{'fraction_non_alphanumeric': 0.01936026936026936, 'fraction_numerical': 0.005050505050505051, 'mean_word_length': 5.9941176470588236, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The Methodology of Simulation Models: Chances and Risks

1.1 In recent years more and more social scientists and in particular economists have been using simulation models. Without doubt numerical approaches—among them most outstanding agent-based approaches—offer a prolific way out of the tight corset which is determining modelling strategies so far for economists (e.g. Pyka and Fagiolo 2007). Accordingly, simulation techniques are applied almost enthusiastically, because they allow modelling economic problems like e.g. interactions of heterogeneous agents on developing markets appropriately. In particular, economists who dealt with innovation and inherent uncertainty have been interested in the tools provided by simulation models. They were dissatisfied with traditional modelling that required a very restrictive set of assumptions, in particular identical agents deciding rationally, thereby inevitably leading to a deterministic representation of innovation. In contrast, economists modelling innovation were satisfied with simulation models that can emulate heterogeneous agents driving the evolution of economic systems and allowing the consideration of true uncertainty.