs

Neo-Schumpeterian Simulation Models

The use of simulation modelling techniques by neo-Schumpeterian economists dates back to Nelson and Winter’s 1982 book “An Evolutionary Theory of Economic Change”, and has rapidly expanded ever since. This paper considers the way in which successive generations of models have extended the boundaries of research (both with respect to the range of phenomena considered and the different dimensions of innovation that are considered), and while simultaneously introducing novel modelling techniques. At the same time, the paper will highlight the distinct set of features that have emerged in these neo-Schumpeterian models, and which set them apart from the models developed by other schools. In particular, they share a distinct view about the type of world in which real economic agents operate, and a invariably contain a generic set of algorithms. In addition to reviewing past models, the paper considers a number of pressing issues that remain unresolved and which modellers will need to address in future. Notable amongst these are the methodological relationship between empirical studies and simulation (e.g. ‘history friendly modelling’), the development of common standards for sensitivity analysis, and the need to further extend the boundaries of research in order to consider important aspects of innovation and technical change.macroeconomics ;

Why is Economic Geography not an Evolutionary Science?

This paper explains the main commonalities and differences between neoclassical, institutional and evolutionary approaches that have been influential in economic geography during the last couple of decades. For all three approaches, we argue that they are in agreement in some respects and in conflict in other respects. While explaining to what extent and in what ways the Evolutionary Economic Geography approach differs from the Neoclassical (or ‘new’) Economic Geography and the Institutional Economic Geography, we can specify the value-added of economic geography as an evolutionary science. Finally, we briefly outline a research agenda of the Evolutionary Economic Geography we like to explore.

Towards a kansei-based user modeling methodology for eco-design

We propose here to highlight the benefits of building a framework linking Kansei Design (KD), User Centered Design (UCD) and Eco-design, as the correlation between these fields is barely explored in research at the current time. Therefore, we believe Kansei Design could serve the goal of achieving more sustainable products by setting up an accurate understanding of the user in terms of ecological awareness, and consequently enhancing performance in the Eco-design process. In the same way, we will consider the means-end chain approach inspired from marketing research, as it is useful for identifying ecological values, mapping associated functions and defining suitable design solutions. Information gathered will serve as entry data for conducting scenario-based design, and supporting the development of an Eco-friendly User Centered Design methodology (EcoUCD).ANR-ECOUS

Social Learning in Market Games

The aim of our experiments is to test the effect of different information settings on firms’ behaviour in duopoly price and quantity games. We find that, when players have full information on their rivals’ choices, the imitation rule prevails and such learning behaviour induces more competitive outcomes in the Cournot market designs. By the same token, when information on the average industrial profit is provided, there is evidence of an increase in cooperation, and the majority of players experiment with new strategies when their payoff falls below the average profit (F. Palomino and F. Vega-Redondo, 1999; H. Dixon, 2000)Learning, Cournot and Bertrand experiments

Money and uncertainty in democratised financial markets

Developments in broad money since the start of the new millennium cannot be explained by the traditional determinants of money demand, namely, income, prices and portfolio effects. Households’ direct and indirect participation in financial markets have led to the widespread democratisation of these markets in the US since the 1970’s. In the pre-democratised era, an increase in uncertainty would have resulted in a fall in the transactions demand for money due to pessimism regarding income and employment prospects. When markets become more democratised, the precautionary, or store-of-value function of money dominates the transactions demand in which case an increase in uncertainty results in a net increase in the demand for money. Our Kalman Filter estimates are consistent with this theory. The money-uncertainty coefficient has been subject to an increasing trend over the whole sample period shifting gradually from significantly negative values up to the mid-to-late-1990s before becoming significantly positive by the early years of the new millennium. There are important repercussions from these new behavioural patterns for both monetary and financial stability which are discussed in this paper.