Abstract for: From data-poor to data-rich: System Dynamics in the era of Big Data

Although SD modeling is often called data-poor modeling, it does not mean it should always be data-poor. SD software packages allow one to get data from, and write simulation runs to, data bases. Moreover, data is also used in SD to calibrate parameters or bootstrap parameter ranges. But more could and should be done, especially in the coming era of `Big Data'. `Big' data simply refers here to more data than was until recently manageable. Big data requires data science techniques to make it manageable and useful. There are at least three ways in which big data science may play an important role in future SD: (1) to obtain useful inputs from data, (2) to infer plausible model structures from data, and (3) to analyse and interpret model-generated ``brute force data''. Interestingly, data science techniques that are useful for (1) may also be made useful for (3) and vice versa. Real cases from diverse application domains will be used to illustrate these possibilities. In fact, there are many application domains in which the combination of SD and Big data science would be beneficial. Examples, some of which are elaborated here, include policy making with regard to crime fighting, infectious diseases, cyber security, national safety and security, financial stress testing, housing market management, marketing, etc.