Code for the paper Direction Augmentation in the Evaluation of Armed Conflict Predictions

by Johannes Bracher, Lotta Rüter, Fabian Krüger, Sebastian Lerch and Melanie Schienle conditionally accepted in International Interactions, preprint available here.

Notes

• The contents of this repository are under a Creative Commons Attribution-NonCommercial 4.0 International Public License.
• Please note that we use the terms optimal point forecast (OPF) and Bayes act (BA) synonymously in this code.
• All computations were performed with R version 4.3.0 (2023-04-21)
• Required packages: tidyverse, rlist, purrr, sn, plotrix, xtable

Contents

Empirical Example – data and code to reproduce all empirical results

• tadda_example.R reproduces the empirical results from Section 5, generates the files in the Results folder, computes the optimal window size w and generates Tables 2 and 3.
• example_mali.R generates Figure 3.
• bayes_acts_functions.R contains functions for computing the optimal point forecasts (OPFs) of different scoring functions (AE, SE and variants of TADDA) as well as a summary function for a nicer representation of the results.

/Data

• data_prep.R (i) extracts time series of country month fatalities due to state based conflict ged_best_sb for each country in the data set and (ii) computes true s = 1, ..., 7 step ahead log-changes for each country and month.
• fatalities.csv contains results of data_prep.R.
• ged_cm_postpatch.parquet and skeleton_cm_africa.parquet contain the data in their original format. These were retrieved from https://github.com/UppsalaConflictDataProgram/views_competition/tree/main/data (published under Creative Commons Attribution-NonCommercial 4.0 International Public License).

/Results

• average_scores_for_different_window_lengths.csv contains average scores by forecast horizon for different values of w; shows that w = 5 would be optimal for minimizing TADDA1 via TADDA1_OPF.
• individual_predictions_w9.csv and individual_losses_w9.csv contain the predictions using w = 9 for the log-changes in fatalities and corresponding losses for each African country, month in 395:495, lead time s = 2, ..., 7 and scoring function / OPF.
• average_scores_w9.csv contains the central results presented in Table 2 (i.e., with w = 9).
• empirical_quantiles_w9.csv is the basis of Table 3.

/Figures

• Figure 3: example_mali.pdf

Simulations – illustrative figures and small simulation examples

• check_formulas.R compares OPFs / Bayes acts computed using numerical optimization to the analytical results obtained using the formulas from the manuscript. The agreement between the two indicates that our derivations are correct.
• functions.R contains functions (i) to compute different scoring functions (AE, SE and variants of TADDA), (ii) to numerically determine the optimal point forecast (Bayes act) given a distribution and scoring rule as well as (iii) to provide text annotations in a plot.
• illustration.R generates Figure 2, Table 1, and Supplementary Figure S6.
• illustrations_proof.R generates Supplementary Figures S4 and S5.

/Figures

• Figure 1: curves_scores_L1.pdf
• Figure 2: illustration.pdf
• Figure S4: F_vs_G_epsilon.pdf
• Figure S5: F_vs_G_minus_epsilon.pdf
• Figure S6: illustration_TADDA2.pdf
• ba_numerical_vs_analytical.pdf (plausibility check of analytical results), curves_scores_L2.pdf (illustration of the L2 version of TADDA1) and expected_scores.pdf (more detailed version of light grey / red curves from Figure 2) further illustrate TADDA1 and TADDA2, not included in the paper.