Herbert Simon講座系列#29

Old Research Questions and New Methodologies: The Perennial History’s Chase for Truth at the Computational Turn

http://www.aiecon.org/herbertsimon.php/

Speaker:

Andrea Nanetti

講者介紹 About the speaker:

Dr. Nanetti, using both his academic and entrepreneurial experiences, teaches as Associate Professor at Nanyang Technological University Singapore (School of Art, Design and Media), is a Founding Co-Director (International Relations) of the International Research Centre for Architectural Heritage Conservation at Shanghai Jiao Tong University, and serves as a member of the College of Professors of the curriculum 'Surveing and Representation of Architecture and Landscape' in the PhD School of Architecture at the University of Florence and as a member of the Board of Directors of the Maniatakeion Foundation (Athens and Koroni).

議程Program Schedule:

時間 Time	講者 Speaker	題目Title	地點 Place
10:00–11:30 AM, May 25, 2017.	Andrea Nanetti	Old Research Questions and New Methodologies: The Perennial History’s Chase for Truth at the Computational Turn	NCCU General Building of Colleges (South), Room 271034 政大綜合院館南棟10樓271034室

主辦單位Sponsor：國立政治大學 經濟系(Economics Department, National Chengchi University), AI-ECON Research Center

摘要 Abstract:

This talk provides a presentation of the three major themes submitted by Dr. Nanetti and Dr. Cheong as book chapter for a new book edited by Prof. Shu-Heng Chen on Big Data in Computational Social Science and Humanities, for the Springer Series on “Computational Social Sciences”. The first part of the talk 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 part of the talk 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 part 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.