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Mining International Political Norms from the GDELT Database

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Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XIII (COIN 2017, COINE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12298))

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Abstract

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems (MAS). Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to learn the norms existing in human society.

This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events mentioned in news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.

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Notes

  1. 1.

    We omit conflict event types (such as Threaten, Assault and Fight) from this list, as we consider these to be events that are likely to be subject to norms, rather than reactions to norm violations.

  2. 2.

    Some sequences contained as many as 20,000 events.

  3. 3.

    https://github.com/jgasthaus/libPLUMP.

  4. 4.

    The priors were are initialised to 1 for all of the norms we consider. However, this choice has no significant effect as we are interested in the relative posterior odds.

  5. 5.

    Note that this approach only reasons about single-norm hypotheses. Based on the log odds of individual norms, it would be possible to select combinations of most likely norms to form multi-norm hypotheses, but this requires a more complex model of observation likelihood than we have at present.

  6. 6.

    The transition annotations have the format \( trigger [ guard ] / action \). Transitions from the initial state have no trigger, and guards and actions are optional.

  7. 7.

    To simplify the model, only the first instantiation, fulfilment, violation and sanction of a norm within a sequence are tracked by the state machine.

  8. 8.

    We perform the calculations in log space, resulting in a log likelihood, but for simplicity of presentation we do not show this.

  9. 9.

    In the case of unconditional norms, this is once for every observation.

  10. 10.

    Add-one smoothing involves adding one to both positive and negative counts, hence the “+2” in the denominator (the total count).

  11. 11.

    Recall from Sect. 5 that we train sequence memoizers with two copies of each event sequences: one with its default directions (where the first event is taken to be “forwards” and a direction-reversed copy of the sequence).

  12. 12.

    The number was constrained by the highly time-intensive process of looking up the SM’s conditional probabilities to generate and to evaluate the likelihood of these large datasets.

  13. 13.

    Murphy discusses the relationship between sampling from the posterior and the well known bootstrap method for approximating the sampling distribution of a test statistic [22, p.192].

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Acknowledgement

We thank Matt Schofield for statistical advice.

Author information

Authors and Affiliations

  1. Indian Institute of Science, Bangalore, India

    Rohit Murali

  2. Sponsa Limited, Wellington, New Zealand

    Suravi Patnaik

  3. University of Otago, Dunedin, New Zealand

    Stephen Cranefield

Authors
  1. Rohit Murali
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  2. Suravi Patnaik
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  3. Stephen Cranefield
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Corresponding author

Correspondence to Stephen Cranefield .

Editor information

Editors and Affiliations

  1. Umeå University, Umeå, Sweden

    Andrea Aler Tubella

  2. University of Otago, Dunedin, New Zealand

    Stephen Cranefield

  3. Norwegian University of Science and Technology (NTNU), Gjøvik, Norway

    Christopher Frantz

  4. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

    Felipe Meneguzzi

  5. University of Aberdeen, Aberdeen, UK

    Wamberto Vasconcelos

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Murali, R., Patnaik, S., Cranefield, S. (2021). Mining International Political Norms from the GDELT Database. In: Aler Tubella, A., Cranefield, S., Frantz, C., Meneguzzi, F., Vasconcelos, W. (eds) Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XIII. COIN COINE 2017 2020. Lecture Notes in Computer Science(), vol 12298. Springer, Cham. https://doi.org/10.1007/978-3-030-72376-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-72376-7_3

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