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Of course a successfully predictive model raises the further question of why it is successful, which may motivate the development of further explanatory models, since a complete scientific understanding requires both prediction and explanation, but not necessarily from the same models (Cartwright 1983).
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Of course this danger is also there for one’s own programming: it is more likely, but far from certain, that you understand some code you have implemented or played with.
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What the “same outcomes” here means depends on how close one can expect the restricted new model to adhere to the original, for example it might be the same but with different pseudo-random number generators.
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Further Reading
Further Reading
Outside the social sciences, simulation has been an established methodology for decades. Thus there is a host of literature about model building in general. The biggest simulation conference, the annual “Winter Simulation Conference”, always includes introductory tutorials, some of which may be of interest to social scientists. Good examples are (Law 2008) and (Shannon 1998).
For a comprehensive review of the currently existing general agent-based simulation toolkits see (Nikolai and Madey 2009); other reviews focus on a smaller selection of toolkits (e.g. Railsback et al. 2006; Tobias and Hofmann 2004; Gilbert and Bankes 2002).
The chapters on checking your simulation model (Galán et al. 2013), documenting your model (Grimm et al. 2013) and model validation (David 2013) in this volume should be of particular interest for anyone intending to follow the exploration and consolidation approach to model development. However, if you would rather attempt a more formal approach to building an agent-based simulation model, the subsequent chapter (Jonker and Treur 2013) discusses one such approach in detail. You could also consult textbooks on methodologies for the design of multi-agent systems, such as (Luck et al. 2004), (Bergenti et al. 2004) or (Henderson-Sellers and Giorgini 2005). After all, any agent-based simulation model can be seen as a special version of a multi-agent system.
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Norling, E., Edmonds, B., Meyer, R. (2013). Informal Approaches to Developing Simulation Models. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_4
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