UN Peacekeeping is often faced with difficult trade-offs. As Paul Williams of George Washington explains, “the [Security] Council's three strategic goals for peacekeeping operations—implementing broad mandates, minimizing peacekeeper casualties and maximizing cost-effectiveness—cannot be achieved simultaneously.” Given the current trend of falling Peacekeeping budgets, it’s clear that Peacekeepers will have to offset their more conservative allocation of resources. A possible way to accomplish all three goals could be using statistical analysis to identify and exploit undervalued assets, as well as maximizing the potential of existing assets. I argue that, by adopting such a perspective, peacekeepers can accomplish more with less.
Assets, in this case, can mean several different things. For example, it can refer to troop contingents in the military component, the types of peacebuilding projects undertaken by the civilian component, and the tactics used by both. Each of these assets has a different cost to acquire and use in mission, both in terms of financial and political capital. It will be cheaper and easier to acquire an infantry battalion from a large troop contributing country (TCC) like India than an infantry battalion from the United States. Each of these assets will also provide different levels of value to the mission. The new Peacekeeping strategy would be to identify the assets that contribute the most value compared to their cost.
This identification will come from statistical analysis. The advantage of statistical analysis over conventional value analysis is that, with statistical analysis, we can identify assets that are undervalued because conventional value analysis also sets the cost of an asset. In order to identify which assets are undervalued, we first must determine what factors create value to a PKO in certain classes of assets, with a special emphasis on the use of new technology. If we are to adopt the statistical approach to PKOs, this research must start immediately.
There are, however, two potential issues with this approach. First, the data we use to determine value may be unreliable or unavailable. Second, we must define what constitutes success for a PKO. Given the many objectives of the average PKO, this is significantly complicated for peacekeepers.
Even with those two caveats in mind, embracing greater statistical analysis and other technologies in Peacekeeping would let Peacekeepers achieve their broad mandates in a safe manner, even with limited financial resources. The UN has already started to embrace this thinking with a new “Digital Transformation” where new technology, such as drones, and statistical analysis combine to maximize the cost-effectiveness of peacekeepers in the field.
The next three sections will provide case studies that highlight this new type of thinking: force generation reform, increased airpower, and conflict prediction. Hopefully, by embracing some of these ideas, a technological revolution can lead UN Peacekeeping to cost-effectiveness and success.\
Case Study A: Force Generation Reform
While it’s easy to outline this new statistics-based methodology in broad strokes, it’s a bit harder to visualize the strategy in practice. This section aims to apply the methodology to a specific hypothetical case, in order to better illustrate the entire methodology.
In this hypothetical scenario, the Department of Peacekeeping Operations Force Generation Service is trying to acquire an additional infantry battalion. They have five choices, as shown in the table below. C represents the total cost in USD to use the infantry battalion for a year, as measured in both direct payments and the indirect effort needed to convince the TCC to loan the infantry battalion. V represents the expected decrease in civilian casualties in the battalion deployment area.
Country: C. V. C/V
A. $50 million. 60% reduction. 833,333.333
B. $40 million. 55% reduction. 727,272.727
C. $40 million. 45% reduction. 888,888.889
D. $30 million. 30% reduction. 1,000,000
E. $25 million. 20% reduction. 1,250,000
In his hypothetical scenario, the Force Generation Service should prioritize recruiting from country B, because the C/V (or the cost in USD for each percent expected decrease in civilian casualties) is the lowest. Importantly, this strategy ignores all other factors, such as whether B is a large or influential TCC, whether B has a long history in PKOs, or whether the Force Generation Service thinks that battalions from B are harder to acquire than from the other countries. We can also compare assets: as shown below. This is a change from the current system of unit evaluation. The current standard operating procedure for the “Assessment and Evaluation of Formed Police Unit Performance,” for example, relies on subjective responses by evaluators to preset questions on a scale of 1-4
Asset: C. V. C/V.
Infantry Battalion from B. $40 million. 55% reduction. 727,272.727
UAV Squadron from B. $20 million. 30% reduction. 66,666.667
In this scenario, it would be more efficient to bring in the UAV unit instead of the infantry battalion. Even if the UAV unit is smaller, it is more cost-efficient than the infantry battalion.
Again, this brief thought exercise is intended to illustrate the statistical strategy in practice. Decisions in this model are made based on statistical value, instead of more subjective evaluations. It’s easy to accomplish this with hypothetical values, in the real world calculating C and V is much more difficult. But attempting to determine the specific values of C and V of military units throughout the DPO’s PCRS system.
Case Study B: Increased Airpower
A consistent effect of underfunded PKOs is that there are never enough peacekeepers to adequately guard the number of people they are charged with protecting. As Williams again notes, even with a modest ratio of 1 peacekeeper for every 100 civilians, the big four UN PKOs would require 397,000 peacekeepers. In reality, they have 69,302 peacekeepers across the four missions. It’s unlikely the UN will be able to afford a larger number of peacekeepers, so the objective would be to allocate their existing peacekeepers in the most efficient way possible. A continued and increased reliance on airpower could maximize this efficiency, for two main reasons. They are increased rapid reaction capability and increased intelligence gathering capability.
Given that peacekeeping missions are understrength, they can’t be in every populated area of their areas of responsibility. This means that when attacks on civilians or other crises occur, particularly in rural areas, peacekeepers have to deploy to the affected areas quickly. Given the poor infrastructure in some of these areas, this is no easy task. One way to dramatically increase the effective deployment range of peacekeeping units is the use of utility transport helicopters. These let peacekeepers bypass physical and political barriers to rapid deployment (such as roadblocks) and reach crisis areas faster.
Right now there is a helicopter shortage in UN PKOs. The United Nations Mission in South Sudan (UNMISS), for example, has only 25 helicopters for 18,106 peacekeepers. Increasing the ratio of helicopters to peacekeepers would be a cost-effective way to maximize the rapid reaction ability of peacekeepers in the field.
However, this advantage is primarily reactive instead of proactive. In order to anticipate attacks on civilians before they happen, and deploy forces to counter beforehand, real-time intelligence and data is needed. Drones offer a possible low-cost way to increase the intelligence gathering capability of UN PKOs.
Drones have several key advantages. First, like other aircraft, they are unconstrained by terrestrial impediments on patrolling, such as harsh terrain or the presence of hostile forces. Second, they are able to remain airborne for a longer period than other forms of airborne surveillance. Third, they are less expensive per flight hour than other forms of airborne surveillance.
The data from drones has proven capable of helping protect civilians. As Karlsrud and Rosen note, the photographic and infrared equipment on UAVs let peacekeepers “track movements of armed militias, assist patrols heading into hostile territory, and document atrocities.” This situational intelligence will help peacekeepers proactively deploy to the same areas hostile groups are moving into. Despite that benefit, UAVs have only widely available in one mission, the United Nations Multidimensional Integrated Stabilization Mission in Mali (MINUSMA).
By increasing their pool of aircraft, UN PKOs can compensate for their lack of personnel at a lower cost than increasing the number of peacekeepers deployed. The DPO has recognized the need for aircraft within their missions. Additional helicopters were deemed a substantial part of their critical mission gaps in a recent report. Another way to increase that efficiency would be to increase the emphasis on conflict prediction, which will be discussed below.
Case Study C: Conflict Prediction
UN PKOs gather large amounts of data from their patrols, and if they embraced some of the recommendations of this article they would gather even more. But that data needs to be used effectively to optimize peacekeeping deployments. One of the best ways to do that would be to further invest in conflict prediction software.
Some existing models have proved successful. A machine learning model developed by Chris Perry, for example, was able to mostly accurately predicate which African countries would experience conflict using publically available data. There are some studies on how these types of conflict predicting algorithms can be applied to PKOs. Duursma and Karlsrud argue that machine learning can provide quantitative analysis in a field dominated by qualitative analysis. If these algorithms can accurately predict where conflict will spike in the mission area, extra peacekeepers and other conflict prevention measures can be deployed to that area. These algorithms, like the accurate calculations of C and V mentioned earlier, are not fully developed. Thus, an analytics revolution is not fully possible immediately.
Conclusion
By setting a broad and bold statistical research agenda, UN leadership can optimize peacekeeping missions. When combined with continuing to embrace new technologies like UAVs and helicopters, this should let PKOs accomplish their broad mandates and protect civilians even in the face of reduced fiscal ability.