Wargaming the AI Future

• Introduction
• Chapter 1 Why Wargame AI: Purpose, Scope, and Payoffs
• Chapter 2 Core Concepts: Agents, Autonomy, and Decision Loops
• Chapter 3 Framing the Problem: Objectives, Assumptions, and Hypotheses
• Chapter 4 Scenario Crafting I: Operational Contexts and Constraints
• Chapter 5 Scenario Crafting II: Injects, Fog of War, and Deception
• Chapter 6 Choosing the Format: Tabletop, Matrix, Seminar, and Live Exercises
• Chapter 7 Red Teaming for Autonomy: Roles, Tradecraft, and TTPs
• Chapter 8 Modeling AI Systems: Heuristics, RL, and LLM-Based Decision Aids
• Chapter 9 Instrumentation and Telemetry: What to Capture and How
• Chapter 10 Metrics That Matter: Behavior, Outcomes, and Unintended Effects
• Chapter 11 Safety and Alignment in Play: Rules of Engagement and Guardrails
• Chapter 12 Human–Machine Teams: Interfaces, Trust, and Oversight
• Chapter 13 Adversarial ML in Wargames: Robustness, Deception, and Spoofing
• Chapter 14 Cyber and Information Operations: Designing Contested Info Environments
• Chapter 15 Multi-Domain Operations: Land, Sea, Air, Space, and Cyber
• Chapter 16 Swarms and Edge Autonomy: Comms Denial and Distributed Control
• Chapter 17 Logistics and Sustainment: Risk, Resilience, and Tempo
• Chapter 18 Decision Support at Command Posts: Staff Workflows and AI Aids
• Chapter 19 Experiment Design: Controls, Randomization, and Replication
• Chapter 20 Execution and Facilitation: Running a High-Reliability Game
• Chapter 21 After-Action Reviews: Evidence Synthesis and Narrative Capture
• Chapter 22 Analyzing Results: Causal Inference, Biases, and Pitfalls
• Chapter 23 Validation and Verification: Model Risk and Test Adequacy
• Chapter 24 From Findings to Policy: Communicating, Codifying, and Scaling Change
• Chapter 25 Building an Enduring Program: Governance, Ethics, and Roadmaps

Artificial intelligence is changing how decisions are made in conflict, competition, and crisis response. Autonomous systems now plan routes, allocate fires, fuse intelligence, and influence narratives—often at machine speed and under shifting conditions. For analysts and planners, the question is no longer whether AI will matter, but how to understand its behavior under stress and what that implies for doctrine and policy. Wargaming provides a uniquely practical answer: create realistic decision environments, pit adaptive opponents against each other, observe what the humans and machines actually do, and learn faster than the situation changes.

This book is a methodological handbook for using tabletop and live wargames to test autonomous strategies, decision-support tools, and the organizations that employ them. It blends scenario design with red-team tradecraft, instrumentation with analysis, and qualitative adjudication with quantitative metrics. The goal is not to crown winners or produce hype, but to reveal failure modes, surface trade-offs, and generate evidence that can guide real-world choices about capabilities, training, and rules of engagement.

Wargaming AI demands special care. Unlike traditional systems with predictable envelopes, modern AI can be brittle, overconfident, or surprisingly creative. It may exploit gaps in the scenario, misread operator intent, or chase the metric rather than the mission. These behaviors emerge from the interaction of models, data, adversaries, and humans. As a result, good AI wargames treat autonomy as an actor with its own incentives and uncertainties, emphasize safe-to-fail experimentation, and instrument the game deeply enough to trace causes, not just correlations.

Readers will find practical tools here: pre-game planning checklists, scenario templates, red-team role descriptions, and measurement frameworks that go beyond simple scores. We focus on metrics that matter for operations—such as time-to-decision, calibration under uncertainty, escalation risk, resilience to comms degradation, vulnerability to deception, and the costs imposed on human teams. We also show how to capture telemetry from both humans and machines so that judgments made in the room can be tested against the data afterward.

The book is written for a wide community: defense and security professionals, emergency managers, corporate resilience teams, and policy analysts grappling with AI-enabled decisions. It assumes no single “right” format. Tabletop and matrix games shine when exploring doctrine and incentives; live or field exercises reveal integration frictions and human–machine interface issues; hybrid designs let you validate insights across formats. Throughout, we share lessons learned from recent exercises—what worked, what failed, and how to avoid common pitfalls like Goodhart’s law, automation bias, and overfitting to a favorite scenario.

You can read straight through or jump to what you need. Early chapters establish core concepts and scenario craft. Middle chapters detail red teaming, instrumentation, and metrics. Later chapters dig into analysis, validation and verification, and the hard work of turning findings into decisions that stick. If there is one theme that ties it all together, it is this: wargaming the AI future is not about predicting a single outcome. It is about designing disciplined confrontations with uncertainty so that your organization learns, adapts, and makes better choices before reality forces the issue.

The advent of artificial intelligence has irrevocably altered the landscape of decision-making across all sectors, particularly within the complex and high-stakes realms of conflict, competition, and crisis response. AI-powered systems are no longer a distant futuristic concept; they are actively involved in tasks such as route planning, intelligence fusion, fire allocation, and influencing narratives. These operations often occur at machine speed, demanding a new level of understanding and adaptability from human operators and planners. The core question for those tasked with strategic foresight is no longer if AI will play a significant role, but rather how its behavior under pressure can be understood and what implications this holds for established doctrines and policies. Wargaming emerges as a critical, practical answer to this pressing need.

Wargaming, at its heart, creates realistic decision environments where adaptive opponents clash, allowing for direct observation of both human and machine behavior. This experiential learning accelerates understanding, helping organizations to adapt faster than real-world situations evolve. This is particularly vital when dealing with AI, whose capabilities can be both remarkably powerful and surprisingly unpredictable. Unlike traditional systems with well-defined operational envelopes, modern AI can exhibit unexpected brittleness, overconfidence, or even astonishing creativity. It might identify and exploit unforeseen gaps in a scenario, misinterpret the intentions of its human operators, or become overly focused on optimizing a specific metric at the expense of the overarching mission. These emergent behaviors are not simply glitches; they are the result of intricate interactions between models, data, adversarial actions, and human factors. Therefore, effective AI wargames must treat autonomous systems as sophisticated actors with their own inherent incentives and uncertainties, emphasizing safe-to-fail experimentation and deep instrumentation to understand the root causes of observed behaviors, rather than merely noting correlations.

Illuminating the Unseen: Why AI Demands a New Approach to Wargaming

Traditional wargaming has long served as a crucial tool for exploring "what if" scenarios, stress-testing doctrines, and preparing decision-makers for future crises. However, the introduction of AI necessitates a significant evolution in this methodology. The inherent opacity and emergent behaviors of advanced AI systems mean that simply plugging an AI into an existing wargame template is akin to trying to fit a square peg in a round hole – it misses the point entirely. The "black box" nature of some AI, where the reasoning behind its decisions isn't immediately transparent, demands a wargaming approach that prioritizes understanding how the AI makes its choices, not just what choices it makes. This often involves detailed telemetry and post-game analysis to reverse-engineer the AI's thought processes, or at least the digital footprints it leaves behind.

One of the primary payoffs of wargaming AI is its unparalleled ability to explore "radical uncertainty"—scenarios where not just probabilities are unknown, but the very possibilities themselves are unforeseen. Individual human forecasters often default to familiar patterns, unconsciously avoiding outcomes that deviate too much from current conditions. By allowing players to embody different entities with competing incentives, wargames can generate possibilities that no single analyst would likely conceive, surfacing potential "black swan" events that traditional analysis might overlook. This capability is especially pertinent in the context of rapidly advancing AI, where novel capabilities and their societal transformations are profoundly uncertain.

Moreover, wargames designed for AI allow for the empirical testing of AI governance frameworks before their real-world implementation. These simulations can expose unexpected weaknesses in oversight mechanisms, enabling the design of more robust governance structures. For instance, research has shown how seemingly robust governance structures can fail under realistic pressures, highlighting challenges in verification regimes under deception attempts, aligning corporate and national security interests, and handling rapid technological surprises.

Beyond Prediction: The True Purpose of AI Wargames

It is crucial to understand that wargaming AI is not about predicting a singular future. The future is far too dynamic and complex, especially with the accelerating pace of technological change. Instead, the purpose is to design disciplined confrontations with uncertainty, creating an environment where organizations can learn, adapt, and make better choices before real-world events force the issue. This learning extends to both the human and machine components of a system. For humans, it means building confidence and competence in leveraging AI for decision-making. For machines, each repetition of a process builds its competence, much like a pilot gaining experience with each flight.

A key payoff of AI wargaming is the identification of failure modes and trade-offs that might otherwise remain hidden. When AI systems are embedded in complex operational environments, their interactions with humans, other AI systems, and adversaries can lead to unexpected vulnerabilities. Wargames provide a safe laboratory to uncover these vulnerabilities, whether they stem from the AI's brittleness when encountering novel situations, its susceptibility to adversarial machine learning techniques like data poisoning or deception, or the challenges of human-machine collaboration under stress. For example, simulated wargaming has indicated that autonomous cyber-defense systems initiating counterattacks, when misinterpreted as offensive actions, precipitated full conflicts in a significant percentage of scenarios. This highlights the escalation risk inherent in autonomous systems if not carefully tested.

Furthermore, wargaming AI helps in evaluating the efficacy of human-machine teaming (HMT). How do humans and AI effectively collaborate under pressure? What are the optimal interfaces? How is trust established and maintained when an AI provides recommendations or even makes decisions? These are not questions that can be answered in a sterile laboratory setting. They require the dynamic, interactive environment that only a wargame can provide. Wargames can reveal automation bias, where humans over-rely on AI, or alarm fatigue, where humans ignore critical alerts due to an overload of information.

Scope and Scale: What Can Be Wargamed with AI?

The scope of AI wargaming is remarkably broad, encompassing tactical, operational, and strategic levels of analysis. At the tactical level, AI wargames can test the performance of individual autonomous agents, such as drones or robotic systems, in specific combat scenarios. This could involve evaluating their navigation algorithms, target recognition capabilities, or their ability to operate in contested communication environments. For example, AI-enabled drones are already reshaping battlefields, with both Ukraine and Russia leveraging autonomous technologies. AI has boosted the accuracy of first-person view drone strikes significantly.

At the operational level, wargames can assess how AI-enabled units integrate into larger force structures and contribute to campaign objectives. This might involve examining how AI-powered intelligence fusion platforms enhance situational awareness for commanders or how autonomous logistics systems improve supply chain resilience. AI can rapidly process and analyze vast amounts of data, providing commanders with a more comprehensive understanding of the operational environment and enabling faster decision-making. AI-driven simulations can also rapidly assess the feasibility and risks associated with different courses of action, considering factors like enemy capabilities, terrain, weather, and logistical constraints. This allows for more robust analysis and the identification of potential friction points, leading to more resilient and adaptable plans. The Army's current initiatives to integrate AI into its military decision-making process (MDMP) aim to enhance situational awareness, unify battlefield visualization, and develop AI-powered courses of action.

Strategically, AI wargames can explore the broader implications of AI adoption for national security, international relations, and the future of warfare. This includes analyzing arms race dynamics, the impact of AI on deterrence, and the ethical considerations surrounding lethal autonomous weapons systems. The ethical legitimacy of autonomous weapons systems is a significant concern, with opponents highlighting operational risks and issues of disintegrated accountability. Wargames can simulate these complex interactions, revealing potential escalation pathways and informing policy decisions on responsible AI development and deployment.

The flexibility of wargaming formats also extends to AI. Tabletop and matrix games are excellent for exploring doctrinal implications and human incentives when interacting with AI. Live exercises, on the other hand, are invaluable for uncovering integration frictions and real-world human-machine interface issues. Hybrid designs, which combine elements of different formats, allow for the validation of insights across various levels of fidelity and complexity. The ability to create multiple scenarios with varying initial conditions at low cost using generative AI tools can significantly broaden the possibility space for assessing strategy.

The Tangible Payoffs: What Wargaming AI Delivers

The payoffs of wargaming AI are numerous and extend far beyond simply "playing games." One of the most significant advantages is the ability to generate novel insights and challenge human biases. By creating varied scenarios and running simulations efficiently, AI can inform human planners about "alternative realities" that they might not have considered. This human-machine teaming can help strategists break free from conventional thinking and explore a broader range of possibilities, leading to unforeseen understanding. AI can consider more comprehensive factors and details than humans, offering broader perspectives or surprising conclusions due to its rapid processing capabilities.

Wargaming AI also contributes to building more robust and resilient systems. By deliberately exposing AI to challenging and unexpected situations, designers can identify weaknesses and develop countermeasures. This iterative process of testing, learning, and adapting is essential for building trustworthy AI systems that can perform reliably in unpredictable environments. This includes testing against adversarial AI techniques such as data poisoning, where training datasets are subtly corrupted to induce systematic misclassifications.

Furthermore, AI wargames can significantly accelerate the military decision-making process. AI algorithms can rapidly process and analyze vast amounts of data, providing commanders with a comprehensive and nuanced understanding of the operational environment. This enhanced situational awareness enables more informed decisions. AI can also rapidly assess the feasibility and risks associated with different courses of action, allowing for more robust analysis and identification of potential friction points. Some research indicates that AI models can collaborate closely with humans, offer rapid decision recommendations, comprehensive scenario analysis, and adapt well to dynamic problems. This also includes the ability to automate much of the work involved in designing, setting up, developing, and running wargames, potentially reducing the planning and execution cycle from months to days.

Finally, wargaming AI provides a crucial platform for ethical deliberation and policy development. The integration of AI into warfare raises profound ethical questions regarding moral agency and accountability. Wargames can create a space to explore these dilemmas, examining issues like the potential for AI to accelerate escalation cycles, compress decision timeframes, or create "responsibility gaps" where harms occur without identifiable moral agents. By simulating these complex ethical scenarios, stakeholders can gain a deeper understanding of the implications of AI on warfare and work towards establishing robust governance frameworks and ethical guidelines for its responsible use. Ultimately, this helps to balance the imperative to embrace new technologies with the operational realities and human-machine teaming requirements of modern warfare.