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Alexa, Write my OPORD: Promise and Pitfalls of Machine Learning for Commanders in Combat

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By Jeff Wong

Introduction

Jump into the time machine and fast forward a few years into the future. The United States is at war, things are not going well, and the brass wants to try something new. John McCarthy,1 a Marine lieutenant colonel whose knowledge of technology is limited to the Microsoft Office applications on his molasses-slow government laptop, mulls over his tasks, as specified by his two-star boss, the commander of Joint Task Force 58:

1. Convene a joint planning group to develop a plan for the upcoming counteroffensive. (Check.)

2. Leverage representatives from every staff section and subject-matter experts participating virtually from headquarters in Hawaii or CONUS. (Roger.)

3. Use an experimental machine-learning application to support the planning and execution of the operation. (We’re screwed.)

Nearly 7,000 miles from a home he might never see again, McCarthy considered two aphorisms. The first was from Marcus Aurelius, the second-century Roman emperor and stoic thinker: “Never let the future disturb you. You will meet it, if you have to, with the same weapons of reason which today arm you against the present.” 2 The second was from Mike Tyson, the fearsome boxer, convicted felon, and unlikely philosopher: “Everybody has a plan until they get punched in the mouth.”3

Artificial intelligence (AI), including large-language models (LLMs) such as ChatGPT, is driving a societal revolution that will impact all aspects of life, including how nations wage war and secure their economic prosperity. “The ability to innovate faster and better – the foundation on which military, economic, and cultural power now rest – will determine the outcome of great-power competition between the United States and China,” notes Eric Schmidt, the former chief executive officer of Google and chair of the Special Competitive Studies Project.4 The branch of AI that uses neural networks to mimic human cognition — machine learning (ML) — offers military planners a powerful tool for planning and executing missions with greater accuracy, flexibility, and speed. Senior political and military leaders in China share this view and have made global technological supremacy a national imperative.5

Through analyzing vast amounts of data and applying complex algorithms, ML can enhance situational awareness, anticipate threats, optimize resources, and adapt to generate more successful outcomes. However, as ML becomes more widespread and drives technological competition against China, American military thinkers must develop frameworks to address the role of human judgment and accountability in decision-making and the potential risks and unintended consequences of relying on automated systems in war.

To illustrate the promise and pitfalls of using ML to support decision-making in combat, imagine the pages of a wall calendar flying to some point when America must fight a war against a peer adversary. Taking center stage in this fictional journey are two figures: The first is McCarthy, an officer of average intelligence who only graduated from the Eisenhower School for National Security and Resource Strategy thanks to a miraculous string of B+ papers at the end of the academic year. The second is “MaL,” a multimodal, LLM that is arguably the most capable – yet most immature – member of McCarthy’s planning team. This four-act drama explores how McCarthy and his staff scope the problems they want MaL to help them solve, how they leverage ML to support operational planning, and how they use MaL to support decision-making during execution. The final act concludes by offering recommendations for doctrine, training and education, and leadership and policies to better harness this capability in the wars to come.

Act One: “What Problems Do We Want This Thing to Solve?”

The task force was previously known as “JTF-X,” an experimental formation that blended conventional legacy platforms with AI and autonomous capabilities. As the tides of war turned against the United States and its allies, the Secretary of Defense (SecDef) pressured senior commanders to expand the use of AI-enabled experimental capabilities. Rather than distribute its capabilities across the rest of the joint force, the SecDef ordered JTF-X into duty as a single unit to serve as the theater reserve for a geographic combatant commander. “Necessity breeds innovation… sometimes kicking and screaming,” she said.

 Aboard the JTF command ship in a cramped room full of maps, satellite imagery, and charts thick with unit designators, McCarthy stared at a blinking cursor on a big-screen projection of MaL. Other members of his OPT looked over his shoulder as he impulsively typed out, “Alexa, write my OpOrd.” Undulating dots suggested MaL was formulating a response before MaL responded, “I’m not sure what you’re asking for.”

The JPG chief, an Air Force senior master sergeant, voiced what the humans in the room were thinking: “Sir, what problems do we want this thing to solve?”

The incredible capacity of ML tools to absorb, organize, and generate insights from large volumes of data suggests that they hold great promise to support operational planning. Still, leaders, planners, and ML tool developers must determine how best to harness the capability to solve defined military problems. For instance, the Ukrainian military uses AI to collect and assess intelligence, surveillance, and reconnaissance (ISR) data from numerous sources in the Russia-Ukraine conflict.6 But as Ukrainian planners are probably discovering today, they must do more than throw current ML tools at problem sets. Current LLMs fall short of desired capabilities to help human planners infer and make sense within the operating environment. Military professionals must tailor the problem sets to match the capabilities and limitations of the ML solutions.

Although tools supporting decision advantage comprised a small fraction of the 685 AI-related projects and accounts within the DoD as of 2021, existing efforts align with critical functions such as the collection and fusion of data from multiple domains (akin to the DoD’s vision for Joint All-Domain Command and Control (JADC2)); multidomain decision support for a combatant command headquarters; automated analysis of signals across the electromagnetic spectrum; and location of bots to support defensive cyber operations.7 There are numerous tools with various tasks and functions, but the crux of the problem will be focusing the right tool or set of tools on the appropriate problem sets. Users need to frame planning tasks and precisely define the desired outputs for a more immature LLM capability, such as the fictional MaL.

McCarthy mapped out a task organization for the JPG to align deliverables with available expertise. The team chief scribbled dates and times for upcoming deliverables on a whiteboard, including the confirmation brief for the commander in 24 hours. An Army corporal sat before a desktop computer to serve as the group’s primary interface with MaL. To help the group develop useful queries and troubleshoot, MaL’s developers in Hawaii participated via a secure video teleconference.

MaL was already able to access volumes of existing data – operations and contingency plans, planning artifacts from previous operations, ISR data from sensors ranging from national assets to stand-in forces in theater, fragmentary orders, and mountains of open-source information.

Act Two: MaL Gets Busy to the ‘Left of the Bang’

Some observers believe that ML capabilities can flatten the “orient” and “decide” steps of the late military theorist John Boyd’s observe-orient-decide-act decision (OODA) loop, expanding a commander’s opportunity to understand context, gain an appreciation of the battlespace, frame courses of action, and explore branches and sequels to inform decisions.8 Nevertheless, the greater capacity that ML tools provide does not eliminate the need for leaders to remain intimately involved in the planning process and work with the platform to define decision points as they weigh options, opportunities, risks, and possible outcomes.

Planners should examine frameworks such as the OODA Loop, IPB, and the Joint Operational Planning Process (JOPP) to guide where they could apply ML to maximum effect. To support IPB, ML can automate aspects of collection and processing, such as identifying objects, selecting potential targets for collection, and guiding sensors. ML capabilities for image and audio classification and natural language processing are already in commercial use. They support essential functions for autonomous vehicles, language translation, and transit scheduling. These commercial examples mirror military use cases, as nascent platforms fuse disparate data from multiple sources in all five warfighting domains.9

MaL’s digital library included the most relevant intelligence reports; adversary tactics, techniques, and procedures summaries; videos of possible target locations taken by uncrewed systems; raw signals intelligence; and assessments of the enemy orders of battle and operational tendencies from the early months of the conflict. The corpus of data also included online news stories and social media postings scraped by an all-source intelligence aggregator.

 McCarthy said, “As a first step, let’s have MaL paint the picture for us based on the theater-level operational context, then create an intelligence preparation of the battlespace presentation with no more than 25 PowerPoint slides.” After the clerk entered the query, the graphic of a human hand drumming its fingers on a table appeared.

Two minutes later, MaL saved a PowerPoint file on the computer’s desktop and announced in a metallic voice, “Done, sir, done!” McCarthy and his J-2 reviewed the IPB brief, which precisely assessed the enemy, terrain, weather, and civil considerations. MaL detailed the enemy’s most likely and dangerous courses of action, discussed adversary capabilities and limitations across all domains, and provided a target-value analysis aligning with the most recent intelligence. The J-2 reviewed the product and said, “Not bad.” She added, “Should I worry about losing my job?”

“Maybe I should worry about losing mine,” McCarthy said. “Let’s go through the planning process with MaL and have it generate three COAs based on the commander’s planning guidance and intent statement.”

American military planning frameworks – JOPP and its nearly identical counterparts across the services – are systematic processes that detail the operational environment, the higher commander’s intent, specified and implied tasks, friendly and enemy COAs, and estimates of supportability across all warfighting functions. They benefit the joint force by establishing uniform expectations about the information needed to support a commander’s estimate of the situation. However, current planning frameworks may hinder decision-making because a commander and his staff may become too focused on the process instead of devoting their energies and mental bandwidth to quickly orienting themselves to a situation and making decisions. Milan Vego, a professor of operational art at the U.S. Naval War College, writes of “a great temptation to steadily expand scope and the amount of information in the (commander’s) estimate. All this could be avoided if commanders and staffs are focused on the mental process and making a quick and good decision.”10

An ML-enabled decision-support capability could help planners stay above process minutiae by suggesting options for matching available weapon systems to targets, generating COAs informed by real-time data, and assessing the likelihood of success for various options in a contemporary battlespace, which features space and cyberspace as contested warfighting domains.11

MaL developed three unacceptable COAs which variously exceeded the unit’s authorities as outlined in the JTF’s initiating directive or extended kinetic operations into the adversary’s mainland, risking escalation.

McCarthy rubbed his face and said, “Time for a reboot. Let’s better define constraints and restraints, contain operations in our joint operational area, and have it develop assessments for risks to mission and force.” He added, “And this time, we’ll try not to provoke a nuclear apocalypse.”

The planning team spent several more hours refining their thoughts, submitting prompts, and reviewing the results. Eventually, MaL generated three COAs that were feasible, acceptable, complete, and supportable. MaL tailored battlespace architecture, fire support coordination measures, and a detailed sustainment plan for each COA and mapped critical decision points throughout the operation. MaL also assisted the JTF air officer develop three distinct aviation support plans for each COA. 

The planning team worked with MaL to develop staff estimates for each COA. The logistics and communications representatives were surprised at how quickly MaL produced coherent staff estimates following a few hours of queries. The fires, intelligence, and maneuver representatives similarly worked with MaL to develop initial fire support plans to synchronize with the group’s recommended COA.

McCarthy marveled at MaL’s ability to make sense of large amounts of data, but he was also surprised at the ML platform’s tendency to misinterpret words. For instance, it continually conflated the words “delay,” “disrupt,” and “destroy,” which were distinct tactical tasks with different effects on enemy formations. The planning team reviewed MaL’s COA overviews and edited the platform’s work. The staff estimates were detailed, and insightful, but still required corrections.

During the confirmation brief, the JTF commander debated several details about MaL’s outputs and risk assessments of the planning team’s recommended COA. McCarthy said, “Respectfully, Admiral, this is your call. MaL is one tool in your toolbox. We can tweak the COA to suit your desires. We can also calibrate the level of automation in the kill chain based on your intent.”

After a moment, the admiral said, “I’ll follow the planning team’s recommendation. Notice that I didn’t say MaL’s recommendation because MaL is just one part of your team.”

Act Three: MaL Lends a Hand to the Right of the ‘Bang’

Contemporary military thinkers believe that ML-enabled tools could improve decision-making during the execution of an operation, leveraging better situational awareness to suggest more effective solutions for problems that emerge in a dynamic battlespace. However, critics argue that developing even a nascent ML-enabled capability is impossible because of the inherent limits of ML-enabled platforms to generate human-like reasoning and infer wisdom from incomplete masses of unstructured data emanating from a 21st-century battlefield. Some are also concerned about the joint force’s ability to send and receive data from a secure cloud subject to possible malicious cyber activities or adversarial ML. Prussian military thinker Carl von Clausewitz reminds practitioners of the operational art that “War is the realm of uncertainty; three-quarters of the factors on which action in war is based are wrapped in a fog of greater or lesser uncertainty.”12 Technological solutions such as ML-enabled capabilities can temporarily lift parts of this fog for defined periods. Still, users must understand the best use of these capabilities and be wary of inferring certainty from their outputs.

Emerging capabilities suggest that ML can augment and assist human decision-making in several ways. First, ML systems can enhance situational awareness by establishing and maintaining a real-time common operational picture derived from multiple sensors in multiple domains. Greater battlespace awareness provides context to support better decision-making by commanders and more accurate assessments of events on the battlefield. Second, ML can improve the effectiveness and efficiency of kill-chain analytics by quickly matching available sensors and shooters with high-value or high-payoff targets.13 This efficiency is essential in the contemporary battlespace, where ubiquitous sensors can quickly detect a target based on a unit’s emissions in the electromagnetic spectrum or signatures from previously innocuous activities like an Instagram post or a unit’s financial transaction with a host-nation civilian contractor.

Indeed, some AI observers in the U.S. defense analytic community argue that warfighters must quickly adopt ML to maintain a competitive edge against the People’s Liberation Army, which some observers believe will favor the possible gains in warfighting effectiveness and efficiency over concerns about ethical issues such as implementing human-off-the-loop AI strategies.14 ML-enabled feedback constructs will enhance the control aspects of command and control to employ a large, adaptable, and complex multidomain force.15

It was now D+21. JTF-58 achieved its primary objectives, but the campaign did not unfold as intended. During the shaping phase of the operation, several high-value targets, including mobile anti-ship cruise missile batteries, escaped kinetic targeting efforts, living to fight another day and putting U.S. naval shipping at risk for the latter phases of the campaign. MaL failed to update the developers’ advertised “dynamic operating picture” despite attempts by forward-deployed sensors and reconnaissance teams to report their movement. Incredibly, the DoD still did not have access to data from some weapon systems due to manufacturer stipulations.16

MaL’s developers insisted that the forward-deployed sensors should have had enough computing power to push edge data to the JTF’s secure cloud. The CommO speculated that environmental conditions or adversary jamming could have affected connectivity. McCarthy shook his head and said, “We need to do better.”

MaL performed predictably well in some areas to the right of the bang. The task force commander approved using MaL to run networked force protection systems, including a Patriot battery that successfully intercepted an inbound missile and electronic-warfare (EW) systems that neutralized small unmanned aerial systems targeting a fuel farm. MaL’s use in these scenarios did not stretch anyone’s comfort level since these employment methods were barely different than the automation of systems like the U.S. Navy’s Phalanx close-in weapon system (CIWS), which has detected, evaluated, tracked, engaged, and conducted kill assessments of airborne threats for more than four decades.17

MaL’s communications and logistics staff estimates were precise and valuable for the staff. The CommO adjusted the tactical communications architecture based on MaL’s predictions about enemy EW methods and the effects of weather and terrain on forward maneuver elements. Similarly, the LogO worked with the OpsO to establish additional forward-arming and refueling points (FARPs) based on MaL’s fuel and munitions consumption projections.

In the middle of the operation, the task force commander issued a fragmentary order to take advantage of an unfolding opportunity. MaL leveraged open-source data from host-nation news websites and social media postings by enemy soldiers to inform battle damage assessment of kinetic strikes. Some of that information was fake and skewed the assessment until the intelligence officer corrected it by comparing it with satellite imagery and human intelligence reporting.

As with any emerging capability, commanders and their staffs must consider the risks of integrating ML into the planning, execution, and assessment of operations. One of the risks is inherent in forecasting, as the ML platform artificially closes the feedback loop to a decision-maker sooner than one would expect during real-world operations. Retired U.S. Navy Captain Adam Aycock and Naval War College professor William Glenney IV assert that this lag might make ML outputs moot when a commander makes a decision. “The operational commander might not receive feedback, and the effects of one move might not be recognized until several subsequent moves have been made,” Aycock and Glenney write. “Furthermore, a competent enemy would likely attempt to mask or manipulate this feedback. Under such circumstances … it is difficult to ‘learn’ from a first move in order to craft the second.”18

Another risk is that the data used by ML platforms are, in some way, inaccurate, incomplete, or unstructured. Whether real or training, flawed data will lead to inaccurate outputs and likely foul an ML-enabled tool’s assessment of the environment and COA development. “Unintentional failure modes can result from training data that do not represent the full range of conditions or inputs that a system will face in deployment,” write Wyatt Hoffman and Heeu Millie Kim, researchers with the Center for Security and Emerging Technology at Georgetown University. “The environment can change in ways that cause the data used by the model during deployment to differ substantially from the data used to train the model.”19

The corollary to inaccurate data is adversarial ML, in which an enemy manipulates data to trick an ML system, degrade or disrupt optimal performance, and erode users’ trust in the capability. Adversarial ML tricks can trick an ML model into misidentifying potential targets or mischaracterizing terrain and weather. In one notable demonstration of adversarial ML, researchers at the Chinese technology giant Tencent placed stickers on a road to trick the lane recognition system of a Tesla semi-autonomous car, causing it to swerve into the wrong lane.20 Just the possibility of a so-called “hidden failure mode” could exacerbate fears about the reliability of any ML-enabled system. “Operators and military commanders need to trust that ML systems will operate reliably under the realistic conditions of a conflict,” Hoffman and Kim write. “Ideally, this will militate against a rush to deploy untested systems. However, developers, testers, policymakers, and commanders within and between countries may have very different risk tolerances and understandings of trust in AI.”21

Act Four: Hotwash

McCarthy took advantage of an operational pause to conduct a hotwash. Over lukewarm coffee and Monsters, the conversation gravitated toward how they could use MaL better. The group scribbled a few recommendations concerning integrating ML into doctrine, training and education, and leadership and policies until the ship’s 1-MC sounds general quarters. 

Doctrine: To realize the utility of ML, military leaders should consider two changes to existing doctrine. First, doctrine developers and the operational community should consider the concept of “human command, machine control,” in which ML would use an auction-bid process akin to ride-hailing applications to advertise and fulfill operational requirements across the warfighting functions. Under this construct, a commander publishes or broadcasts tasks, including constraints, priorities, metrics, and objectives. “A distributed ML-enabled control system would award-winning bids to a force package that could execute the tasking and direct the relevant forces to initiate the operation,” write naval theorists Harrison Schramm and Bryan Clark. “Forces or platforms that accept the commander’s bid conducts (or attempts to conduct) the mission and reports the results “when communications availability allows.”22 This concept supports mission-type orders/mission command and allows C2 architectures to flex to instances and areas subject to low-bandwidth constraints.23

Second, doctrine developers should adjust joint, service, and domain-centric planning processes to account for additional planning aids, such as LLMs, modeling and simulation, and digital twins, which can more deeply explore COAs, branches, and sequels and accelerate understanding of threats and the operating environment. Explicitly changing planning doctrine to account for these emerging capabilities will encourage their use and emphasize their criticality to effective planning.

Training and Education: Tactical units must train and continually develop ML technical experts capable of conducting on-the-spot troubleshooting and maintenance of the tool. Meanwhile, the services should develop curricula to train budding junior leaders — corporals, sergeants, lieutenants, ensigns, and captains — that introduce them to machine-learning tools applicable to their warfighting domains, provide best principles for generating productive outputs, and articulate risks – and risk mitigations – due to skewed data and poor problem framing.

Best practices should also be documented and shared across the DoD. Use of ML capabilities should become part of a JPG’s battle drill, featuring a designated individual whose primary duty is to serve as the human interface with a decision-support tool such as MaL. Rather than work from scratch at the start of every planning effort, JPGs should have a list of queries readily available for a range of scenarios that can inform a commander’s estimate of the situation and subsequent planning guidance, and formulation of intent based on an initial understanding of the operating environment. Prompts that solicit ML-enabled recommendations on task organization, force composition and disposition, risks to force or mission, targeting, and other essential decisions should be ready for immediate use to speed the JPG’s planning tempo and, ultimately, a unit’s speed of action as well. The information management officer (IMO), which in some headquarters staffs is relegated to updating SharePoint portals, should be the staff’s subject matter expert for managing ML capabilities. IMOs would be the military equivalent of prompt engineers to coax and guide AI/ML models to generate relevant, coherent, and consistent outputs to support the unit’s mission.24

Leadership and Policies: There are implications for senior leaders for warfighting and policy. Within a warfighting context, senior defense leaders must identify, debate, and develop frameworks for how commanders might use ML to support decision-making in wartime scenarios. It seems intuitive to use a multimodal LLM tool such as the fictitious MaL to support IPB, targeting, and kill chain actions; in the same way, campaign models are used to inform combatant commander planning for crises and contingencies.

However, leaders and their staffs must also understand the limitations of such tools to support a commander’s decision-making. “Do not ask for an AI-enabled solution without first deciding what decision it will influence and what the ‘left and right limits’ of the decision will be,” Schramm and Clark warn.25 Likewise, AI might not be the appropriate tool to solve all tactical and operational problems. “Large data-centric web merchants such as Amazon are very good at drawing inferences on what people may be interested in purchasing on a given evening because they have a functionally infinite sample space of previous actions upon which to build the model,” they write. “This is radically different from military problems where the amount of data on previous interactions is extremely small and the adversary might have tactics and systems that have not been observed previously. Making inference where there is little to no data is the domain of natural intelligence.”26

Meanwhile, future acquisition arrangements with defense contractors must provide the DoD with data rights – particularly data generated by purchased weapon systems and sensors – to optimize the potential of ML architecture in a warfighting environment.27 Such a change would require the DoD to work with firms in the defense industrial base to adjudicate disagreements over the right to use, licensing, and ownership of data – each of which might bear different costs to a purchaser.

Epilogue

Technologists and policy wonks constantly remind the defense community that the Department must “fail fast” to mature emerging technologies and integrate them into the joint force as quickly as possible. The same principle should guide the development of AI/ML-enabled warfighting solutions. Commanders and their staffs must understand that this is a capable tool that, if used wisely, can significantly enhance the joint force’s ability to absorb data from disparate sources, make sense of that information, and close kill chains based on an ML tool’s assessment.

If used unwisely, without a solid understanding of what decisions ML will support, the joint force may be playing a rigged game against a peer adversary. ML-enabled capabilities can absorb large amounts of data, process and organize it, and generate insights for humans who work at a relative snail’s pace. However, these nascent tools cannot reason and interpret words or events as a competent military professional can. As strategic competition between the United States and China intensifies over Taiwan, the South China Sea, the Russian-Ukraine war, and other geopolitical issues, American political and military leaders must develop a better understanding of when and how to use ML to support joint force planning, execution, and assessment in combat, lest U.S. service members pay an ungodly sum of the butcher’s bill.

Lieutenant Colonel Jeff Wong, a U.S. Marine Corps reserve infantry officer, studied artificial intelligence at the Eisenhower School for National Security and Resource Strategy, National Defense University in the 2022-2023 academic year. In his civilian work, he plans wargames and exercises for various clients across the Department of Defense.

The views expressed in this paper are those of the author and do not necessarily reflect the official policy or position of the National Defense University, the Department of Defense, or the U.S. Government.

References

1. The fictional hero of this story, John McCarthy, is named after the Massachusetts Institute of Technology researcher who first coined the term “artificial intelligence.” Gil Press, “A Very Short History of Artificial Intelligence,” Forbes, December 30, 2016, https://www.forbes.com/sites/gilpress/2016/12/30/a-very-short-history-of-artificial-intelligence-ai/?sh=51ea3d156fba.

2. Marcus Aurelius, Meditations, audiobook.

3. Mike Berardino, “Mike Tyson Explains One of His Most Famous Quotes,” South Florida Sun-Sentinel, November 9, 2012, https://www.sun-sentinel.com/sports/fl-xpm-2012-11-09-sfl-mike-tyson-explains-one-of-his-most-famous-quotes-20121109-story.html.

4. Eric Schmidt, “Innovation Power: Why Technology Will Define the Future of Geopolitics,” Foreign Affairs, March/April 2023.

5. “Military-Civil Fusion and the People’s Republic of China,” U.S. Department of State, May 2020.

6. Eric Schmidt, “Innovation Power: Why Technology Will Define the Future of Geopolitics,” Foreign Affairs, March/April 2023.

7. Wyatt Hoffman and Heeu Millie Kim, “Reducing the Risks of Artificial Intelligence for Military Decision Advantage,” Center for Security and Emerging Technology Policy Paper (Washington, D.C.: Georgetown University, March 2023), 12.

8. James Johnson, “Automating the OODA Loop in the Age of AI,” Center for Strategic and International Studies, July 25, 2022, https://nuclearnetwork.csis.org/automating-the-ooda-loop-in-the-age-of-ai/.

9. Hoffman and Kim, “Reducing the Risks of Artificial Intelligence for Military Decision Advantage,” 7.

10. Milan Vego, “The Bureaucratization of the U.S. Military Decision-making Process,” Joint Force Quarterly 88, January 9, 2018, https://ndupress.ndu.edu/Publications/Article/1411771/the-bureaucratization-of-the-us-military-decisionmaking-process/.

11. Hoffman and Kim, 7.

12. Carl von Clausewitz, On War, ed. and trans. Michael Howard and Peter Paret (Princeton: Princeton University Press, 1976), 101.

 13. Hoffman and Kim, 7.

 14. Elsa Kania, “AI Weapons” in China’s Military Innovation, Brookings Institution, April 2020.

 15. Harrison Schramm and Bryan Clark, “Artificial Intelligence and Future Force Design,” in AI at War (Annapolis, Md.: Naval Institute Press, 2021), 240-241.

16. Josh Lospinoso, Testimony on the State of Artificial Intelligence and Machine Learning Applications to Improve Department of Defense Operations before the Subcommittee on Cybersecurity, U.S. Senate Armed Services Committee, April 19, 2023, https://www.armed-services.senate.gov/hearings/to-receive-testimony-on-the-state-of-artificial-intelligence-and-machine-learning-applications-to-improve-department-of-defense-operations. “Today, the Department of Defense does not have anywhere near sufficient access to weapon system data. We do not – and in some cases, due to contractual obligations, the Department cannot — extract this data that feeds and enables the AI capabilities we will need to maintain our competitive edge.”

 17. MK15 – Phalanx Close-In Weapon System (CIWS), U.S. Navy, September 20, 2021, https://www.navy.mil/resources/fact-files/display-factfiles/article/2167831/mk-15-phalanx-close-in-weapon-system-ciws/.

18. Adam Aycock and William Glenney IV, “Trying to Put Mahan in a Box,” in AI at War, 269-270.

19. Hoffman and Kim, CSET Policy Brief, 8.

20. Ibid, 8-9.

21. Ibid, 11.

22. Schramm and Clark, “Artificial Intelligence and Future Force Design,” 239-241.

23. AI at War, 241.

24. Craig S. Smith, “Mom, Dad, I Want To Be A Prompt Engineer,” Forbes, April 5, 2023, https://www.forbes.com/sites/craigsmith/2023/04/05/mom-dad-i-want-to-be-a-prompt-engineer/amp/.

25. AI at War, 247-248.

26. AI at War, 248.

27. Heidi M. Peters, “Intellectual Property and Technical Data in DoD Acquisitions,” Congressional Research Service In-Focus, IF12083, April 22, 2022, https://crsreports.congress.gov/product/pdf/IF/IF12083.

Featured Image: PHILIPPINE SEA (Sept. 22, 2020) Cpl. Clayton A. Phillips, a network administrator with Marine Air Control Group 18 Detachment, 31st Marine Expeditionary Unit (MEU), and a native of Beech Bluff, Tennessee, tests the connectivity of the Networking On-the-Move Airborne (NOTM-A) communications system during flight operations from the amphibious assault ship, USS America (LHA 6). (U.S. Marine Corps photo by Lance Cpl. Brienna Tuck)

Future Tech

@Channel – A Dialogue Concerning Kill Webs

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By The Naval Constellation

The Naval Constellation is an online, unofficial forum resident on the team communication application Slack. The group includes Navy, Marine Corps, and Coast Guard officers, enlisted, and civilians, and serves as a place to break down organizational silos and facilitate conversation on topics ranging from innovation to strategy, emerging technology, and more. While it has existed and grown for six years, we are now partnering with CIMSEC on an enduring public series, “@Channel,” to be published as the conversation warrants it. Contact information to join future conversations like this can be found at the bottom of the article.

The below is a discussion from the Constellation between 11 participants identified by first name. It has been lightly edited for clarity. All content is submitted with the participants’ consent.

Shane: @channel, Kyle and I are discussing how the Joint Force ought to think about warfare as disabling or breaking down kill webs rather than kill chains; that these webs should be thought of almost as complex adaptive systems (CASs), not multiple reducible chains of effects. What’s the best mental model for attacking or defending against kill webs in war?

Jason: Kill chains are single path, unidirectional, and fairly fragile. Kill webs are multipath, multi-directional, and resilient. To take out a web you have to affect the node and surrounding nodes… That means you need to affect proximal relationships, not just specific targets. Counterintuitively, precision fires don’t work on kill webs. You need an area of effect and less precise methods.

Throw a rock through the spider web, don’t clip individual strands.

DARPA’s Adapting Cross-Domain Kill-Webs (ACK) Program
Figure 1 – Depiction of DARPA’s Adapting Cross-Domain Kill-Webs (ACK) Program. 

Chris: I think of kill chains as being the decision process. We are hindered by the chain of command and decision process that is fragile effect and does have a single path. Kill webs are what Jason described above, but they are aspirational right now. I’d argue that most of our systems are not part of kill webs and are still really fragile. Are we that adaptive? Do our systems actually work that way outside of a very structured exercise?

And if they are, will our Command and Control (C2) process actually let us operate like that?

Jason: Kill webs take a lot of energy to maintain. They are resilient, but if disrupted are more difficult to restore. I recommend a hybrid approach.

Kyle: Would you describe China’s A2AD networks as kill webs? Is there any difference?

Jason: Certainly. Highly resilient, taking out a single node won’t affect the web, but a significant enough, broad disruption, and the system would be hard to reconstitute.1

Figure 2 – An interpretation of the kill web-centric Mosaic Warfare concept for a Chinese audience, published in the April 2021 edition of the PLA journal Aero Weaponry.

Chris: Jason, do you design a kill web so that it fails to a chain? Or do you choose what systems are part of a web and which are a chain? 

Jason: It needs to fail to chains. That’s essentially what graceful degradation is… The reduction of nodal complexity until the issue is solved. Isolate the system, identify the problem, and reconstitute the system. For example, we should actually practice moving from battlegroup, coordinated operations to a unit, disconnected independent ops, and back again. The back again shouldn’t be “the network is back.” That’s unrealistic.

Instead, its independent ops, becoming two ships talking, becoming a group of ships coordinating, becomes battlegroup operations.

We actually do this in damage control. Think about engineering redundancy. We make the system more complicated than it has to be by building redundant systems. Two of the same system with a series of crossovers and disconnects. If something leaks in the system, you can isolate a portion without fully shutting down the system. You may have some penalties to efficiency… Can only operate with a 50% flow rate for example… But the system keeps working and furthermore… The degradation makes the system simpler to troubleshoot and operate.

Shifting back to the more complex operations should be deliberate – just because you fixed one thing, doesn’t mean there aren’t secondary issues you will find when you bring it back up to a higher level of complexity… Open the isolation valves too fast and you end up with 100% flow against potential unknown issues you haven’t fixed yet.

Shane: Do we do enough to train the Information Warfare Community (IWC) in how to understand and potentially disrupt, degrade, deny or destroy complex systems like kill webs?

Ryan: We absolutely do not do enough to train the IWC in this.

That’s the issue with webs and warfare at the liminal edge. We believe that shooting a few multimillion-dollar surface-to-air missiles (SAM) at an ISR (Intelligence, Surveillance and Reconnaissance) drone is Distributed Maritime Operations (DMO), or that mixing up a CVW (Carrier Air Wing) composition enables more resiliency. Neither are webs – they are chains – and we think of warfare in a chain mentality.

The Navy fundamentally lacks the ability to see outside the cave and assess how cyber or info ops might result in degraded C2 from a geographic node in the web. Or space-based effects. Nor would JFMCCs (Joint Force Maritime Component Commanders) know how to employ that kind of stuff at the Fleet or AOR (Area of Operations) level.

Kurt: I’d argue no one in the USG (United States Government) can assess cyber ops, and no one in the US military (except maybe the bubbas at CYBERCOM) trust that cyber can reliably deliver the right effect at the right time for cyber to be selected before a traditional kinetic option is selected.

Ryan: I think breaking up kill webs requires a truly joint effort. That’s not something we can expect our CSGs (Carrier Strike Groups) or single DDGs (Guided Missile Destroyers) to determine and execute alone.2

China, on the other hand, does this really well with its joint structure and national technical means. We would do well to think on how that can be broken down, and how a similar construct “with American characteristics” might be developed to serve attacking CASs.3

Kyle: So as much as I understand the concepts we’re applying here, what I struggle with is assigning the WHAT to invest in and WHO needs WHAT training.

Is this Fleet staffs? Individual ships or units? Everyone? What are we buying that would turn our chains into webs, and is that even feasible with our current acquisition process? Isn’t a “Cloud of Fire Control Data” sort of what you want?4

Kurt: I think the Navy can’t do it alone.

Alex: Kurt, because the Navy doesn’t have the assets to populate a web, i.e. “this is inherently ‘Joint’”? Or because culturally the Navy is resistant to implementing the organizational changes to exploit a web should it exist?

Ryan: Yes to both.

Kurt: Maybe I don’t know what you all mean by “web.” I’m assuming you mean some ultra-resilient system (like a mesh network) exists that can absorb significant damage before a worrisome degradation of capability occurs. And if that damage isn’t adequately and appropriately applied, the system would shrug off the attack with very little actual impact to the overall system.

Kurt: I said the Navy can’t do it alone because the Navy almost assuredly doesn’t have the fielded capabilities to unilaterally destroy what I (perhaps incorrectly) am assuming is a multi-domain, multi-modal “kill web.” Ignoring everything else, the way the DoD (Department of Defense) allocates (or doesn’t, as it happens) cyber forces would preclude Navy cyber from having a major role in fights occurring outside certain geographic boundaries.

Alex: Ah, the Navy can’t attack/dismantle an adversary kill web alone. I think there’s a parallel discussion about implementing and using blue force kill webs.

Louis: I like the term “ecosystem”, which brings to mind perhaps “full-spectrum ecological attack” or something. Military eco-collapse.

Matt: I think that the idea of webs will require different C2 and ways to think about sensor/weapon/target pairings. We can’t allocate a set of assets for a single or small set of targets unless we want a very narrow web.

I think that chains can be a way to test and think about paths through the web but they’ll always need to have follow-up analysis as a web. In aerospace engineering (AE) we used to have a joke that you know you’re an AE if you’ve represented a wing as a plane, the plane as a line, and the line as a point (to simplify the calculation). The same holds, you can test paths through the web exquisitely using analysis and might have to run exercises as exquisitely planned chains or small webs, but we should war game and do larger [Modelling and Simulation] as webs. I worry about webs though, if we need the always-on comms to make them work. That means they’re fragile and our already targeted comms infrastructure will be an easy point of failure.

Mike: In my mind, having multiple comms channels is part of what would make a web a ‘web’.

Matt: I agree, but those might be very low bandwidth or short duration or one way rather than bi-directional. We don’t just turn on “Uber comm path 1” and know that will link everyone with the same protocols.

Mike: It worked in World War Two, some of the naval battles in the Slot [during the Guadalcanal campaign] came down to real-time unencrypted bridge-to-bridge radio to share info across the Fleet rapidly.

Matt: Absolutely think of them as complex adaptive systems, but if you do then you have to accept that any given path through the web might not give you the same performance twice, or that the assets you thought you’d have for a given mission might be taken over by another use of the web. This is where the joint piece comes in, the bigger the web the more possible elements in any given role/roles.

Practicing as a web will be hard though. We need MMO (massively multiplayer online) wargaming that is always on. I should be able to log in with the rest of my DDG or on my own, and should be able to find other players online. We would also need some way to figure out classification so we could play red as accurately as possible without giving up methods and sources. We should have stats that we can download and leader boards and tutorial sessions that are always available. Have practice where people can try out ideas free of criticism and then have more serious competitions where people are graded and that data-rich environment is plumbed to find out how to do better.

This is going to be expensive, in terms of dollars and resources, but would be well worth it. We could/should have layers of detail, so someone can play a simpler game against

Others in one on one or play in very detailed complex many on many battles.

Nick: Agreed, and you can’t benefit from AI-enabled reinforcement learning (think AlphaGo)5 to make tactical and operational recommendations without building a robust modeling and simulation environment first.

So, this virtual environment has to essentially mirror the real-world COP (Common Operational Picture), or at least have exposed APIs (Application Programming Interfaces) that are formatted similarly, so you can train a model in a simulated environment and immediately deploy that same model into an operational context.

Matt: Use peoples’ play to train the AI as you go?

Nick: You could do it either way. You have a rewards/punishments system, where let’s say people play China, and fight to China’s tactics and capabilities. A model can then iterate through millions of blue force responses to find the one with the highest probability of success and make those as recommendations. Another method is you can program China’s tactics in as broad rules, but have it be an AI instead of people, so the two models learn from each other. The reason for machine learning is that there are so many exponentially complex scenarios in the responses, you can’t necessarily try all of them every time, so you train a model that can estimate the best response without needing to try every scenario. That was the significance of the AlphaGo achievement in beating the world champion at Go.6

Once a model is trained though, you can deploy it to look at real-world situations and make recommendations for blue force actions based on its training in the simulated environment. 

Google's AlphaGo beats Go master
Figure 3 – Google’s AlphaGo beats Go master Lee Se-dol in 2016.

Alex: I think this is the end state that LVC (Live, Virtual, Constructive) training should aim for. Imagine if off each coast there was a persistent virtual battlespace that you could “log” into, either with a ship, aircraft, or submarine (not sure how LVC is thought to work for subs, if at all).

Nick: Also, having no experience or interaction with JADC2 (Joint All-Domain Command and Control), would someone mind explaining how that initiative relates to the kill web concept?[7] I was under the impression that the JADC2 initiative was trying to help solve that problem.

Shane: JADC2 should, in theory, give you the technical ability to link together currently non-compatible networks, sensors, and weapons systems. But from what I can gather JADC2 is pretty much only about that technical ability. There’s no attempt to get people to conceptualize attacking webs as opposed to just attacking a series of chains. That’s not really a technical thing, it’s more a doctrine, training, and TTPs (Tactics, Techniques, and Procedures) thing.

That half seems to be sorely lacking.

If you would like to join the conversation at the Naval Constellation, please email: navalconstellation@gmail.com. 

Endnotes

1. “Military and Security Developments Involving the People’s Republic of China 2020, Annual Report to Congress.” Office of the Secretary of Defense. https://media.defense.gov/2020/Sep/01/2002488689/-1/-1/1/2020-DOD-CHINA-MILITARY-POWER-REPORT-FINAL.PDF 

2. Captain Carmen Degeorge, U.S. Coast Guard, Commander Nathaniel Schick, U.S. Navy, and Lieutenant Colonel Jimmy Wilson And Majors Chad Buckel And Brian Jaquith, U.S. Marine Corps. “Naval Integration Requires A New Mind-Set”. 2021. U.S. Naval Institute. https://www.usni.org/magazines/proceedings/2021/october/naval-integration-requires-new-mind-set-0.

3.” Military and Security Developments Involving the People’s Republic of China 2020, Annual Report to Congress.” Office of the Secretary of Defense. https://media.defense.gov/2020/Sep/01/2002488689/-1/-1/1/2020-DOD-CHINA-MILITARY-POWER-REPORT-FINAL.PDF

4. Shelbourne, Mallory. 2020. “Navy’s ‘Project Overmatch’ Structure Aims To Accelerate Creating Naval Battle Network – USNI News”. USNI News. https://news.usni.org/2020/10/29/navys-project-overmatch-structure-aims-to-accelerate-creating-naval-battle-network.

5. “Google AI Defeats Human Go Champion”. 2017. BBC News. https://www.bbc.com/news/technology-40042581. 

6. Ibid.

7. “Joint All-Domain Command and Control (JADC2)”. Congressional Research Service. July 1, 2021. https://crsreports.congress.gov/product/pdf/IF/IF11493 

Featured Image: PHILIPPINE SEA (Jan. 22, 2022) An F-35C Lightning II, assigned to the “Black Knights” of Marine Fighter Attack Squadron (VMFA) 314, and an F/A-18E Super Hornet, assigned to the “Tophatters” of Strike Fighter Squadron (VFA) 14, fly over the Philippine Sea. (U.S. Navy photo by Mass Communication Specialist 2nd Class Haydn N. Smith)

Future Tech

Solving Communications Gaps in the Arctic with Balloons

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Emerging Technologies Topic Week

By Walker D. Mills

Defined by their remoteness and extreme climate, the polar regions present an array of tactical and operational challenges to US forces as sea icing, repeated thawing and freezing cycles, permafrost, and frequent storms can complicate otherwise simple operations. However, often overlooked are the challenges to communications, which are critical to Navy and Coast Guard vessels operating in the polar regions. Perhaps once possible to ignore, these challenges are becoming more pressing as the Marines, Navy and Coast Guard increase their operations at higher latitudes and place more emphasis on the arctic and more arguments are made for sending Marines and soldiers to the arctic for training and presence. In order for US naval forces to compete in the polar regions and fight if needed, the military needs to invest in persistent and reliable communications capabilities. One solution is high-altitude balloons.

Arctic experts have long understood the difficulty of communicating in the arctic, noting that “While communicating today might be easier than it was for Commodore Perry 111 years ago, it’s not that much better.” Arctic communications are especially difficult for a number of reasons. Satellite-based options are limited or nonexistent because the vast majority of satellites maintain equatorial orbits, which means the polar region’s extreme latitudes fall outside satellite range. Though a few satellites follow non-equatorial orbits, there are simply not enough to provide continuous connectivity at the bandwidth needed for modern operations.    

There are also natural barriers to communications in the arctic. The ionosphere covering the polar regions has a high-level of electron precipitation, which is the same characteristic that produces the Northern Lights. However, this interferes with and degrades the high-frequency (HF) radios that the military normally uses for long-range communications in the absence of satellites. Additionally, the extreme climate and cold weather in the arctic presents another challenge to communications infrastructure such as antennas and ground stations. Arctic conditions make it harder to access and maintain ground arrays, batteries expire faster in colder temperatures, and equipment can easily be buried by falling snow and lost.

Finally, the near complete lack of civilian infrastructure complicates arctic communications. The polar regions comprise about eight percent of the earth’s surface, accounting for over 10 million square miles of land on which only about 4 million people live. Most are clustered in small communities, resulting in sparse commercial communications infrastructure across the region. However, persistent and reliable communications are absolutely essential for the successful employment of maritime forces in the arctic.

One solution is for naval forces to use high-altitude balloons that provide temporary communications capabilities. Balloons are far cheaper than satellites and much more responsive. They can be quickly deployed where coverage is needed and fitted with communications payloads specific to the mission. They are also low-cost and effective enough that they can be used not only in operations but also in training at austere locations.

Balloons offer a degree of flexibility critical for operations in remote environments like the arctic. Differently sized balloons can be fitted with specific capacities for mission-tailored      requirements and priorities. The size of payload, loiter time, and capabilities are primarily a function of balloon size. Large balloons and stratospheric airships can stay aloft for months, while smaller “zero pressure” balloons might last hours or a few days. Given their diverse uses and capabilities, high-altitude balloons have already been used to provide communications in hard-to-access environments by organizations such as NASA, the US Air Force, and Google. For example, researchers at the Southwest Research Institute and NASA have supported atmospheric balloon flights over the poles that lasted up to a month – more than enough time to meet operational needs.

Though there are various ways to launch and lift high-altitude balloons, recent advances show that hydrogen gas is the best candidate. Researchers at the Massachusetts Institute of Technology’s Lincoln Laboratory recently discovered a new way to generate hydrogen with aluminum and water. With this new ‘MIT process,’ researchers have already demonstrated the ability to fill atmospheric balloons with hydrogen in just minutes – a fraction of the time it takes using other methods. The MIT process promises to be not just faster, but also cheaper and safer than other methods of hydrogen generation. It also means that units can generate hydrogen at the point of use – obviating the need to store or transport the volatile gas or other compressed gasses. The researchers have demonstrated effective hydrogen generation with scrap and recycled aluminum and with non-purified water including coffee, urine, and seawater.

The deployment of balloons utilizing this new hydrogen generation process would be extremely simple. A balloon system could conceivably be developed where the system is simply dropped into the ocean from a ship, airplane, or helicopter with a mechanism that causes it to self-deploy when it comes into contact with seawater. This single system – one that does not require stores of compressed gas or an electrolyzer to generate hydrogen – would also take up far less space than other balloons and the associated equipment required to get them aloft. Balloons full of hydrogen gas could also act as giant batteries as the hydrogen can also be used to power communications equipment or sensors.

So far, the US Coast Guard has been leading the way with arctic communications. The service has highlighted improving communications in the arctic as part of their first line of effort in the 2019 Arctic Strategic Outlook and as a key initiative in their 2015 Arctic Strategy Implementation Plan. Along with the Marine Corps, the service has also been experimenting with Lockheed’s Mobile User Objective System (MUOS), a next-generation satellite communication constellation intended to replace the constellation that the Pentagon relies on today. But even the systems’ creators are clear that in extreme polar regions, MOUS may only offer eight hours of coverage per day. Constellations of small and cheap cube satellites might also be a partial fix for the communications dead zones, but hundreds or thousands would be required to cover a region as large as the arctic. The Army and the Air Force are also interested and intend to invest $50 million each toward arctic communications. The Army has previously experimented with using high-altitude balloons to support multi-domain operations and might be a key partner in developing an arctic communications capability, and the Air Force is looking at using commercial broadband satellites to meet service and joint communications needs in the arctic.

Communications issues are a consequence of the polar operating environment and an obstacle for the military services operating there. But just because the environment is difficult does not mean that US forces have to go without persistent and reliable communications. High-altitude balloons could plug the communications gap not just for maritime forces but also for the Army and special operations units operating in these extreme latitudes. Developing and deploying high-altitude communications balloons, lifted by hydrogen gas generated by the MIT process, offers near-term capability for US forces operating in polar regions with underdeveloped communications infrastructure.

Walker D. Mills is a U.S. Marine Corps officer serving as an exchange officer in Cartagena, Colombia, the 2021 Military Fellow with Young Professionals in Foreign Policy, a non-resident WSD-Handa Fellow at Pacific Forum, and a Non-Resident Fellow with the Brute Krulak Center for Innovation and Future War. 

 The views expressed are his alone and do not represent the United States government, the Colombian government, the United States military, or the United States Marine Corps.

Feature Image: A NASA long duration balloon is prepared for launch on Antarctica’s Ross Ice Shelf near McMurdo Station in 2004. (NASA photo)

Future Tech

Back to the Future: Routine Experimentation with Prototypes

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By John Hanley

Broad agreement exists that the Department of Defense’s, and thus the Navy’s, acquisition system is bound like Gulliver by Lilliputian processes, resulting in an inability to adapt. This inflexibility threatens to increase the risks to operating forces as they face a growing number of adaptive adversaries, ranging from China and Russia, North Korea and Iran, to the Islamic State, Al Qaeda, and others.1 Well-intended legislation and increasing reliance upon computer modeling to inform the selection of future platforms and systems are major contributors to the current situation. Greater reliance on experimenting with prototypes at sea could provide a large improvement.2

Introduction

Congress passed the Goldwater-Nichols legislation in 1986 to promote joint operations and provide more civilian control by creating an Undersecretary of Defense for Acquisition and reducing the role of the Chief of Naval Operations (CNO) and other Service Chiefs in acquisition decisions. This legislation added joint duty requirements to the already-packed career paths for line officers, even as it added new educational and experience requirements for acquisition professionals.3 The Defense Acquisition Workforce Improvement Act in 1990 further created mandatory requirements for a more professional acquisition force. Line and acquisition professionals “had completely different chains of command and, consequently, were situated in different performance evaluation and promotion structures.”4 Having little appreciation for an increasingly complex acquisition process, line officers had trouble articulating their needs to an acquisition workforce that was itself increasingly isolated from the operational environment.

Though the Packard Commission that informed Goldwater-Nichols legislation called for more prototyping to gain experience with new platforms and systems before making major investments, the Department of Defense (DoD) and the Navy increasingly turned to computer-based combat and campaign simulations as a cheaper and more flexible way to inform acquisition decisions.5 This had the effects of further separating the experience of fleet operators from Navy acquisition, and removed an important source of data for ensuring computer-based simulations were accurate.6

In their book Switch: How to Change Things When Change Is Hard, Chip and Dan Heath highlight the value of bright spots; examples of projects that work well to make a case for needed change.7 This article suggests some bright spots, and continuing challenges, in acquiring capabilities the Navy needs to adapt to rapidly emerging security opportunities and challenges.

A Virtuous Prototype Cycle

As a junior officer, I was privileged to be assigned to the USS Guitarro (SSN 665) in San Diego in 1973. The Guitarro played a major role in developing tactics for prototype combat systems deployed to the Pacific submarine fleet, in particular the new Submarine Towed Array Sensor System (STASS) along with its BQR-20 series digital sonar displays. In the mid-1970s, Guitarro also installed the first digital submarine combat system (BQQ-5 sonar and Mk-117 fire control system) and participated in the development of submarine-launched Harpoon and Tomahawk cruise missiles.8

Following my service on the Guitarro, I became an operations analyst supporting several programs. The Naval Electronics Systems Command (PME-108) was sponsoring the Coordination in Direct Support (CIDS) program developing technology and techniques for communicating with submarines to operate in direct support of carrier battle groups, and the Over-the-Horizon Targeting (OTH-T) program was developing technology and techniques for targeting ships with Harpoon and Tomahawk missiles at ranges beyond the line of sight. These programs integrated their efforts with the Tactical Development and Evaluation Program sponsored by the OP-953 on the Navy staff. My next job involved working with the Chief of Naval Operations Strategic Studies Group where I witnessed the speed with which a small team of intelligence specialists, engineers using the latest technology, and Navy leadership could deliver cutting edge capabilities to the fleet very rapidly.

My experience in these programs taught the value of providing prototypes to the fleet early. Working with prototypes allowed us to develop tactics and techniques that the system developers never considered, and highlighted operational limitations and misperceptions of those developing the systems. Fleet analysis data contributed directly into operations analysis, computer simulations, and war games. The experience also demonstrated the limitations of tightly-coupled integrated systems as opposed to systems with modules that could adapt and change easily. As my career continued, I observed revisions to the DoD acquisition system that diminished the role of prototyping and extended times to demonstrate new capabilities to the fleet, usually exceeding cost estimates and requiring modifications as operators discovered what they could, and could not do.

Sonar Towed Arrays and Digital Displays

STASS was a long, linear array of hydrophones deployed behind the submarine on a cable. This kept the array’s sensors away from the towing submarine’s radiated noise, significantly improving the signal-to-noise ratio needed to detect faint signals. It could detect contacts behind the submarine that were screened from the hull-mounted sensors in the bow. Its length provided a larger aperture to detect lower frequencies at longer ranges. This sonar system made submarines more effective.

However, the new system had its challenges. Initially, a sonar operator could monitor only one of the array’s 16 beams at a time, by listening and/or monitoring the BQR-20’s digital display.9 The display would provide a waterfall of illumination if a signal was detected on that beam. Low frequencies required several minutes of integration time to process signals from the ambient noise. Thus it could take more than an hour to search though all of the beams. The submarine also had to travel at slow speed to prevent the noise from water flowing over the hydrophones from masking signals from other vessels. Even with the slower speeds, the longer detection ranges provided the new sonar system significantly increased the search rate in deep ocean areas.

The principal tactic for estimating a targets range using passive sonar was developed by Lieutenant John Ekelund in 1956.10 Ekelund’s approach significantly improved upon target motion analysis techniques that involved only plotting bearings to a target over time. His method involved calculating the rate of change of the relative bearing of the contact as the host submarine maneuvered on two courses. The time to do the calculation affected the accuracy of the estimate. Slow maneuvering with the STASS was frustrating.

Our sister ship, USS Drum (SSN 677), was the first ship in the Pacific fleet to receive the new STASS. To reduce the time maneuver to a new course, Drum tried a tactic of speeding up through the turn, then slowing to reduce the flow noise. Unfortunately, the sub slowed faster than the array, resulting in the array’s cable wrapping around the horizontal stabilizer on the sub.

Guitarro then had its opportunity to develop tactics for employing the STASS. Our efforts focused on three areas: maneuvering the ship, sonar search procedures, and plotting contacts. I had the lead on plotting. Current practice used a “compressed” time-bearing plot along with “strip” plots. The time bearing plot provided bearing rates needed to compute Ekelund ranges. Speed strips marked with various speeds were manually aligned across bearings to a contact’s for estimating its range, course, and speed. Given the time required to generate contact bearings with the STASS, we developed an “expanded” time-bearing plot.

A big innovation occurred when Dr. Ted Molligen (a ship rider from Analysis and Technology, Inc.) noted that the array’s beams were cones and the sea bottom was a plane. The intersection of a cone and a plane is a hyperbola. Therefore, when the contact’s signal bounced off the bottom, which occurred frequently in the Pacific, we were dealing with lines of bearing along a hyperbola. Within a day, we manufactured templates of hyperbolas out of plexiglass for strip plotting using bottom bounce signals. Without measuring bearing rates, the intersection of two hyperbolas provided a contact’s estimated position quickly after our maneuver.

Another unanticipated effect was the ability to observe the contact’s Doppler signal shift in near-real time. Thus we could observe not only the contact’s bearing change during maneuvers, but also whether it was opening or closing us. Reconstructed plots of our target clearing its baffles (simulating “crazy Ivans”) during exercises showed our depiction of the target’s motion to be very accurate.

The next breakthrough occurred when we received the BQR-22 a couple of months later. The BQR-22 could process two beams simultaneously. We discovered that, with some regularity, we would receive both direct path and bottom bounce signals from the contact. The different signals would arrive on different beams because of their paths through the water. The intersection of a direct path line of bearing with a bottom bounce hyperbola produced an estimate of the target’s range without having to maneuver. Exercise reconstruction showed our estimates to be within a few percent of the target’s range.

Under the leadership of our superb Executive Officer, Lieutenant Commander Dan Bacon, we documented the tactics we had developed for maneuvering the sub, conducting the sonar searches, and plotting in a tactical memo and submitted it to Commander, Submarine Forces Pacific. He replaced our cover with his, and distributed it as a Tactical Memorandum to the fleet.

Within a year, we received the BQR-23 that processed four beams simultaneously. We then deployed with this sonar system, and other prototype sensors and processors, for operations in the western Pacific. Deploying with prototype equipment was routine in the submarine force.

During World War II U.S. submarines could attack only surfaced enemy submarines.11 In 1949, the submarine force created Submarine Development Group 2 and tasked it with antisubmarine warfare (ASW) as part of an effort to preserve the submarine force structure during demobilization. Within twenty years, the U.S. submarine force went from having essentially no ASW capability to becoming the dominant ASW force in the world. Following their motto of “Science, Technology, Tactics”, the Group employed a program of designing, conducting, and reconstructing exercises to develop tactics for prototype systems, and reconstructing submarine performance during operations using extensive data collected during patrols.12 Using the Group’s methodology, we were able to exploit the STASS and the BQR-20 series digital displays and document proven tactics for the fleet that significantly improved the U.S. advantage over Soviet submarine forces within an 18 month period.

In contrast, installing the first submarine digital combat system in the shipyard demonstrated challenges that occur when developing systems without prototyping. The system had no feature for entering bearings directly from the periscope. Apparently, the engineers thought that all approach and attack would use sonar only. We also were told that adding hyperbolic ranging to the software in the central computer complex, which serviced the sonar and fire control system, would take at least a decade. Stand-alone computers came to support search planning and target motion analysis since the integrated system was incapable of rapid change.

Coordination in Direct Support

Admiral Rickover had pushed through the development of the Los Angeles-class submarines by arguing that their higher speed would allow them to screen a carrier battle group.13 The major problems were communicating with submarines to keep them on station as the battle group maneuvered, to direct them to prosecute contacts detected by other battle group platforms, and to prevent other battle group ASW forces from attacking them. Also, based on the way that the U.S. targeted German U-boat radio transmissions during World War II, our silent service routinely disabled its radio transmitters while on patrol to prevent detection. Standard submarine communications involved the submarine getting an antenna to the surface for broadcasts that were repeated for eight hours on a two-hour cycle. The submarine restricted its speed to a few knots when at communications depth, both to prevent anyone seeing the wake of the periscope and to keep its floating wire antenna on the surface. Thus the submarine could best communicate at scheduled intervals, and could not transit at battle group speeds while communicating.

Rear Admiral Guy H.B. Shaffer took the methods he had used commanding Submarine Development Group 2 with him to the Naval Electronic Systems Commands program office PME-108.14 He established the Coordination in Direct Support (CIDS) program to develop means to communicate with submarines providing direct support to carrier battle groups.

The Submarine Analysis Notebook provided the methodology and data required for assessing submarine ASW performance. The first step in the CIDS program was to develop a Fleet Exercise Analysis Guide that provided a conceptual battle group ASW process and performance metrics.15 PME-108 then worked with the Tactical Development and Evaluation (TAC D&E) Program and the numbered fleets to schedule participation in their exercises, and invited the Navy laboratories to provide prototype communications systems for submarine communications. The prototypes included everything in the electromagnetic spectrum from blue-green lasers to Extremely Low Frequency (ELF) radios and a variety of acoustic communication methods.16 For each exercise a team would work with relevant commands to design the exercise and develop data collection plans. The team would then ride key ships in the exercise providing advice on accomplishing exercise training and tactical development objectives, and overseeing the data collection. Following the exercise, the team would reconstruct and analyze the event in full, including documenting the timelines for each ASW interaction and every ASW communication over every communications path.

This approach allowed prototypes to be evaluated not just as stand-alone systems, but demonstrated their value both in enhancing communications as part of a suite of systems operating simultaneously and in accomplishing the mission of protecting the carrier from submarines attacking with torpedoes and cruise missiles.

Occasionally a laboratory would offer a prototype that was operationally unsuitable. One such system was a shaped buoy weighing several thousand pounds to be towed behind a submarine at depth and speed to push an antenna to the surface. Had the buoy hit a surface vessel, or submarine at shallower depth, it would have had the impact of a torpedo without the explosion.

Documenting every step of the communications path demonstrated the delays created by communications controlled by the submarine operating authority ashore. This led the submarine force to provide Submarine Element Coordinators (SEC) at sea with the battle group. The exercises explored many operational schemes with these SECs adjusting submarine broadcast schedules and using ELF or acoustic “bell-ringers” to call the submarine to communications depth for higher data rate communications.

After 10 fleet exercises conducted over a three-year period involving all the numbered fleets, the CIDS program demonstrated that the tactical concept for using submarines as an outer screen moving with the carrier battle group was infeasible. This led to alternative schemes for employing submarines supporting task groups. The communications data proved valuable and was incorporated in the Navy’s Warfare Environment Simulator which allowed teams playing task group platforms on different terminals to receive information with realistic time delays.17 Over time, this became the Navy Simulation System, but lost its original purpose of focusing on command and control issues using fleet data.

Over-the-Horizon Targeting

Shortly after the command and control fleet exercises, the Navy began deploying Harpoon and was getting ready to deploy Tomahawk missiles to the fleet. So RADM Shaffer established an Over-the-Horizon Targeting (OTH-T) program within PME-108. The approach followed the CIDS program; developing a fleet exercise analysis guide, designing exercises to incorporate prototype systems and tactics, collecting data, and conducting analyses. The Mediterranean, with its high shipping density and many islands, provided the most challenging environment for OTH-T.

The exercises were again successful in demonstrating that the technology and tactics were insufficient to support the proposed concepts for anti-ship Tomahawk use. This and the abundance of targets ashore were major factors in emphasizing land attack versus anti-ship versions of the Tomahawk missile.

Advanced Technology Panel

By the late 1970s, Navy efforts to develop special intelligence sources provided deep penetration of Soviet Navy thinking and practices.18 The CNO repurposed the Navy’s Advanced Technology Panel (ATP), created in the 1970s, to become the main customer for this highly restricted intelligence.19 The ATP was a small group of the senior admirals on his staff, his top ‘thinkers’, who were cleared primarily to review special programs, but did a lot more.20 Working closely with the Navy laboratories, the leadership could deliver counters to what the Soviets were deploying within months to a year or two of having firm intelligence on their systems.

CNO Admiral Tom Hayward, on the advice of then Under Secretary of the Navy Robert Murray, formed a Strategic Studies Group of six promising Navy officers selected personally by him and two Marines at the Naval War College in 1981. Murray characterized the SSG as changing captains of ships into captains of war, employing terms that Winston Churchill used when he said that he needed more of those in World War I.

That fall, the ATP led by Vice CNO Admiral Bill Small was looking for ways to game using new, sensitive intelligence. In January 1982, the SSG was asked to develop concepts employing the new intelligence. The SSG held an extensive war game in April 1982. Admiral Small brought the ATP to Newport for two days at the conclusion of the game to review the results. The concepts used in the game became the foundations for the 1980s Maritime Strategy and rapidly changing war plans. The ATP was able to focus special programs on providing capabilities tailored to executing the new war plans.21

Two Different Paths: Nuclear Submarines and Distributed Surface Combat Power

Prototyping should not be restricted only to the payloads on vessels. In 1951, then Captain Hyman G. Rickover received authorization to build nuclear powered submarines. USS Nautilus (SSN 571) was commissioned in 1954 with a pressurized water reactor. The Navy then commissioned:

  • The USS Seawolf (SSN 575) with a liquid metal cooled reactor in 1957. This design presented too many risks and was quickly replaced.
  • The USS Triton (SSRN 586) in 1959, a large radar picket submarine with two reactors.
  • The USS Tullibee (SSN 597) in 1960, a very small, quiet submarine with a small reactor.
  • The USS Jack (SSN 605) in 1967 with direct drive and counter-rotating shafts and propellers.

These submarines, along with the small classes of SSNs built between the prototypes, explored the design space, adapted design features, and informed the building the following classes of nuclear submarines.22 The large capacity of the USS Hallibut (SSGN 587), designed to shoot Regulus nuclear cruise missiles, allowed it to adapt to different missions over its service life.

In 1996, the CNO Strategic Studies Group briefed its concepts for dispersed and distributed surface power to the CNO.23 The Group had in mind fast, stealthy ships of several hundred tons capable of mounting modular payloads for different missions. They anticipated that the Navy would explore the design space with prototypes, as it did with nuclear submarines. Instead, DoD acquisition processes led to the Littoral Combatant Ship. Rather than using a range of small and large prototypes using differing propulsion concepts, the Navy ended up with two much larger ship classes that have had many early difficulties.

Conclusion

The DoD acquisition system has come to believe that we must precisely predict the threat decades into the future, optimize designs by spending many million dollars on computer analysis, and then commit billions of dollars for procurement, without any of the experience and operator feedback provided by prototypes. This developmental approach incurs major cost, schedule, and performance risks because the future remains stubbornly uncertain – just as it always has been.

A better alternative is to prototype operational systems and platforms rapidly, providing agility to adapt to emerging threats and take advantage of emerging technology. Programming, budgeting, and contracting processes present major hurdles. Though routine acquisition procedures do not support such agility, Other Transaction Authority and similar processes authorized by Congress should be employed to their maximum extent. However, to do so effectively will require reinvigorating experimenting with prototypes in fleet exercises in ways similar to Submarine Development Group 2, the CIDS and OTH-T programs, and early nuclear submarine force development.

Captain John T. Hanley, Jr., USNR (Ret.) began his career in nuclear submarines in 1972. He served with the CNO Strategic Studies Group for 17 years as an analyst and Program/Deputy Director. From there in 1998 he went on to serve as Special Assistant to Commander-in-Chief U.S. Forces Pacific, at the Institute for Defense Analyses, and in several senior positions in the Office of the Secretary of Defense working on force transformation, acquisition concepts, and strategy. He received A.B. and M.S. degrees in Engineering Science from Dartmouth College and his Ph.D. in Operations Research and Management Sciences from Yale. He wishes that his Surface Warfare Officer son was benefiting from concepts proposed for naval warfare innovation decades ago. The opinions expressed here are the author’s own, and do not reflect the positions of the Department of Defense, the US Navy, or his institution.

Endnotes

  1. For example see Barber, Arthur H. “For War Winning Innovation, Fix the Process.” Naval Institute Proceedings, October 2016 and National Academy of Sciences-Engineering-Medicine. “The Role of Experimentation Campaigns in the Air Force Innovation Lifecycle.” Washington DC: National Academies Press, 2016.
  2. This type of experimentation involves trying out concepts and technology at sea, and learning from the results. Attempts by the former Joint Forces Command to restrict the concept of experimentation to hypotheses without control cases were inappropriate, misused, and misguided.
  3. U.S. Code Title 10 Chapter 87.
  4. Charles Nemfakos, Irv Blickstein, et. al. The Perfect Storm: The Goldwater-Nichols Act and Its Effect on Navy Acquisition. Santa Monica: RAND, 2010.
  5. David Packard, President’s Blue Ribbon Commission Defense Management, A Quest for Excellence: Final Report to the President, Washington, D.C., June 30, 1986.
  6. John T. Hanley, Jr. “Changing the DoD’s Analysis Paradigm: The Science of Wargaming and Combat/Campaign Simulation.” Naval War College Review, Winter 2017.
  7. Chip Heath, Dan Heath. Switch: How to Change Things when Change is Hard. (New York: Broadway Books, 2010).
  8. The first installation of the BQQ-5 and Mk-117 was not called a prototype at the time. However, the submarine museum adjacent to Sub Base New London now characterizes it as a prototype.
  9. A story on the waterfront was that the BQR -20 resulted from a sonar Chief in San Diego who observed a mechanic using a digital processor when diagnosing his car engine. He obtained a device and connected it into his sub’s system, demonstrating an ability to see distinct frequencies.
  10. Ekelund’s story is a classic example of junior officer innovation. See http://www.public.navy.mil/subfor/underseawarfaremagazine/issues/archives/issue_15/ekelund.html .
  11. Captain Gene Porter, USN (Retired) informed me of an action on Action of 9 February 1945 where the Royal Navy submarine HMS Venturer sank the U-boat U-864 in the North Sea off the Norwegian coast. This action is the first and so far only incident of its kind in history where one submarine has intentionally sunk another submarine in combat while both were fully submerged.
  12. For a comprehensive account see “Submarine Warfare and Tactical Development: A Look – Past, Present, and Future: Proceedings of the Submarine Development Group TWO & Submarine Development Squadron TWELVE 50th Anniversary Symposium 1949-1999,” U.S. Naval Submarine Base Groton, Connecticut: Submarine Development Squadron TWELVE, 1999.
  13. The Los Angeles or 688 class had twice the shaft horsepower of the proceeding 637 class, and cost about twice as much. It originally sacrificed under ice and electronic surveillance capabilities to keep the costs down. The submarine force was under the gun from Secretary of Defense MacNamara’s Systems Analysis Office to demonstrate that the benefits of about 20% more speed were worth the cost. In fact, since both classes had the same sonars and weapons, the tactical speeds for detecting targets attack ranges were the same, and the 637 could conduct under ice and electronic surveillance missions. Captain Gene Porter, USN (Retired) provided oversight from OSD’s Systems Analysis Office. Studies demonstrated that the extra 688 speed was most useful in evading enemy torpedoes, but not worth twice the cost of the submarine.
  14. Submarine Development Group 2 became Submarine Development Squadron 12 in the mid-1970s. The Naval Electronics Systems Command is now the Space and Naval Warfare Systems Command (SPAWAR).
  15. The author contributed to writing the CIDS Fleet Exercise Analysis Guide and wrote the OTH-T Fleet Exercise Analysis Guide.
  16. ELF frequencies are 3-30 Hertz, corresponding to wave lengths 10,000 to 100,000 kilometers. The data rate is a few characters per minute. ELF energy penetrates seawater to a greater depth than higher frequencies, allowing the submarine to remain at depth and receive communications. The prototype ELF transmitter was on the order of 100 miles long, located in upper Michigan and required the submarine to tow a long antenna. The program used a bull under the transmitter to monitor any biological effects.
  17. The author also used this data in 1982 to model and analyze the first Chief of Naval Operations Strategic Studies Group Combined Arms ASW concept for rapidly gaining forward sea control and attacking Soviet submarines in their bastions. This work resulted in quickly changing U.S. naval war plans. Over their careers, Admiral William A. Owens expanded the original SSG concept into his Systems-of-Systems ideas and Vice Admiral Arthur Cebrowski into his Net Centric Warfare concepts. John T. Hanley, Jr. “Creating the 1980s Maritime Strategy and Implications for Today.” Naval War College Review, 2014: 11-30 provides more details.
  18. Christopher Ford and David Rosenberg, The Admirals’ Advantage: U.S. Navy Operational Intelligence in World War II and the Cold War (Annapolis; MD: Naval Institute Press, 2005), p. 84.
  19. John B. Hattendorf, The Evolution of the U.S. Navy’s Maritime Strategy, 1977–1986, Newport Paper 19 (Newport, R.I.: Naval War College Press, 2004), pp. 32-33.
  20. Admiral William N. Small, U.S. Navy (Retired), “Oral History.” Interviewed by David F. Winkler, Naval Historical Foundation, 1997, p. 56.
  21. Ibid. Hanley 2014 and Petrucelli, Joe. 2021. “John Hanley on Convening the Strategic Studies Group and Assessing War Plans.” CIMSEC. March 23. Accessed April 26, 2021. https://cimsec.org/john-hanley-on-convening-the-strategic-studies-group-and-assessing-war-plans/.
  22. The principal argument against such prototypes is the cost of maintaining one-off designs. Space in this article does not permit an exploration of how technologies such as 3D printing could change this calculus.
  23. The author was Deputy Director of the CNO Strategic Studies Group at this time.

Featured Image: Navy Petty Officer 2nd Class Shawn Halliwell monitors a waterfall display on his sonar system during a battle drill aboard the strategic missile submarine USS Maryland, Feb. 16, 2009. (DoD Photo).