multiplex.studio.web/files/cogwar-simulation.md

Cloud-Native Modular Cognitive Warfare Simulation Platform

Introduction

Modern conflicts increasingly target the cognitive domain – the perceptions, decision-making, and behavior of people – as much as physical targets. Cognitive warfare (CogWar) leverages information attacks, psychological operations, and cyber tactics to “alter and shape the way humans think, react, and make decisions,” often in invisible and invasive ways. Preparing warfighters to counter such threats requires training beyond traditional kinetic wargames. However, current training for information/cognitive warfare is lacking. Trainees rarely experience realistic simulations of social-media-fueled attacks or misinformation campaigns preceding cyber strikes. Instead, most cyber-defense exercises today are simplistic tabletop drills with scripted injects, failing to immerse trainees in the chaotic information environment that characterizes real incidents.

Recognizing this gap, the Navy’s SBIR topic N252-110 calls for “a simulation model of information warfare” that realistically represents multi-modal attacks – specifically cyber-attacks and their social media precursors. The envisioned system would enable live, virtual, constructive exercises where information conflict plays a key role, complete with tools to help exercise planners manage complex scenarios and provide White Cell adjudication support. In short, the Navy needs a realistic, rapidly updatable simulation environment blending cyber and information warfare elements for training purposes.

This whitepaper proposes the development of a cloud-native, modular cognitive warfare simulation platform to meet these needs. Our approach centers on three integrated components working in tandem:

1. Hybrid Simulation Engine (Agent-Based Modeling + LLM): Combines agent-based modeling of actors/networks with large language model (LLM)-generated dynamic social and narrative content, simulating information spread and generating realistic messages, posts, and reports.

2. Real-Time Scenario Adaptation via Reinforcement Learning: An AI-driven agent using reinforcement learning (RL) to dynamically adjust scenarios based on trainee performance and unfolding events, calibrating difficulty to ensure optimal training engagement.

3. Gamified User Interfaces for Red, Blue, and White Cells: Role-specific interfaces engaging participants through interactive decision-making, real-time communication, and immersive scenario management, mimicking real-world information environments.

Together, these components form a cohesive system that fulfills the SBIR’s goals of creating realistic, multi-modal training exercises for information/cognitive warfare. The platform will allow trainees to experience fully interactive cyber-information attack scenarios—from social media manipulation through coordinated cyber strikes, all running on a cloud-based architecture enabling rapid updates and scalability.

Hybrid Simulation Engine: Agent-Based Modeling with LLM-Generated Content

The platform’s core is a hybrid simulation engine fusing agent-based modeling (ABM) with large language model (LLM)-driven content generation. The ABM represents entities in cognitive warfare scenarios, defining interaction rules and behaviors among adversaries, defenders, and neutral populations. Leveraging frameworks such as MITRE ATT&CK and social-cyber maneuvers, it models complex scenario dynamics.

LLM-generated content provides realistic narrative elements (social media posts, news articles, briefings), enriching scenarios with contextually appropriate content. Prompted by the ABM’s state changes, the LLM creates content dynamically, ensuring trainees respond to realistic and varied information inputs. This hybrid approach significantly broadens scenario realism and scalability.

Reinforcement Learning for Real-Time Scenario Adaptation

Our platform features an RL-driven "game master" capable of adapting scenarios in real time. Monitoring trainee performance and scenario developments, this AI agent makes decisions to introduce, modify, or withhold scenario events, maintaining optimal difficulty and ensuring key learning objectives are met. This dynamic difficulty adjustment maximizes trainee engagement and learning outcomes, adapting scenarios on-the-fly based on trainee actions and performance metrics.

Gamified User Interfaces for Red, Blue, and White Cells

The platform includes interactive, gamified user interfaces tailored specifically to the Red team (adversaries), Blue team (defenders/trainees), and White cell (exercise control and evaluation). Each interface is designed to be immersive and realistic, providing scenario-related information through simulated social media feeds, dashboards, and decision-making tools. Visualization and real-time communication tools allow the White cell to manage and adjudicate scenarios effectively. These interfaces facilitate comprehensive logging for after-action reviews, enhancing reflective learning.

System Architecture and Integration

The platform is built on a cloud-native, modular microservice architecture, enabling scalability, flexibility, and ease of integration. Each component, from the simulation engine to user interfaces, operates independently yet seamlessly through cloud-based container orchestration. This architecture ensures rapid scenario development and updates, robust performance under varying workloads, and secure, role-based access controls. AI components (LLM and RL agents) are integrated through clearly defined interfaces, enabling straightforward updates and improvements.

Phase I Technical Feasibility and Deliverables

Phase I will demonstrate core feasibility through:

• Selecting and detailing a specific cyber-social use case scenario (e.g., social-media-facilitated DDoS).
• Developing a prototype hybrid ABM+LLM simulation engine capable of realistic content generation.
• Implementing basic RL-driven adaptive scenario control.
• Creating a minimal user interface for Blue and White cells.
• Demonstrating end-to-end scenario execution with preliminary evaluations.
• Providing a documented feasibility study, dataset, prototype software, and comprehensive Phase II development plan.

Phase II Development and Extension

In Phase II, we will scale the prototype into a robust training platform by:

• Expanding use case scenarios across diverse cognitive warfare contexts.
• Enhancing synthetic data generation and potentially creating specialized LLMs for military use.
• Fully implementing sophisticated RL-driven adaptive scenario logic.
• Developing advanced scenario authoring tools and interactive user interfaces for comprehensive exercise management.
• Conducting extensive testing, validation, and demonstrating readiness in live virtual constructive exercises.

Conclusion

Our proposed cloud-native modular cognitive warfare simulation platform addresses critical Navy training gaps, significantly improving realism and responsiveness of cognitive warfare training scenarios. By combining agent-based modeling, generative AI content, real-time adaptive logic, and immersive user interfaces, the platform enables trainees to gain realistic and engaging experiences in combating multi-modal cognitive threats. Ultimately, this solution positions the Navy at the forefront of cognitive warfare training, ensuring warfighters are effectively prepared for emerging threats in the information domain.