Abstract
The first section of this chapter gives an overview on how big data and their mathematical calculation enter in the historical discourse. It introduces the two main issues that prevent ‘big’ results from emerging so far. Firstly, the input is problematic because historical records cannot be easily and comprehensively decomposed into unambiguous fields, except for the population and taxation ones, which are rare and scattered throughout space and time till the nineteenth century. Secondly, even if we run machine-learning tools on properly structured data, big results cannot emerge until we built formal models, with explanatory and predictive powers. The second section of the chapter presents a complex network, data-driven approach to mining historical sources and supporting the perennial historical chase for truth. In the time-integrated network obtained by overlaying all records from the historians’ databases, the nodes are actors, while the links are actions. The third section explains how this tool allows historians to deal with historical data issues (e.g., source criticism, facts validation, trade-conflict-diplomacy relationships, etc.), and take advantage of automatic extraction of key narratives to formulate and test their hypotheses on the courses of history in other actions or in additional data sets. The conclusions describe the vision of how this narrative-driven analysis of historical big data can lead to the development of multiscale agent-based models and simulations to generate ensembles of counterfactual histories that would deepen our understanding of why our actual history developed the way it did and how to treasure these human experiences.
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Acknowledgments
This study has been funded by 2016 Microsoft Research Asia Collaborative Research Program and 2016 Microsoft Azure for Research. The research project, called Engineering Historical Memory (EHM), was first theorized by Andrea Nanetti in 2007, when he was Visiting Scholar at Princeton University. The actual web development initiated in 2012 when he was Visiting Professor at the University of Venice Ca′ Foscari, and since 2013 has been carried out at Nanyang Technological University (NTU Singapore), where he is Associate Chair (Research) in the School of Art, Design and Media, and has been funded among others by an NTU Start-up Grant (2014–2016 M4081357), 2014 Microsoft Research Asia Collaborative Research Program, 2015 Microsoft Azure for Research, and 2016 Microsoft Research Internship Program. This study contributed to the application and kick off of the NTU TIER 1 Grant (2017--2019) on ‘‘Data Consolidation for Interactive Global Histories (1205--1533) within the NTU National and International Research Network: Towards an NTU Interdisciplinary Laboratory for Data-Driven Agent-Based Modelling and Simulations for Historical Sciences’’ (2017–2020 M4011828). The domain of EHM (www.engineeringhistoricalmemory.com) is administrated by Meduproject S.r.l., an Italian Pte Ltd. company established in 2002 by Andrea Nanetti as academic spin-off of the University of Bologna (Department of Histories and Methods for Cultural Heritage Conservation), after having been awarded in 2001 a prize in the first Italian business plan competition devoted to projects with high content of knowledge and having been financially supported by the Italian National Agency for New Technologies, Energy and Environment.
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Nanetti, A., Cheong, S.A. (2018). Computational History: From Big Data to Big Simulations. In: Chen, SH. (eds) Big Data in Computational Social Science and Humanities. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-95465-3_18
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