From data-poor to data-rich: system dynamics in the era of big data
Erik Pruyt · Data Archiving and Networked Services (DANS) · 2014
Although SD modeling is sometimes called theory-rich data-poor modeling, it does not mean SD modeling should per definition be data-poor. SD software packages allow one to get data from, and write simulation runs to, databases. Moreover, data is also sometimes 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 often requires data science techniques to make it manageable and useful. There are at least three ways in which big data and data science may play a role in SD: (1) to obtain useful inputs and information from (big) data, (2) to infer plausible theories and model structures from (big) 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. There are many application domains in which the combination of SD and big data 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, market assessment and asset management.