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Self-Organised Criticality (SOC) is a concept developed over the last decade from dynamic systems analysis that aims to investigate the transition trajectories of evolutionary systems. The main emphasis in SOC is on the analysis of the impact of slow exogenous forces in combination with strong localised interactions between single elements or components of the systems concerned, with particular regard to the attainment of a critical state at the macro-level. Against this background, SOC serves to identify the critical conditions from the internal dynamics of the systems that lead to major and significant transformations of their behaviour. The present paper aims to offer insights and reflections originating from the SOC concept, as well as to explore its potential contribution to understanding the evolution of regional economic patterns, in particular the functioning of regional labour markets. In our study, the evolutionary dynamics of employment at a district level in West Germany as well as in the combined West and East German setting will be investigated, in order to detect the possible existence of a power law distribution of growth rates, which may be seen as an indication for SOC at the macro-economic level. In this context, SOC may help to explain the presence of large socio-economic regional fluctuations in a country, and, in general, the dynamics inherent to regional development. [ABSTRACT FROM AUTHOR]

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models-in terms of explanatory variables and NN structures-we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models. [ABSTRACT FROM AUTHOR]