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Wargaming the AI Future MTA
Designing Simulations and Red Teams to Test Autonomous Strategies

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About this book:
Wargaming the AI Future

*Wargaming the AI Future* is a comprehensive methodological handbook designed for defense professionals, policy analysts, and systems engineers tasked with testing autonomous strategies. The book argues that because AI systems can be brittle, opaque, and prone to emergent behaviors, traditional linear testing is insufficient. Instead, it proposes a rigorous wargaming framework that treats AI as an adaptive actor within realistic, high-stress decision environments. By pitting human-machine teams against intelligent "Red Teams," organizations can surface failure modes, identify ethical misalignments, and stress-test doctrines before real-world deployment.

The text provides a step-by-step guide to constructing these simulations, beginning with the framing of testable hypotheses and the crafting of dynamic scenarios. It emphasizes the importance of "Environmental Friction," such as communications denial and adversarial machine learning (AML), to reveal how swarms and edge-autonomous systems behave when isolated from human oversight. Detailed chapters explore the technical modeling of AI—ranging from simple heuristics to Large Language Models (LLMs)—and the critical necessity of deep instrumentation to capture the "digital footprint" of machine decisions alongside human cognitive data.

A significant portion of the book focuses on the "human element," specifically the dynamics of trust, automation bias, and effective oversight within command posts. The author outlines how to measure "metrics that matter," moving beyond simple win/loss ratios to evaluate calibration under uncertainty and escalation risks in multi-domain operations. By integrating safety guardrails and ethical adjudication directly into the game mechanics, the book provides a laboratory for defining responsible Rules of Engagement for autonomous systems.

Ultimately, the book transitions from simulation design to organizational implementation, offering strategies for validating models and translating wargame findings into formal policy and doctrine. It concludes by advocating for the establishment of enduring wargaming programs governed by a permanent ethical compass. This iterative cycle of "safe-to-fail" experimentation is presented as an existential imperative for navigating a future where the speed of AI-driven conflict outpaces traditional human deliberation.

What You'll Find Inside:
  • Comprehensive methodology for designing and executing AI wargames across multiple formats (tabletop, matrix, seminar, live) to test autonomous strategies and decision-support tools.
  • Frameworks for scenario crafting that incorporate fog of war, deception, and dynamic injects to reveal AI vulnerabilities and emergent behaviors under uncertainty.
  • Detailed guidance on red teaming techniques specifically adapted for AI systems, including adversarial machine learning tactics and human-AI interaction exploitation.
  • Measurement approaches that go beyond simple scores to capture meaningful metrics on AI behavior, human-machine teaming dynamics, and unintended consequences.
  • Practical tools for translating wargame findings into policy, doctrine, and system improvements through evidence-based analysis and communication strategies.
Who's It For:

The book is designed for defense and security professionals, emergency managers, corporate resilience teams, and policy analysts who need to understand and test AI-enabled decision systems. It assumes no single 'right' format and provides guidance for anyone involved in designing, facilitating, or analyzing wargames that involve autonomous systems, human-machine teams, or AI decision aids.

Table of Contents:
  • 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
Author:

Melissa Ferguson

Published By:

MixCache.com


Date Published:

March 25, 2026

Type:

Nonfiction

Language:

English

Word Count:

78,271 words

Reading Time:

5 hours 29 minutes

Sample:

Read Sample


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