Modelling Military as an Open, Socio-technical System

 1. Introduction

The first step in military socio-technical system evolution (Trist, 1981) was the creation of the Wehrmacht Panzer Division in cooperation with the Luftwaffe's air-to-ground support. (Guderian, 2001) The available industrial machinery, tank crew training based on professional warrant officers, and gradually challenging training matured a coherent team of men and machines who were aware of their strengths and weaknesses and trusted the support from other actors (e.g., commanders, artillery, engineers, signals, air support, logistics) of their combat power system. 

After the blitzkrieg, the military has been trying to generate combined arms, Joint operations, and Combined operations capabilities with varyingly sourced force elements. (Vego, 2009) Figure 1 presents a hypothesis for the evolution of the military socio-technical system. Before digitisation, military organisations used weapons as tools, managed information manually on paper, and emphasised commander-centric decision-making. As weapons turned into platforms, the human operator merged with the machine. Information was digitised, and computers became interfaces to communications and knowledge. In the future, large platforms will turn into swarming, autonomous systems; data will be processed by learning computer algorithms; and commanders will be supported by their machine Companions in decision-making. Will this be the military socio-technical system in the future?

Figure 1: An evolutionary view of the military socio-technical system

The military has a history of adopting technologies and methods from other domains of society. The values, education, and culture the society provides to its citizens also reflect on military socio-technical features:

• Russia aims to mass fire from automated weapon platforms (Mittal, 2022) without concern for collateral damage in operations (Lavrov, 2018).  
• China installs autopilots on old fighter aircraft to create swarms of targets and educates its commanders in computer-simulated battlefields . 
• The USA has a long history of remote-controlled Unmanned Aerial Systems (UAS). It currently operates unmanned systems    in all five domains. 

Technology is transforming governments (eGovernment) , industrial manufacturing and logistics (4th Industrial Revolution)  (Schwab, 2017), retail (Amazon ) (Hagberg, et al., 2016), transportation (Uber and Green Deal) , social life (cogni-tech) (ESPAS, 2018), working (gig economy)  and creative production (ChatGPT) . There are opinions (Oliviero, 2021) (Biddle, 2006) that technology has been the servant of military ideas: ideas create concepts, concepts create intellectual structures, and intellectual structures drive technological change in military organisation. So, despite the revolutions in surrounding societies, the military has to invent emerging technology rather than adapt to changes in their environment. The complex, adaptive system theory opens other views to military evolution. (Jackson, 2019) A combination of determined and complex ideas produces three paths for socio-technical system development (Mattila, 2020) 

1. Preadaptation is driven by the need to develop a new System of Systems. It includes research, experimenting, or learning new knowledge with other means. Several optional solutions may be produced and explored to find the best fit. Gained knowledge and prototypes are used to design a new system to fulfil the requirements of the new function.
2. Adaptation happens when the System of Systems is co-opted gradually for different uses without a broader comprehension of the evolution.
3. Exaptation occurs when a component from another system is co-opted as part of a new System of Systems, making it more efficient or fitting for the purpose.

The article aims to create a tactical engagement model to answer the question -What may be the military future with artificial cognition as part of their socio-technical system? The building of the model starts by reviewing man-machine interface evolution, then extends to the man-machine cooperation as part of a more extensive system, uses network theory to define other dimensions of the military socio-technical system, introduces an army confrontation model at a tactical level, and, finally, composes a simplified model for the tactical engagement as an open, complex, adaptive socio-technical system.  

2. Evolution of Man-machine Interface 

The model creation starts from the interface of how a man and his machine communicate. The paper combines three evolutionary interface paths: industrial terminals, cockpits in aircraft and computers in an office. The evolution of the industrial operator terminal interface (Papcun et al., 2018) indicates the tendency for symbolic and manual communication when man-machine cooperation is not continuous. Still, the operator roams between different terminals on the factory floor. (Buxbaum-Conradi, et al., 2016)

A different situation is in the cockpit of an aircraft. The pilot faces the aircraft interface throughout the flight and monitors it even when the craft is on autopilot. In the cockpit, the tendency has been to present information fitting to the constraints of human sensors in stressful situations while keeping the pilot's visual view as clear as possible to surrounding airspace.   Naturally, radars and infrared sensors have widened the spectrum of optical sight and introduced synthetic vision.

Again, a different story is with the interface of office computing. The interface of screen, keyboard, and mouse since the late 1980s has seen minimal changes for an ordinary knowledge worker.    For engineers and graphic designers, the workstation interface appears more applied.  Naturally, the ongoing expansion of mobile devices has replaced keyboard and mouse with audio, video, and touch as the content has also evolved from text to multimedia.  On the other hand, digital content creation accelerated the evolution of the personal computer as a portal to a wide variety of business, governmental, social, financial, and other everyday related content and transactions.   Figure 2 provides a combined view of interface evolution in different environments.

Figure 2: Samples of man-machine interface evolution from three environments: factory, aerospace, and knowledge work (CC-BY-ND Juha Kai Mattila)

Conclusions from the interface evolution model: 

• The tendency seems to lead to a more human-friendly interface between humans and machines. Interaction through touch, audio, synthetic video, and hand movements in virtual or augmented reality may be the next step the military may adopt from elsewhere in society.
• More cognitive, non-intrusive or intrusive links between human thinking and artificial intelligence may emerge.
• A stressful environment narrows the human ability to receive and comprehend information. Hence, artificial cognition-enhanced situational awareness, process automation, and autonomous action may be appreciated on the battlefield.

3. A Model for a Man-machine Cooperation as Part of a Larger System

The socio-technical system theory (Trist, 1981) provides a broader framework to study man-machine cooperation. The socio-technical system comprises two subsystems (social and technical) in interaction with the environment, as illustrated in Figure 3. The social system includes individuals or teams that constitute an organisation. The organisational members deliver the relationships, values, structure, work-related elements, and associations. (Pasmore, 1995) The technical system includes the physical, material, and information flows required in the value creation process and tasks, controls, and maintenance functions. In the organisational setting, the technical system also includes the tools, techniques, skills, and devices that people require to fulfil enterprise purposes and tasks. (Trist, 1981) The environment is the context, surroundings, and conditions in which the open socio-technical system resides, operates, and interacts. (Abbas & Michael, 2023)

The model presents a human-oriented social system following different principles than the machine-oriented technical system but promoting a joint optimisation or equilibrium, which is a degree of fit or balance between the subsystems. Cybernetics (Wiener, 1954) introduce organisational entropy and information-based evolution of complex systems. These two theories explain transformation challenges when new technology and existing human competency do not fit each other, organisational change does not align with existing informal social connections, or the current system is profoundly stabilised, resisting all transformation initiatives.

Figure 3: A model of a socio-technical system

Conclusions from the socio-technical model:

• The process is a critical subsystem which interrelates with the organisation, people and technical systems. 
• The social system will be exposed to cognitive artificial entities and social relations created between these entities and men. The fast surge of ChatGPT and video game popularity indicates that men may be keen to communicate with reactive chatbots. Will this interest overcome the suspicions in alien encounters, and how much training will it take for men to trust artificial entities for their survivability on the battlefield?
• The joint optimisation between the subsystems seems essential for the digital transformations of the military enterprise. Technology-driven change fails if soldiers' competencies and social behaviour are not transferred simultaneously. Conversely, a lack of technology drives people to use other available means of communication, establish unofficial networks, and accomplish tasks.
• The organisation has been structuring the social networks between people. With artificial intelligence, the organisational structure may experience significant changes when new cognition finds ways of working beyond human imagination.

4. Network Models for Large Systems

The theory of Actor-Network, ANT (Latour, 2005) introduces a broader model to study man-machine systems. The ANT does not differentiate humans from machines or processes in a network. The ANT defines three network layers: real world, symbolic and imaginary, as illustrated in Figure 4. The real-world network presents human–machine–machine–process links between actors whose activity is required to deliver the intended outcome. The symbol network reflects the real-world network topology. Still, it presents the different symbols in which actors understand the situation and task. For example, as the organisation is a social network, this level reflects both the official and unofficial networks using different symbols between people and their attitude to the task ahead. (Kadushin, 2012) The third layer illustrates the imaginary view of the event, where humans and artificial intelligence-enabled machines may perceive the situation differently in the future.

Figure 4: A simple representation of close air support delivery on ANT layers (CC-BY-ND Juha Kai Mattila)

Conclusions from the model:

• The ANT layers illustrate the interdependencies and interoperability challenges between actors to achieve a task. No actor can accomplish the job alone but depends on others' coordinated contribution. Every link between actors has three different information transfer levels. 
• The symbol network illustrates that understanding differs from one system to another. Humans understand events differently than machines, so symbols and meanings alter while passing the man-machine interface. Moreover, devices use different logic and may understand the same event contrarily from humans. With artificial intelligence, socio-technical system integration becomes more critical when militarily trained humans are not translating information from one machine to another.
• While daydreaming is human and their misperceptions create errors, artificial intelligence also hallucinates and may promote failed resolutions. How these two species with different imaginary features can cooperate in stressful situations?

5. Modelling Military Engagement at the Tactical Level

Traditionally, the engagement at the tactical level has been a contest between two commanders  (Oliviero, 2021).  Both commanders use combat power, defined as the ability to manoeuvre, mass effects, fire, and maintain tempo orchestrated through all five domains (space, air, cyber/electromagnetic, land and maritime) on the battlefield. (Friedman, 2017) Besides the physical destruction, both sides use their combat power to deceive, surprise, confuse, or shock the other side's cognitive level and aim to disintegrate social and moral cohesion. The operational level may use information and psychological operations to weaken adversaries at social and mental levels in support of tactical combat. (Vego, 2009) Figure 5 illustrates the engagement model.

Figure 5: Modelling tactical actors, tenets, and their relationships in confrontation (CC-BY-ND Juha Kai Mattila)

Conclusions from the model:

• Machine intelligence will increase cognitive vulnerabilities in deception, confusion, and shock when they face an event beyond their training data. Is there a human on the loop to detect this and configure a machine to adapt to unseen situations?
• The Man-machine interface has often been the breaking point of the cohesion of military units. A crew abandon their vehicle when they are afraid of their survivability. How do humans trust algorithms in tight situations?
• Engagement in five domains and projecting effect to adversary systems through tactical tenets become complex for humans to control. A commander requires an artificial enabler (Companion) to understand the situation, choose cost-effective targets, and optimise effectors used on the battlefield.

6. A Composed Model for the Research

From the above frameworks, the paper chooses to compose a simplified model in Figure 6 to study the opportunities and challenges of introducing artificial intelligence in military systems at a tactical level. The composition of the Blue and Red units is assumed to be the same. Still, the differences in adopting artificial intelligence may produce different outcomes on the battlefield. The model reduces all information and communications technology to computational power. Artificial intelligence is in algorithms and includes both preprogramming and self-learning. Data indicates the symbolic layer in the technical system and cognition in a social system. The process is an essential socio-technical system including interfaces, tools, skills, procedures, and doctrines that make the military system cooperate. Social structures include both man-to-man, machine-to-machine and man-to-machine relationships. Culture is in the background, impacting everything from algorithms to social structures. A simplified tactical engagement between the systems will provide a testing ground for the outcome of the confrontation.

Figure 6: The core actors, interfaces, and dependencies of a jointly optimised socio-technical and networked system in a tactical confrontation (CC-BY-ND Juha Kai Mattila)

Further research will use the above model to study what possible effects improved algorithms may introduce in military enterprises, especially in tactical engagement.

Information and Data Management in a Military Enterprise

 1. Transformation Journey Towards Data-driven Military

The Military has been following their societies in digitisation, digitalisation, digital transformation and, recently, the "datization"  of reality. As the world's biggest companies (MAMAA)  are creating revenues from data, the military also creates value from increasing amounts of data. The old wisdom of "If you know the enemy and know yourself, you need not fear the result of a hundred battles" (Sun Tzu) is still valid. The journey has taken longer for military enterprises because of their size and cultures, but, for example, open-source data has revolutionised the transparency of the battlefield in Ukraine.  Therefore, data has become capital  even for the Military.

Figure 1 illustrates a view of a military roadmap  towards data-driven affairs and operations. Here are some snapshots along the journey:

• - 1990:  The Commander was briefed in the afternoon, and situational review was provided in the morning. Briefings used paper maps hanging from walls with graphs and symbols on transparent plastic sheets. All documentation was managed on paper, although it was written with computers but printed for distribution and presentation.
• 1990 – 2000: The Commander was briefed twice daily on the situation. Branches provided their part of the awareness, each with their specific information system. Some were graphical presentations, but most information was summarised in presentations, consuming staff time. There were hardly any inter-branch estimations, and the Commander composed the bigger picture himself. Plans and orders were stored as files and transferred over email.
• 2000 – 2025 Commander has a real-time battlefield model through his battle management system. Intelligence provides him with an estimated deployment of the adversary, and logistics provides their estimation of friendly forces' sustainment if the combat continues at the current rate. He reflects on his thoughts with the planning staff. They draft the Commander's intent and share it in a planning collaboration platform to initiate parallel planning among the subordinate commands.
• 2025 - Immersive user interface and visual effects created by Military Companion (artificial intelligence bot) hosts the Commander in digital twin Enterprise or Battlespace, where she is constantly aware of the situation, gains insights from vast amounts of data prepared by the Companion, runs war games to estimate outcomes from options, or ask her Companion to create possible courses of action analysed from structured data, unstructured information, history records, doctrinal knowledgebase, and real-time data flows.

Figure 1: A View to roadmap towards Data-driven Military enterprise

The timely sequence does not apply linearly as some militaries have all the above behaviour within their large enterprise, some forerunners are already in immersive reality, and others are journeying somewhere on the roads of evolution. There is a difference in outcome when the military governs data usage and development (preadaptation) compared to the technology driven floating along the stream (adaptation).  The following sections provide a framework for data governance and describe arrangements for data management.

2. Data Governance Approaches in Military Organizations

2.1 Common Data Governance Model

While the value the Military can gain from available data is increasing, the information and data assets become resources that need governance, management, and operation to enable the optimum outcome. The data are different assets to manage compared to paper documents. Hence, the previous material item-based management culture  needs to be transformed. Figure 2 provides a common Data Governance structure for line command and an example of arranging the data governance transformation program.

 

Figure 2: A General Data Governance Framework

2.2. Benchmarking some Military Organizations

Table 1 provides a view of various military organisations' ways of governing their data and transforming towards data-driven enterprise. The following tendencies emerge from comparison:

• Defence Forces of Finland data governance follows their line command because of their readiness requirements. Emphasis is on capability owner, their ownership of data and their responsibility to develop new capabilities. ICT is an enabler, and X6s act as data stewards in their area/organisation of responsibility.
• US DoD also follows the line command as their Services are independent and strong. DoD level focuses on policies, strategies, and compliance measuring but extends their governance deep towards the defence industry and partners. 
• UK MoD has data governance similar to Finland; only their titles differ. The capability owner is the data policy owner, and an organisation has an executive data steward.
• Australian Defence Forces also follow line command in their data governance. Still, their strategic level board focuses beyond the borders of the organisation (partners and providers) and nation (5 eyes).

Table 1: Benchmarking some military ways of data governance

Level/Country	FIN	USA[1]	UK[2]	AUS[3]
Strategic	Chief Digital Officer reports to Chief of Strategy; Defence Board resolves issues over the extended FINDEF enterprise.	Chief Digital and Artificial Intelligence Officer, CDAO/DoD[4] reports to the Deputy Secretary and governs efforts over the DoD, sets the policy and oversees the implementation of DoD data strategy.	Defence CIO reports to the Secretary and has a subordinate Director of Digital Enablement who chairs the data governance board (main customers represented) overseeing the implementation of strategy and policy compliance.	Chief Data Integration Officer reports to the Chief of Force Integration[5] and develops and releases policies and guidance. CDIO chairs the Defence Data Management Board, which oversees the extended enterprise data governance.
Joint	J6 coordinates over capability owners. Capability owners own their data. Process owners as Stewards develop data usage.	Chief Information Officer/DoD ensures data integration and development. JADC2 cross-functional team, Joint artificial intelligence centre and CIO for C3 provides the coordination. DoD Comptroller/CMO empowers the application of business intelligence.	Data policy owners are setting priorities and developing policies in each business area.	Appointed Data Custodians implement the strategy within their fields of responsibility.
Service	X6 of each force/command is the Steward of the data in use.	CIO/X6 of each service act as Data Stewards of their organisation. Roles of Stewards and responsibilities for Custodians are assigned to line organisation.[6]	Executive data stewards are responsible for improvements. Domain data stewards improve business processes.	Appointed Data Custodians implement strategy and comply with policies in their areas of responsibility.
Transformation	CDO coordinates transformation with the data governance office, impacting each capability development program and measuring annual performance indicators.	CDO Council identifies and prioritises data challenges, develops solutions, and oversees compliance with policy and standards. The council uses working groups to create plans and implement them.[7]	The CIO's arm of the Data Centre of Expertise maintains the data catalogue. It accelerates transformation and strategy implementation through data domain working groups.	Data Management Body of Knowledge supports transformation.

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[1] https://media.defense.gov/2020/Oct/08/2002514180/-1/-1/0/DOD-DATA-STRATEGY.PDF

[2] https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/877705/Defence_Data_Management_Strategy_2020_FINAL_FINAL.pdf

[3] https://www.defence.gov.au/about/strategic-planning/defence-data-strategy-2021-2023

[4] https://www.ai.mil/index.html

[5] https://www.defence.gov.au/about/who-we-are/organisation-structure/australian-defence-force-headquarters

[6] https://www.dcma.mil/Portals/31/Documents/Policy/MAN_4502-15_(20220401).pdf

[7] https://www.ai.mil/blog_04_30_20-jaic-leaders-establish-data-governance-council.html

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Benchmarks define the following good practices:

1. Line of command is followed to enforce ownership and responsibilities like in other resources (people, finance, facilities, platforms, and systems)
2. Data governance must reach outside the military organisation as more data resides in other government entities, the defence industry and alliances.
3. Data is nothing by itself but an essential component in platforms and processes. Moreover, the use of data for decision-making needs an innovative and empowering incubator, a Centre of Excellence, where data science, architecture, and engineering meet and create knowledge tools for commanders.

The following Section describes a concept that combines the governance framework with flavours from the good practices.

3. A Concept for Data Governance in a Military Enterprise 

In a military enterprise, the usual information/data management-related roles are as follows:

• Capability Owner  is accountable for the life cycle, combat readiness, sustainability force structure, and performance of a military capability. Since the information is a valuable asset within any military capability, the owner needs at least to govern the data.  Typical Capability Owners are, for example, Commander Land Forces, who owns Land Combat capabilities; J2, who owns Joint Intelligence capabilities; J6, who owns Joint C5I capabilities; and J4, who owns Joint Logistics capabilities. The capability Owner is usually the Data Owner of the information assets.
• The process Owner is responsible for designing an effective and efficient process, using the right people and financial and technical resources to run the process, and delivering quality outcomes as required within the organisation.  The process owner reports to the Capability Owner, develops how the process utilises information and data for performance and output, governs the end-to-end processes, and is usually a Data Steward.
• Organisational Data Steward governs the creation, utilisation, processing and storing of data and information in particular organisations.  Usually, the Chief Information Officer , CIO, is the Data Steward in military organisations .
• Operation centres, Centres of Excellence and process development hubs act as process managers, are responsible for the end-to-end execution of processes, have operational control, facilitate daily activities, and provide insight into where improvement is needed to enhance performance. 
• Users and information creators are the customers of the data. Data users can be individuals or other organisations. The chief responsibility of the data users is to ensure that they store, process, and securely handle the data and work to maintain integrity.  Users and creators are also Data Custodians.
• ICT Service Providers produce ICT services by transmitting, storing, retrieving, or processing information using network and information systems.  Service Provider owns, operates, manages, or provides any ICT service.  Software as a Service (SaaS) or Platform as a Service (PaaS) Providers usually act as Data Custodians. Infrastructure as a Service (IaaS) Provider may have some data backup-related responsibilities but is typically not a Data Custodian.

Man versus Machine at Combat Tactical Level Decision Making

The human ability to collect information, make sense of a situation, optimise action, and learn while executing has been challenged recently in games, simulators, diagnoses, and real-time analysis. How may this development reflect to future tactical combat level decision-making? Is the machine going to win the man in combat?

"This requires not only substantial investment in resources but also an open-minded and exploratory approach, in contrast to the common but sometimes exaggerated perception of military organisations as conservative entities." Meir Finkel (Finkel 2023)

"Fifth-generation warfare shifts the focus from kinetic force in physical dimension to the impact information dimension, where narratives and perceptions take centre stage, enabled by emerging technologies such as artificial intelligence, automation, and robotics." Daniel Abbott (Abbott 2010)

The article reviews some recent achievements in artificial intelligence, sets the situation for the combat technical level functions, digs deeper into decision-making under stressful conditions and illustrates a possible vision for the future state. The aim is to shake the historically conservative concepts of land battle to consider future possibilities.

Artificial Intelligence Improvements in Decision Making

A view to the evolution of machine learning improvements in various strategic-tactical games and competitions in Table 1 shows that machines are catching up and dominating men in table, card and video games and creativity competitions. Furthermore, fast-learning general-purpose algorithms are beating dedicated algorithms in those same games. 

Table 1: A sample of improvements in Machine learning applications in gaming and creativity

Year	Confrontation	Improvement
1997	Chess: DeepMind against Garry Kasparov	It took IBM 11 years to build and use customised chips to execute parallel searches. DeepMind was able to evaluate 200 million positions per second.
2016	Go: AlphaGo against Lee Sedol	A neural network-based algorithm first learned from game data, then played against itself, and finally, improved based on made mistakes. AlphaGo was able to create an unseen move during the game.
2017	Chess: AlphaZero against Stockfish (2016 top chess engine)	General purpose reinforcement learning algorithm that learned Chess after playing 4 hrs against itself. AlphaZero was able to assess 80 000 positions per second.
	Shogi: AlphaZero against Elmo (2017 world champion Shogi engine)	The algorithm learned the game after playing 2 hrs by itself. AlphaZero was able to assess 40 000 positions per second on a board that has more options than Chess.
	Go: AlphaZero against AlphaGo Lee (advanced Go engine)	Deep neural network with tabula rasa reinforcement learning algorithm. The algorithm learned the game within three days while playing itself.
	Poker: Liberatus against four champion poker players	The algorithm used a game theoretic approach for reasoning in an imperfect information environment while playing simultaneously against four human players with the following abilities: ·        Managing the whole poker competition in advance ·        Solving each game during the contest ·        Self-improvement after each day of the three-week competition
2019	Dota 2: Open AI Five against a Team of 5 esport players	The algorithm used proximal policy optimisation. The algorithm used 800 petaflops/s to gain about 45 000 years of experience within ten months. The short-term average decision time was 80ms.
2020	AlphaFold2 doubled the score of human competitors in Critical Assessment of Structure Prediction.[1]	The algorithm predicted 3D structures based on complicated rules faster and more holistic than a human.
2022	AI model that uses tens of terabytes of Earth system data and can predict the next two weeks of weather tens of thousands of times faster and more accurately than contemporary forecasting methods.[2]	With enormous amounts of data, ML algorithms can create forecasts of very complex phenomena.

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[1] https://www.technologyreview.com/2022/02/23/1045016/ai-deepmind-demis-hassabis-alphafold/

[2] https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science

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In conclusion and, in theory, a machine combined with the above features could:

1. Starts from zero knowledge and trains within months to master given battle scenario's technical, tactical, and possibly operational level features for victory.
2. Anticipates adversary moves ahead, creates picture of potential scenarios, and predicts adversary manoeuvring in 3-D space better than humans.
3. Makes short-term decisions within 80 milliseconds and optimises decisions simultaneously at technical and tactical levels.
4. Identifies lessons from the events and gains 150 years of theoretical combat experience teaching itself overnight.

 

Technical Level of Ground Combat is a Complex Military Decision-Making Environment

Probability and chance are well-recognised (Clausewitz 1984) (Fuller 2012) (Oliviero 2021) factors of battle environment. Tactics-technical level combat capability is a sum of surprise, manoeuvre, mass, firepower, and tempo (to name some essential tenets) orchestrated in variety of combinations with Command and Control to disrupt the adversary's socio-technical military system and exhaust its fighting ability. (Friedman 2017) The tactical tenets are in transformation to address the foreseeable changes on the battlefield. First, let's review the most likely changes in land warfare and, second, see what they will require from tactical tenets.

RUSI Land Warfare Conference (RUSI 2023) promoted the following tendencies of change in land warfare, which will challenge the contemporary tactics:

1. Transparent battlefield

• Civilian and military LEO satellite-based sensors provide a continuous feed of information from above the battlefield. The data can be acquired from commercial sources and fused with algorithms trained to identify especially military action on the ground.
• Unattended ground sensors improve details and add reliability to real-time event pictures.
• Cover and concealment become harder since sensors can fuse detection data from different parts of spectrum.
• Adversary will know the location and movement of blue forces as quickly as the information flows in the blue battle management system.

2. The concentration of effects vs protection

• Standoff weapons, lethal autonomous weapon systems, and precision warheads make it challenging to survive with contemporary armour. Adding armour thickness slows tactical mobility.
• Concentrated armoured units create a lucrative target for conventional artillery, attack helicopters, or massing of anti-tank UASs.
• Platforms and actors need to become more expendable and distributed but able for coordinated manoeuvres and fires.

3. Sustainment

• Logistics enables the tempo of fighting and is essential for offensive operations. Supplying distributed units require new delivery methods.
• Movement and mass of material expose logistics for continuous, wide-spectrum surveillance, so protection and endurance of logistics become a challenge.

4. Situational awareness

• An increasing amount of data and information challenges sense-making as human cognition overburdens from large amounts of information, loses focus in the stimulus-rich environment, and makes a biased conclusion.
• The organisational culture may prevent the distribution of information (need-to-know vs need-to-share; air-gap security vs zero-trust security), so situational awareness does not meet the requirements of distributed tactics. (Mansoor and Murray 2019)

5. Boundless, urban battlespaces

• People reside primarily in urban environments, and military strategies aim to "capture the will of the people and their leaders, and thereby win the trial of strength." (Smith 2005)
• Participating actors in urban battlespace may include, for example, civilians, communal authorities, law and rescue institutes, local corporates, international corporates, non-governmental organisations, insurgents, commercial military companies, interest groups, militias, criminal organisations, adversary regular forces and adversary coalition units. (Waterman 2019)
• The urban environment is more complex as these actors do not carry clear signs for identification, their intentions may transfer from day to night, and they do not follow agreements on war crimes.

In conclusion, the following Table 2 reflects the above tendencies to classical tenets of tactics and illustrates the possible impact in battle techniques and tactics and, therefore, change of tactical sense- and decision making.

Table 2: How do visible tendencies of change in land warfare affect tactical tenets of ground combat?

Tenet / Tendency	Surprise	Manoeuvre	Massing of force	Firepower	Tempo
Transparency	Surprise in land domain may be gained through other domains and dimensions.	Swarming manoeuvre of smaller, less detectable platforms.	Concentration becomes lethal, but dispersion rules.	Target acquisition is more lethal if situational awareness is achieved.	The advantage is harder to gain in a transparent battlefield.
Effect	Systems effect creates surprise and disrupts force cohesion.	A large, moving, hot, and radiating platform is an easy target.	Calls for a mass of nimble, small, and mobile warheads	The 4IR produces software-defined effectors.	Dispersed effectors will increase friction and entropy.
Sustainment	N/A	Higher mobility and wider distribution obscures logistics.	Dispersed troops increase the logistical challenge.	Smart warheads require software maintenance.	Besides live supplies, the force needs technical maintenance.
Situational awareness	Digitalised C2 creates more cognitive bottlenecks.	Becomes a core enabler and vulnerability for the swarming of distributed effectors.	Becomes a core enabler and vulnerability.	Becomes a core enabler and vulnerability.	Becomes a core enabler and vulnerability .
Urbanisation	Provides concealment in the physical dimension.	Slows manoeuvre and promotes smaller, autonomous, and agile platforms.	Constraints massing of units, but prefers small, swarming effectors.	Favours defence but constraints offence.	Slows down units and increases their entropy.

Art of Military Sense- and Decision-making

A Concept for Sense- and Decision-making

The classical military decision-making framework defined by John Boyd is simplified as Observe, Orient, Decision, and Action (OODA) (Osinga 2007). Based on this framework, Figure 2 illustrates a concept for sense- and decision-making. In this context, sense-making consisting of observation and orientation, which interprets the equivocal data. (Mattila 2016) Furthermore, decision-making is searching and selecting alternatives optimising between projected results, capabilities, and constraints. (Mattila 2016) The concept has three different situational pictures: real-time events per domain, composed operational picture, and forecasted possible/intended situations, which are referred to existing information and, finally, shared and agreed upon at the socio-cognitive level.

Figure 2: Concept for Observe, Orient and Decide at the Military tactical level

The above Command and Control (C2) concept may be established with an emphasis on creative leadership or policy compliance. These emphases are founded in the culture from which armed forces are generated. For example, German culture from 1871 – 1945 promoted officers' autonomous and aggressive action on the battlefield. (Mansoor and Murray 2019) Conversely, after the forceful manipulation of Bolshevik government, Russian culture produced obedient younger officers and relied on experienced and resourceful commanders at the operational level. (Freedman 2022)

A Team of Military Officers in Decision-making

A successful military command should be a mixture of compliance with institutional management culture and creative operational art. (Kuronen 2015) German culture before WW II reflected the war as "an art, a free and creative activity founded on scientific principles." (Condell and Zabecki 2008) The US FM 5-0 requires adaptive leaders"…who do not think linearly, but  who instead seek to understand the complexity of problems before seeking to solve them…" (Cojocar 2011) On the other hand, NATO assesses military success with five measures of merit and only one of them, measures of performance (MoP), includes some personal leadership features. (CCRP 2002) The other four enforce doctrinal and process compliance. (NATO RTO 2002) The 1/5 ratio in expectations does not indicate innovative tactical decision-making from NATO officers.

At the tactical commitment level, all efforts should focus on gaining the initiative and, eventually, victory over the adversary (reduction of adversary combat power by more than 30%). (Oliviero 2021, 51) In reality, this is not necessarily evident for all officers: 

• Training enforces drills and tactical forms, so officers prefer to use familiar concepts to solve battlefield challenges in decision-making. 
• Viewpoints may be constrained by their basic training and arms. An infantry officer aims to gain ground, an armoured forces officer aims to gain distance, or an artillery officer assesses ranges, amount of ammunition and supplies to impose a particular effect. 
• The Red Force doctrine, officers are training against, remains linear, predictable, and unimaginative adversary. 
• Since live exercises are expensive, officers train their tactical decision-making in war games, which often neglect friction, fog, chaos, and cognitive stress present on the battlefield.

Studies (Henaker 2022) (Scott and Bruce 1995) (Loo 2000) have concluded that there are five different decision-making styles categorising individuals when making important decisions: Rational, Intuitive, Dependent, Avoidant and Spontaneous.

1. Rational seeks information systematically and prefers logical assessment. However, rational has challenges in creativity and implementation of decided intent.
2. Intuitive recognises details from the information flow and matches patterns that feel right. Intuitive relates positively to creativity and difficulty-solving. 
3. Dependent seeks social conformance from others before decision-making. The decision-making process may be distracted and in need of social support.
4. Avoidant tries to postpone decision-making because of their low self-esteem. Still, avoidant is compliant with policies, doctrines, and orders. Avoidant is not suitable for creativity and tends to have high stress levels.
5. Spontaneous tries to accomplish decision-making as soon as possible. Spontaneous does not like conflict situations but perform well in rash decision and high-risk situations.

Human vs Machine Decision-making in Future Battlefield

The section fuses the tenets of tactical combat with visible transformations and tries to reflect these new situations in human-centric and machine-centric decision-making as featured in previous sections. Table 3 illustrates the outcome of the fusion from the view of two champions:

• Human is assumed as an average decision-making officer with 3-4 years of military education and about five years of professional experience with, possibly, one year of experience gained in live tactical action. 
• Machine is assumed to be a high-performance computer running a combination of continuously learning algorithms, expert algorithms, and pre-trained algorithms with real-life or synthetic data. Digital connectivity is supposed to be at combat cloud level . 

Table 3: Human vs Machine decision-making in transforming tactical combat environment

Transforming tenets of tactical combat feature decision-making challenges	Human	Machine
Transparency increases information and requires more computing power to make sense of collected data. Tactics prefer smaller, profoundly dispersed, manoeuvrable effectors, which swarm for effect, and retreat quickly.	Available data and information may overburden the cognitive ability to comprehend the situation.	A machine can recognise images, find patterns from large data mass, and forecast complicated, interdependent behaviour.
Effect calls systems understanding for system-wide impact. Dispersed effectors are harder to control and coordinate. Software-defined precision requires better target acquisition and configuration.	The adversary must be understood as multi-dimensional actor-network (Inglis and Thorpe 2019). Dispersed effectors require coordination of larger volume of details.	A machine can map the COA spectrum, model complicated, interdependent systems, and optimise the action of small effectors.
Sustainment of distributed, cyber-physical platforms requires more flexible and expert maintenance.	Rising complexity of critical paths on availability or sustainment may overwhelm cognitive capacity under stress.	With a digital-twin model and scenario-based simulation, a machine creates an overall logistics picture and can optimise sustainment.
Awareness is achieved by delegating sense-making to lower cooperative level or improving the information management ability of a steeper, hierarchical command structure.	Socio-cultural structures and beliefs handicap the application of the optimum C2 method.	Socio-cultural structures do not constrain a machine, and it can act even with partial information environment.
Urbanisation increases entropy, slows the tactical pace, increases casualties, raises the need for sustainment, and makes the environment and situation harder to understand.	The urban environment increases entropy and requires more innovative decision-making.	A machine makes sense of complicated situation even with partial information, recognises faster volatile behaviour, and optimises effort and sustainment.

A Company Commander Meets an Ex-Machina Battle Captain

When a Human Commander meets an Ex-Machina Captain within a tactical scenario on a future battlefield, the parties of combat may have the different abilities for decision-making. In situation with equal forces, linear doctrines, and a reasonably stable battlefield, the company commander does not have a chance against Ex-Machina. A creative human commander may gain an advantage in more chaotic conditions and with innovative tactics. Are our military institutes educating agile officers? Still, higher man-machine teaming performance indications are positive in Dota 2 strategic game, but it remains to be studied in future articles.

 

Figure 3: Man vs machine in tactical decision making