Machine learning for operational decisionmaking in competition and conflict : a demonstration using the conflict in eastern Ukraine / Eric Robinson, Daniel Egel, George Bailey.

Format:

Book

Author/Creator:

Robinson, Eric, author.

Egel, Daniel, author.

Bailey, George, author.

Series:

Research report (Rand Corporation)

Research report

Language:

English

Subjects (All):

Artificial intelligence--Military applications--United States.

Artificial intelligence.

Machine learning.

Decision making.

Russo-Ukrainian War, 2014---Case studies.

Russo-Ukrainian War, 2014-.

Machine Learning.

Data Science.

Emerging Technologies.

Ukraine.

Local Subjects:

Machine Learning.

Data Science.

Emerging Technologies.

Ukraine.

Physical Description:

xi, 76 pages : illustrations (black & white, color) ; 23 cm.

Place of Publication:

Santa Monica, CA : Rand Corporation, [2023]

Summary:

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.

Contents:

Chapter One: Machine Learning as a System

Chapter Two: Demonstrating the System at Work: Machine Learning and the Conflict in Ukraine

Chapter Three: Strengths and Limitations of Machine Learning

Chapter Five: Implications for the U.S. Army

Appendix: Training a Machine to Read the News.

Notes:

Includes bibliographical references.

ISBN:

9781977412102

1977412106

OCLC:

1398210375