Machine learning for operational decisionmaking in competition and conflict: a demonstration using the conflict in Eastern Ukraine
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Santa Monica, Calif.
RAND Corporation
[2023]
|
| Series: | Research report
RR-A815-1 |
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034727395&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034727395&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | xi, 76 Seiten Diagramme |
| ISBN: | 9781977412102 |
Staff View
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| adam_text | Contents About This Report........................................................................................... iii Summary............................................................................................................ v Figures and Tables........................................................................................... xi CHAPTER ONE Introduction..................................................................................................... 1 CHAPTER TWO Machine Learning as a System..........................................................................5 Military Applications of Machine Learning and Human-Machine Collaboration.................................................................................... 6 Radar and the Systems Approach to Human-Machine Collaboration..... 8 Machine Learning and Unstructured Text Data........................................... 9 CHAPTER THREE Demonstrating the System at Work: Machine Learning and the Conflict in Ukraine.......................................................................... 13 Taking Command.......................................................................................... 14 Key Conflict Indicators for Assessment...................................................... 16 Implementing a Machine Learning Approach............................................ 17 The Briefing................................................................................................... 21 Way
Ahead.................................................................................................... 36 CHAPTER FOUR Strengths and Limitations of Machine Learning........................................ 39 Machine Learning Produces Significant Efficiency Gains........................ 39 Efficiency Gains Depend on the Volume of Data........................................41 Machine Learning Opens New Lines of Inquiry......................................... 42 Efficiency Comes at the Expense of Accuracy............................................ 43 Machine Learning Is Only as Good as the Underlying Data..................... 46 ix
Machine Learning for Operational Decisionmaking in Competition and Conflict CHAPTER FIVE Implications for the U.S. Army.................................................................... 49 Research Findings....................................................................................... 49 Recommendations...................................................................................... 51 APPENDIX Training a Machine to Read the News........................................................ 53 Abbreviations................................................................................................. 71 References.......................................................................................................73 X
References Assessing Revolutionary and Insurgent Strategies Project, “Little Green Men”: A Primer on Modern Russian Unconventional Warfare, Ukraine 2013-2014, U.S. Special Operations Command, 2015. As of July 1,2021: https://www.soc.mil/ARIS/books/arisbooks.html Atherton, Kelsey, “Targeting the Future of the DoD’s Controversial Project Maven Initiative,” C4ISRNET, July 27, 2018. As of September 13,2018: https://www.c4isrnet.com/it-networks/2018/07/27/ targeting-the-future-of-the-dods-controversial-project-maven-initiative/ Berruti, Federico, Pieter Nel, and Rob Whiteman, “An Executive Primer on Artificial General Intelligence,” McKinsey Company, April 29,2020. As of September 8,2020: https://www.mckinsey.com/business-functions/operations/our-insights/ an-executive-primer-on-artificial-general-intelligence Callandar, Bruce D„ “The Ground Observer Corps,” Air Force Magazine, February 1, 2006. September 8,2020: https://www.airforcemag.com/article/0206goc/ Clymer, Kenton, “The Ground Observer Corps: Public Relations and the Cold War in the 1950s,” Journal of Cold War Studies, Vol.15, No. 1, Winter 2013. Dear, Keith, “A Very British AI Revolution in Intelligence Is Needed,” War on the Rocks, October 19, 2018. Egel, Daniel, Ryan Andrew Brown, Linda Robinson, Mary Kate Adgie, Jasmin Léveillé, and Luke J. Matthews, Leveraging Machine Learningfor Operation Assessment, RAND Corporation, RR-4196-A, 2022. As of July 31,2023: https://www.rand.org/pubs/research_reports/RR4196.html Fischer, Sabine, The Donbas Conflict: Opposing Interests and Narratives, Difficult Peace Process, Berlin:
German Institute for International and Security Affairs, April 2019. As of September 8,2020: https://www.swp-berlin.Org/10.18449/2019RP05/#en-dl6368el017 Florea, Adrian, “Rebel Governance in De Facto States,” European Journal of International Relations, Vol. 26, No. 4, December 2020. Gibbons-Neff, Thomas, “Three-Day-Old Ceasefire in Ukraine Broken as Fighting Resumes in some Areas,” Washington Post, September 3,2015. Hoffman, Frank, “The Hypocrisy of the Techno-Moralists in the Coming Age of Autonomy,” War on the Rocks, March 6,2019. 73
Machine Learning for Operational Decisionmaking in Competition and Conflict Imperial War Museums, “Support from the Ground in the Battle of Britain,” webpage, undated. As of September 8,2020: https://www.iwm.org.uk/history/ support-from-the-ground-in-the-battle-of-britain Janes Information Services, “Terrorism and Insurgency Database,” 2020. Karatzoglou, Alexandros, Alex Smola, Kurt Hornik, National ICT Austrial, Michael A. Maniscalco, and Choon Hui Teo, “Kernel-Based Machine Learning Lab,” Comprehensive R Archive Network, undated. Karlin, Mara, The Implications ofArtificial Intelligence for National Security Strategy, Brookings Institution, November 1,2018. Kofman, Michael, Katya Migacheva, Brian Nichiporuk, Andrew Radin, Olesya Tkacheva, and Jenny Oberholtzer, Lessonsfrom Russia’s Operations in Crimea and Eastern Ukraine, RAND Corporation, RR-1498-A, 2017. As of July 1,2021: https://www.rand.org/pubs/research_reports/RR1498.html Lim, Nelson, Bruce R. Orvis, and Kimberly Curry Hall, Leveraging Big Data Analytics to Improve Military Recruiting, RAND Corporation, RR-2621-OSD, 2019. As of July 1, 2021: https://www.rand.org/pubs/research_reports/RR2621.html “Luhansk Energy Association to De-Energize Popasnianskyi District Water Supply Channel Supplying with Water 75,000 Residents of Luhansk Region on February 21 Due to UAH 24.3 Million Debts,” Ukrainian News Agency, February 17,2020. McKendrick, Kathleen, “The Application of Artificial Intelligence in Operations Planning,” paper presented at the 11th NATO Operations Research and Analysis (OR A) Conference, October 9,2017. “New
Year Ceasefire Enters into Force in Donbass,” TASS Russian News Agency, December 28,2018. As of September 2018: https://tass.com/world/1038447 Niland, Paul, “Making Sense of Minsk: Decentralization, Special Status, and Federalism,” Atlantic Council, January 27, 2016. Organization for Security and Co-operation in Europe Special Monitoring Mission to Ukraine, “2019 Trends and Observations,” webpage, 2019. As of September 8,2020: https://www.osce.Org/files/f/documents/l/e/444745.pdf Organization for Security and Co-operation in Europe Special Monitoring Mission to Ukraine, “Trends and Observations: Jan-Mar 2020,” webpage, 2020a. As of September 8,2020: https://www.osce.Org/files/f/documents/0/d/450175.pdf 74
References Organization for Security and Co-operation in Europe Special Monitoring Mission to Ukraine, “Trends and Observations: Apr-Jun 2020,” webpage, 2020b. As of September 8, 2020: https://www.osce.Org/files/f/documents/e/d/457987.pdf OSCE SMM Ukraine —See Organization for Security and Co-operation in Europe Special Monitoring Mission to Ukraine. Paul, Christopher, Colin P. Clarke, Bonnie L. Triezenberg, David Manheim, and Bradley Wilson, Improving C2 and Situational Awareness for Operations in and Through the Information Environment, RAND Corporation, RR-2489OSD, 2018. As of July 1, 2021: https://www.rand.org/pubs/research_reports/RR2489.html Pellerin, Cheryl, “Project Maven to Deploy Computer Algorithms to War Zone by Year’s End,” press release, U.S. Department of Defense, July 21,2017. Raleigh, Clionadh, Andrew Linke, Havard Hegre, and Joakim Karlsen, “Introducing ACLED-Armed Conflict Location and Event Data,” Journal of Peace Research, Vol. 47, No. 5, September 2010. Robinson, Eric, Daniel Egel, Patrick Johnston, Sean Mann, Alex Rothenberg, and David Stebbins, When the Islamic State Comes to Town: The Economic Impact of Islamic State Governance in Iraq and Syria, RAND Corporation, RR-1970-RC, 2017. As of July 1,2021: https://www.rand.org/pubs/research_reports/RR1970.html Robinson, Linda, Daniel Egel, and Ryan Andrew Brown, Measuring the Effectiveness of Special Operations, RAND Corporation, RR-2504-A, 2019. As of July 1, 2021: https://www.rand.org/pubs/research_reports/RR2504.html Ross, Casey, and Ike Swetlitz, “IBM Pitched Its Watson Supercomputer as a Revolution
in Cancer Care. It’s Nowhere Close,” STAT, September 5, 2017. As of September 8, 2020: https://www.statnews.com/2017/09/05/watson-ibm-cancer/ “Russia Sends 98th Humanitarian Convoy to Donbas, Ukraine’s Foreign Ministry Protests,” 112 Ukraine, August 28, 2020. As of September 8,2020: https ://l 12 .international/conflict-in-eastern-ukraine/ russia-sends-98th-humanitarian-convoy-to-donbas-ukraines-foreignministry-protests-54243.html Sayler, Kelley Μ., Artificial Intelligence and National Security, Congressional Research Service, R45178, August 26,2020. Scaparrotti, Curtis, “USEUCOM 2019 Posture Statement,” testimony before the Senate Armed Services Committee, March 5,2019. As of September 8, 2020: https://www.eucom.mil/article/39546/useucom-2019-posture-statement 75
Machine Learning for Operational Decisionmaking in Competition and Conflict Schuety, Clayton, and Lucas Will, “An Air Force ‘Way of Swarm’: Using Wargaming and Artificial Intelligence to Train Drones,” War on the Rocks, September 21,2019. Stone, Adam, “Army Logistics Integrating New AI, Cloud Capabilities,” C4ISRNet, September 7,2017. As of September 8,2020: https://www.c4isrnet.com/home/2017/09/07/ army-logistics-integrating-new-ai-cloud-capabilities/ Sukman, Daniel, “The Institutional Level of War,” Strategy Bridge, May 5, 2016. As of July 1,2021: https://thestrategybridge.Org/the-bridge/2016/5/5/ the-institutional-level-of-war U.S. Department of Defense, “DOD Announces $250M to Ukraine,’’press release, June 11,2020. As of September 8,2020: https://www.defense.gov/Newsroom/Releases/Release/Article/2215888/ dod-announces-250m-to-ukraine/ van den Bosch, Karel, and Adelbert Bronkhorst, Human-AI Cooperation to Benefit Military Decision Making,” paper presented at the Big Data and Artificial Intelligence for Military Decision Making conference, Bordeaux, France, May 30-June 1,2018. As of September 8,2020: https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/ STO-MP-IST-160/MP-IST-160-S3-l.pdf Vreeland, Hans, “Targeting the Islamic State, or Why the Military Should Invest in Artificial Intelligence,” War on the Rocks, May 16, 2019. Winn, Zach, “A Human-Machine Collaboration to Defend Against Cyberattacks,” MIT News, February 21,2020. As of September 6,2020: https://news.mit.edu/2020/patternex-machine-learning-cybersecurity-0221 Work, Robert, “Remarks by Defense
Deputy Secretary Robert Work at the CNAS Inaugural National Security Forum,” Center for a New American Security, December 14,2015. Wolters, Tod D., “USEUCOM 2020 Posture Statement,” testimony before the Senate Armed Services Committee, February 25,2020. As of September 2014: https://www.eucom.mil/document/40291/ general-wolters-fy2021-testimony-to-the-senat f Bayerische Staatsbibliothek München 76
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| spellingShingle | Robinson, Eric Egel, Daniel Bailey, George 1919-2001 Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine Militär (DE-588)4039305-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine |
| title_auth | Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine |
| title_exact_search | Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine |
| title_full | Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine Eric Robinson, Daniel Egel, George Bailey |
| title_fullStr | Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine Eric Robinson, Daniel Egel, George Bailey |
| title_full_unstemmed | Machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in Eastern Ukraine Eric Robinson, Daniel Egel, George Bailey |
| title_short | Machine learning for operational decisionmaking in competition and conflict |
| title_sort | machine learning for operational decisionmaking in competition and conflict a demonstration using the conflict in eastern ukraine |
| title_sub | a demonstration using the conflict in Eastern Ukraine |
| topic | Militär (DE-588)4039305-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Militär Maschinelles Lernen Ukraine |
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