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This paper introduces a categorization of simulation models. It provides an explicit overview of the steps that lead to a simulation model. We highlight the advantages and disadvantages of various simulation approaches by examining how they advocate different ways of constructing simulation models. To this end, it discusses a number of relevant methodological issues, such as how realistic simulation models are obtained and which kinds of inference can be used in a simulation approach. Finally, the paper presents a practical guide on how simulation should and can be conducted.

The Lisbon strategy for growth and jobs seeks to use knowledge and innovation in the context of the European Research Area (ERA). To build the ERA the European Union (EU) implements - amongst others - regional innovation policy. Ample scientific publications have investigated how innovation drives regional dynamics. Therefore, we assess the goals of European regional innovation policy in the light of the scientific findings, which we collected and condensed by bibliometrical analysis. The general goals of the Lisbon strategy to at the same time stimulate growth and achieve cohesion of economic activities across the EU is not in line with the finding that positive cumulative and self-reinforcing processes go hand in hand with the agglomeration of economic activities. However, the goals of the specific innovation policies for the regional level are mainly in line with the scientific findings.

This paper discusses how the results of simulation models can be made more reliable and the method of simulating therefore more widely applicable. We suggested to calibrate simulation models empirically and developed a methodology based on Critical Realism in order to so. We suggested combining the procedures of two strands of literature: the empirical underpinning of the assumptions (like in microsimulations) and the empirical check of the implications (like in Bayesian inference). Both these strands of literature are mainly concerned with predicting future developments. We, instead, aim to infer statements about causal relations and characteristics of a set of systems or dynamics, such as, e.g., the development of an industry, that have a general validity for this set of systems or dynamics. In other words, instead of deriving probabilistic predictions of the future and statements of the current situation and dynamics of one single system we developed a methodology to gain general statements about the features of systems and dynamics.