Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143168
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNanetti, Andreaen_US
dc.contributor.authorCheong, Siew Annen_US
dc.contributor.editorChen, Shu-Hengen_US
dc.date.accessioned2020-08-07T05:18:16Z-
dc.date.available2020-08-07T05:18:16Z-
dc.date.issued2018-
dc.identifier.citationNanetti, A., & Cheong, S. A., (2018). Computational history : from big data to big simulations. In S.-H. Chen (Ed.), Big Data in Computational Social Science and Humanities (pp. 337-363). doi:10.1007/978-3-319-95465-3_18en_US
dc.identifier.isbn978-3-319-95464-6en_US
dc.identifier.urihttps://hdl.handle.net/10356/143168-
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.publisherSpringer Nature Switzerland AGen_US
dc.rights© 2018 Springer International Publishing AG, part of Springer Nature. All rights reserved.en_US
dc.subjectScience::Mathematicsen_US
dc.titleComputational history : from big data to big simulationsen_US
dc.typeBook Chapteren_US
dc.contributor.schoolSchool of Art, Design and Mediaen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1007/978-3-319-95465-3_18-
dc.relation.ispartofbookBig data in computational social science and humanitiesen_US
dc.identifier.spage337en_US
dc.identifier.epage363en_US
dc.subject.keywordsBig Dataen_US
dc.subject.keywordsComputational Historyen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:ADM Books & Book Chapters

Page view(s) 50

39
checked on Oct 26, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.