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The Violence Early-Warning System: Building a Scientific Foundation for Conflict Forecasting

Periodic Reporting for period 3 - ViEWS (The Violence Early-Warning System: Building a Scientific Foundation for Conflict Forecasting)

Reporting period: 2020-01-01 to 2021-06-30

The aim of the project is to carry out the basic research to build ViEWS -- an ambitious political violence early-warning system. A useful system must have uniform, global coverage and frequent updates to avoid blind spots and specifically provide alerts that are as location- and actor-specific as possible. It must be transparent and publicly available to anyone in the sense that all risk assessments may be traced back to a fully specified argument and publicly available data and tools with which assessments were created. It must build on the most advanced theoretical and empirical research within the field, and be based on systematic evaluation of predictive performance on data not used to train the system. It must cover a range of related but distinct forms of political violence, most importantly armed conflicts between organized state armies or non-state armed groups, violence against civilians perpetrated by governments and armed groups, and the forced displacement of civilians that often follows in conflicts' wake. Warnings must be issued with respect to the geographical location of the violence as well as the actors involved.

Succeeding in this objective is important for society. Large-scale political violence kills and maims thousands of people every month across the globe. For every person killed, hundreds are forced to relocate within countries and across borders. Armed conflicts have disastrous economic consequences, undermine political systems and public services, prevent developing countries from escaping poverty, and hinder international actors in providing humanitarian assistance. The challenges of preventing, mitigating, and adapting to large-scale political violence are particularly daunting when it escalates in locations and at times where it is not expected. Policy-makers and first responders would benefit greatly from a system that systematically monitors all locations at risk of conflict and assesses the risks of conflict escalation. If fully successful, ViEWS will help crisis responders to prepare for and prevent conflict-induced humanitarian disasters. Such a system will be useful for domestic actors and international NGOs, ensure maximum transparency and credibility regarding decisions made on the basis of specific warnings. In addition, it provides the scientific benefits of better understanding the causes, connections and consequences of conflict.

When planning the project, we have broken it into five sub-objectives.

- Develop the data-collection and programming routines required for a pilot ViEWS system

- Formulate theoretically informed models of violent processes and integrate in an ensemble forecast

- Solve methodological challenges to incorporate spatial and temporal dynamics across multiple levels of analysis to integrate, weight, and improve forecasts

- Highlight the theoretical implications of the integrated and evaluated modeling approach

- Compare ViEWS with other forecasts in the field

Several of these sub-objectives imply conducting basic research to develop methods for training of predictive models and for evaluating their performance, and the adaptation of theoretically founded empirical models from the extant relevant literature. Is it even possible to construct a high-quality political violence early-warning system based on openly accessible data? How good are such systems?

The first sub-objective, however, entails setting in production a live forecasting pilot that provides monthly forecasts for all of Africa. Currently, the pilot produces forecasts at the country level -- the probability of a conflict event in any African country -- and the geographic level -- the probability of a conflict event in any of approximately 10,000 grid cells with a size of 0.5x0.5 degrees (about 55x55km), as defined by the PRIO-GRID project. The live pilot has been running since June 2018 and we are currently finalizing the first major revision of the system, based on the basic research carried out over the past 18 months.
In the following, and in all other text elements in the report, we refer to items in the project publication list as 'PP#' where # is the item number. PP10, for instance, is the 2019 article in Journal of Peace Research that presents an overview of the forecasting pilot. We refer to items in the list of dissemination and outputs as 'DO#'.

The most visible output from the project is the initiation of the live forecasting pilot, accessible at the project website. As elaborated on in the section on Project achievements, this has entailed constructing a large and complex database and an associated data ingestion system, developing forecasting models that maximizes predictive performance while retaining interpretability, implementing simulation routines for prediction models where useful solutions are not available elsewhere, and construct a pipeline that processes all steps required to run monthly updates to the forecasting pilot.

From June 2018, ViEWS has had a complete first version of the pilot running, with updated forecasts for the coming 36 months published every month. The figure 'ViEWS flowchart' summarizes the entire process underlying it. The routines ensure that the forecasts have excellent consistency and that operations are reliable and secure. The procedures handle a large amount of data and complex models, so considerable efforts have been invested in maximizing efficiency. Efficiency and automatization is critical for operating a live pilot with regular updates. The investments put down to ensure this, however, also opens up new possibilities for exploring new models and methods on top of the excellent infrastructure the team has built.

The current pilot produces forecasts for all the three forms of political violence coded by the Uppsala Conflict Data Programme - state-based conflict between a government army and the army of another government or an organized, armed non-state actor; one-sided violence in which government or non-state armed groups kill unarmed civilians; or non-state conflict where non-state armed groups fight each other. The first step in the procedure is to monitor past conflicts. In the figure 'History of non-state conflict, June 2019', locations with recent non-state conflict events are shown with red color, whereas locations that have been calm over the past few years have purple color.
The ViEWS pilot is the most ambitious live, open-source conflict early-warning system in existence. Other open-source projects (e.g. Coupcast and the US holocaust memorial museum's Early warning project) are begin updated regularly and of excellent quality, but only publish forecasts at the country level and for a more restricted set of outcomes. ViEWS is unique in its multi-level, multi-outcome forecasting ambition. This ambition level has merits on its own, but a main rationale for ViEWS is that pursuing multiple related forecasting problems simultaneously allows the solutions to inform each other. ViEWS has already demonstrated (in PP10) that this strategy bears fruits, but we also believe that there is considerable untapped potential to be explored in the next iterations of the forecasting system.
Change in predicted probabilities, state-based conflict, geographical level
Classification matrix, geographical level, state-based conflict
History of non-state conflict, June 2019
Classification matrix, country level, state-based conflict
Forecasts for August 2019, one-sided violence, geographical level
ViEWS flowchart
Forecasts for August 2019, state-based conflict, country level
Evaluation metrics, cm level, state-based conflict