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Journal Article

Computational History: Challenges and Opportunities of Formal Approaches

MPS-Authors
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Lalli,  Roberto       
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

/persons/resource/persons194160

Laubichler,  Manfred Dietrich
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

/persons/resource/persons194294

Renn,  Jürgen       
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

/persons/resource/persons194471

Wintergrün,  Dirk
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375934
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Citation

Jost, J., Lalli, R., Laubichler, M. D., Olbrich, E., Renn, J., Restrepo, G., et al. (2023). Computational History: Challenges and Opportunities of Formal Approaches. Journal of Social Computing, 4(3), 232-242. doi:10.23919/JSC.2023.0017.


Cite as: https://hdl.handle.net/21.11116/0000-000E-6865-C
Abstract
We propose a program for a computational analysis, based on large scale datasets, of deep
conceptual and formal structures, representing the mechanisms of historical transformations in different domains ranging from biological to social, cultural, and knowledge systems. We conceptualize such systems as consisting of complex multi-layer networks. Structural properties of such networks may explain the
spreading of innovations. Temporal relations between the dynamics of interacting networks may help to identify causalities. Complex systems may show path and context dependencies. We illustrate our approach by case studies from all those types of systems.