Forecasting Densities of Fatalities from State-based Conflicts using Observed Markov Models

TL;DR

This paper introduces an observed Markov model with a two-stage approach for predicting fatality densities from armed conflicts, demonstrating superior performance over benchmarks in the VIEWS 2023 challenge.

Contribution

The paper presents a novel two-stage observed Markov model combining domain-informed latent states with regression models for conflict fatality prediction.

Findings

Model produces well-calibrated forecasts

Outperforms benchmark models

Achieves top performance in evaluation metrics

Abstract

In this contribution to the VIEWS 2023 prediction challenge, we propose using an observed Markov model for making predictions of densities of fatalities from armed conflicts. The observed Markov model can be conceptualized as a two-stage model. The first stage involves a standard Markov model, where the latent states are pre-defined based on domain knowledge about conflict states. The second stage is a set of regression models conditional on the latent Markov-states which predict the number of fatalities. In the VIEWS 2023/24 prediction competition, we use a random forest classifier for modeling the transitions between the latent Markov states and a quantile regression forest to model the fatalities conditional on the latent states. For the predictions, we dynamically simulate latent state paths and randomly draw fatalities for each country-month from the conditional distribution of fatalities given the latent states. Interim evaluation of out-of-sample performance indicates that the observed Markov model produces well-calibrated forecasts which outperform the benchmark models and are among the top performing models across the evaluation metrics.