Conflict Forecasting via Conformal Prediction for Markov Processes

TL;DR

This paper applies conformal prediction to Markov process data to generate reliable future conflict state predictions, providing uncertainty quantification for conflict forecasting.

Contribution

It introduces a conformal prediction framework tailored for temporally-dependent Markov data, enhancing conflict prediction with valid uncertainty measures.

Findings

Conformal prediction yields valid uncertainty quantification in conflict forecasting.

The approach outperforms likelihood-based methods in robustness to model misspecification.

Real-world conflict data forecasts demonstrate practical applicability.

Abstract

Whether or not a country is at war, or experiencing escalating or deescalating levels of conflict, has massive ramifications on a country's national and foreign policy. Given a country's history of conflict, or lack thereof, future predictions about the war-status of a country are valuable information. In this paper, we present the use of conformal prediction on temporally-dependent data to obtain prediction sets of possible future conflict state-sequences. More specifically, we compare the results of conformal prediction to a likelihood-based prediction strategy when the data are assumed to come from a discrete-state Markov process. A point-prediction may not supply sufficient information because the penalty for a wrong prediction is extreme, and so we consider a machine learning alternative that gives valid uncertainty quantification and is robust to model misspecification. In the data analysis, we present real forecasts of conflict dynamics across multiple countries. Lastly, we comment on the possible limitations of existing approaches for applying conformal prediction to Markovian data, where the exchangeability assumption is violated.