One of the characteristics of Big Data is that it often involves “imperfect” information. This paper examines the work of John Graunt in the tabulation of diseases in London and the development of a life table using the “imperfect data” contained in London’s Bills of Mortality in the 1600s. London’s Bills of Mortality were Big Data for the 1600s, as they included information collected over time, the depth and accuracy of which improved gradually. The main shortcoming of the data available (...) at the time was its nonuniform upkeep and the lack of depth of variables included at its outset. Due to these characteristics, it provides a perfect model for the examination of imperfect Big Data, as it has been analyzed, criticized, and interpreted repeatedly since the 1600s. (shrink)

I address the philosophical debate over whether the mathematical theory of probability arose on the basis of empirical observations or of purely theoretical speculations. The debate tends to pose a strict dichotomy between empirical problem-solving and pure theorizing. I alternatively suggest that, in the case of mathematical probability, an empirical problem-context acted as an enabling condition for the possibility of mathematical innovation, but that the activity of the early mathematical probabilists gradually became the study of a theoretical system of ideas. (...) This case has some implications for a more general philosophical view on the context of mathematical discovery. ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- ----------------------------------------. (shrink)