Tackling Uncertainty Through Probabilistic Modelling of Proportionality in Military Operations

Abstract

Just as every neuron in a biological neural network is a reinforcement learning agent, thus a component of a large and advanced structure is de facto a model, the two main components forming the principle of proportionality in military operations can be seen and are as a matter of fact two different entities and models. These are collateral damage depicting the unintentional effects affecting civilians and civilian objects, and military advantage symbolizing the intentional effects contributing to achieving the military objectives defined for military operation conducted. These two entities are complex processes relying on available information, projection on time to the moment of target engagement through estimation and are strongly dependent of common-sense reasoning and decision making. As a deduction, these two components and the proportionality decision result are processes surrounded by various sources and types of uncertainty. However, the existing academic and practitioner efforts in understanding the meaning, dimensions, and implications of the proportionality principle are considering military-legal and ethical lenses, and less technical ones. Accordingly, this research calls for a movement from the existing vision of interpreting proportionality in a possibilistic way to a probabilistic way. Henceforth, this research aims to build two probabilistic Machine Learning models based on Bayesian Belief Networks for assessing proportionality in military operations. The first model embeds a binary classification approach assessing if the engagement is proportional or disproportional, and the second model that extends this perspective based on previous research to perform multi-class classification for assessing degrees of proportionality. To accomplish this objective, this research follows the Design Science Research methodology and conducts an extensive literature for building and demonstrating the model proposed. Finally, this research intends to contribute to designing and developing explainable and responsible intelligent solutions that support human-based military targeting decision-making processes involved when building and conducting military operations.