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.
