Cover: Machine Learning for Operational Decisionmaking in Competition and Conflict

Machine Learning for Operational Decisionmaking in Competition and Conflict

A Demonstration Using the Conflict in Eastern Ukraine

Published Sep 19, 2023

by Eric Robinson, Daniel Egel, George Bailey

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Research Questions

  1. How can commanders leverage machine learning for operational decisionmaking?
  2. How should human analysts collaborate with machine learning tools to enable operational decisionmaking?

The integration of machine learning into military decisionmaking is widely seen as critical for the United States to retain its military dominance in the 21st century. Advances in machine learning have the potential to dramatically change the character of warfare by enhancing the speed, precision, and efficacy of decisionmaking across the national security enterprise. This report explores how machine learning can be leveraged to enable military decisionmaking at the operational level of competition and conflict as part of a collaboration between machine learning tools and human analysts.

The authors present a case study based on a machine learning-based analysis of real-world data about the conflict in eastern Ukraine prior to Russia's 2022 invasion. This case study places the reader in a commander's shoes, tasked with making decisions about the best types of support to provide Ukrainian forces to achieve shared objectives. This analysis demonstrates that machine learning can improve efficiency by helping human analysts leverage massive data sets that would be impractical for humans alone to examine. The authors found that machine learning has great potential to enable military decisionmaking at the operational level of war but only when paired with human analysts who possess detailed understanding of the context behind a given problem.

Key Findings

  • Machine learning approaches can generate new insights to inform military decisionmaking in competition and conflict when employed as part of a human-machine collaborative system.
  • Human involvement—from analysts who possess detailed understanding of the context behind a given problem—is critical for deriving useful insights for military decisionmaking from currently available machine learning capabilities.
  • By enabling dramatic efficiency gains in the performance of repetitive tasks, a human-machine systems approach can analyze massive data sets at a scale that would be impractical for human analysts alone, generating new insights about the operational environment that would have previously been unattainable.
  • Machine learning enables standardized, objective, and long-term analysis of a multitude of data streams already available in an operational-level headquarters.

Recommendations

  • The Army should provide personnel at all echelons of command with frequent exposure to machine learning to build familiarity with how humans can leverage these capabilities as part of a military decisionmaking system.
  • The Army should build diverse machine learning teams to unlock the full potential of this capability, integrating operations research systems analysts with operators, analysts, and commanders to help interpret the implications of machine learning-based analysis.

Research conducted by

This research was sponsored by the United States Army and conducted by the Strategy, Doctrine, and Resources Program within the RAND Arroyo Center.

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