Due to its inherently temporary nature, the Special Jurisdiction for Peace (JEP) must prosecute the most significant crimes committed during the Colombian armed conflict by selecting the highest-ranking perpetrators and identifying systematic patterns of human rights violations and breaches of international humanitarian law. Only in this way is it possible to avoid impunity and mitigate the impossibility of individually prosecuting the thousands of people involved in the hostilities.

However, the JEP has recently faced criticism for not focusing on the selection of top perpetrators, but adopting maximalist approaches and case-by-case analysis methodologies that tend to focus on the individual involvement of all those who participated in the conflict. This has prevented the authority from adequately responding to the massive and complex nature of the cases, leading to delays that disappoint both former combatants and victims. Moreover, it has increased the risk of impunity, as dedicating resources to individual cases may mean that the JEP cannot timely fulfill its main objective.

To prevent this, it is necessary to explore new perspectives that allow for a more efficient selection of the highest-ranking perpetrators. Mathematics seems to offer some insights.

Indeed, for centuries, various scientists have studied ways to maximize the probability of success when making such decisions. They have sought to identify an algorithm that would allow for the best and most efficient selection in the shortest time possible. It is said that the first to attempt this was Johannes Kepler, who, in 1611, set out to find the best candidate to be his wife. The main obstacle he faced, like the JEP, was time: if he spent too much time thoroughly reviewing all the candidates, he risked losing the best one, as she might not wait for him to finish deciding. The challenge, then, was to determine when to stop reviewing his options. In other words, he needed to find the most efficient way to choose the most likely best candidate without having to examine all the available options.

Since the mid-20th century, the search for the best method to quickly make a selection became popular as a mathematical problem, applicable to any situation, such as choosing a house, a partner, a university major, a financial investment, or a secretary for a company. This latter scenario gave rise to the so-called “Secretary Problem,” a topic of study in optimal stopping theory, that seeks to determine the moment to stop evaluating our alternatives and make a decision. Essentially, it is the same question Kepler tried to solve but applied to a company that wants the highest probability of finding the best secretary among several candidates without interviewing them all. This problem becomes critical as the number of options increases because it takes more time and effort to examine each one, which would make the selection process inefficient.

After several experts attempted to solve the problem, John Gilbert and Frederick Mosteller from Harvard University found the solution: the 37% rule. They discovered that one only needs to follow a simple algorithm to maximize the probability of finding the best option, at least mathematically. The rule is to first examine only 37% of the available options and then choose the next one that is as good or better than those, even if many more remain unexamined.

This model, of course, does not guarantee absolute certainty that the best choice will be made, nor does it consider the myriad variables that exist in the real world. However, it provides an optimal solution for increasing the likelihood of achieving the best outcome while reducing the time and effort required for the selection process. This is particularly important for institutions like the JEP, which have a very limited operational period and cannot afford to prosecute crimes using a case-by-case methodology. Instead, they must optimize their efforts to identify the top perpetrators. Perhaps the 37% rule does not address the complexity of the various scenarios within the armed conflict. However, it underscores the value of knowing when to stop analyzing cases and applying what has been learned to make quicker decisions.