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Toward a Bayesian Network Model of Events in International Relations

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9708))

Abstract

Formal models of international relations have a long history of exploiting representations and algorithms from artificial intelligence. As more news sources move online, there is an increasing wealth of data that can inform the creation of such models. The Global Database of Events, Language, and Tone (GDELT) extracts events from news articles from around the world, where the events represent actions taken by geopolitical actors, reflecting the actors’ relationships. We can apply existing machine-learning algorithms to automatically construct a Bayesian network that represents the distribution over the actions between actors. Such a network model allows us to analyze the interdependencies among events and generate the relative likelihoods of different events. By examining the accuracy of the learned network over different years and different actor pairs, we are able to identify aspects of international relations from a data-driven approach. We are also able to identify weaknesses in the model that suggest needs for additional domain knowledge.

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Notes

  1. 1.

    gdeltproject.org, lockheedmartin.com/us/products/W-ICEWS/iData.html.

  2. 2.

    We also used ICEWS, but omit those results for space considerations.

  3. 3.

    http://eventdata.parusanalytics.com/data.dir/cameo.html.

  4. 4.

    https://dataverse.harvard.edu/dataverse/Voeten.

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Acknowledgments

This work was sponsored by the U.S. Army Research, Development, and Engineering Command (RDECOM). The authors also thank Torsten Woertwein, and Drs. Marco Scutari, Kalev Leetaru, Eric Voeten, Arthur Spirling, Jeffery Freiden, and Steven Brams.

Author information

Authors and Affiliations

  1. Institute for Creative Technologies, University of Southern California, Los Angeles, USA

    Ali Jalal-Kamali & David V. Pynadath

Authors
  1. Ali Jalal-Kamali
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  2. David V. Pynadath
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Corresponding author

Correspondence to Ali Jalal-Kamali .

Editor information

Editors and Affiliations

  1. University of Toledo, Toledo, Ohio, USA

    Kevin S. Xu

  2. Penn State University, University Park, Pennsylvania, USA

    David Reitter

  3. Penn State University, University Park, Pennsylvania, USA

    Dongwon Lee

  4. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Nathaniel Osgood

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Jalal-Kamali, A., Pynadath, D.V. (2016). Toward a Bayesian Network Model of Events in International Relations. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-39931-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39930-0

  • Online ISBN: 978-3-319-39931-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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