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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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Discovering the mesoscale for chains of conflict
1Complexity Science Hub, Josefstædter Strasse 39, 1080 Vienna, Austria.
PNAS Nexus
|August 3, 2023
Summary
This study introduces a novel method to analyze the causal chains of armed conflict events across various scales. It identifies a mesoscale where conflict dynamics emerge, aiding in understanding and predicting conflict patterns.
Area of Science:
- Social Science
- Computational Social Science
- Conflict Studies
Background:
- Social processes, including conflicts, occur across diverse temporal and spatial scales.
- Existing research lacks systematic methods for connecting these scales, formally treating causality, and measuring uncertainty in event relationships.
Purpose of the Study:
- To develop a method for extracting causally related chains of events in armed conflict.
- To address limitations in scale, causality, and uncertainty in conflict event analysis.
Main Methods:
- Developed a method to extract causally related event chains.
- Utilized adjustable spatial and temporal scales for event clustering.
- Employed data from the Armed Conflict Event & Location Data Project.
Main Results:
- Discovered a mesoscale (weeks to months, tens to hundreds of kilometers) exhibiting long-range correlations in conflict events.
- Identified mesoscale clusters that align with mechanisms described in field studies.
- Revealed zones of causal interaction around conflict hotspots, incorporating uncertainty.
Conclusions:
- A systematic, data-driven, and scalable procedure can extract social objects for study.
- The developed technique offers a framework for scrutinizing and predicting conflict and other social processes.
- The mesoscale analysis provides new insights into the dynamics of armed conflict.
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