Accounting for variability in conflict dynamics: A pattern-based predictive model
Bibliographic record
Abstract
Existing models for predicting conflict fatalities frequently produce conservative forecasts that gravitate towards the mean. While these approaches have a low average prediction error, they offer limited insights into temporal variations in conflict-related fatalities. Yet, accounting for variability is particularly relevant for policymakers, providing an indication on when to intervene. In this article, we introduce a novel risk-taking methodology, the 'Shape finder', designed to capture variability in fatality data, or rather the sudden surges and declines in the number of deaths over time. The method involves isolating historically analogous sequences of fatalities to create a reference repository. Comparing the shape of the input sequence to the historical references, the most similar historical cases are selected. Predictions are then generated using the average future outcomes of the selected matches. The Shape finder is derived from the theoretical understanding that strategic and adaptive interactions between the government and a non-state armed group produce recurring temporal patterns in fatality data, which are indicative of broader developments. In this article, we demonstrate that our approach maintains high accuracy while significantly enhancing the ability to predict shifts, surges, and declines in conflict fatalities over time. We show that combining the Shape finder with existing approaches, the Violence Early-Warning System ensemble, achieves a lower mean squared error and better accounts for variability in fatality data. The Shape finder methodology performs particularly well for high intensity cases, or rather country-months with substantial armed violence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".