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 # 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 ([Mitigating and Responding to Cognitive Warfare. | National Technical Reports Library - NTIS](https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/AD1200226.xhtml#:~:text=as%20availability%20and%20access%20to,ICT%29%2C%20neuroscience)). Preparing warfighters to counter such threats requires training beyond traditional kinetic wargames. However, current training for information/cognitive warfare is lacking. Trainees (e.g. cyber defenders or information operations officers) 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 “white card” injects – participants are merely *told* that an event occurred and asked how they would respond ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=attacks,fight%E2%80%9D%20%E2%80%93%20to%20experience%20the)). This method fails to immerse trainees in the chaotic information environment that characterizes real incidents, depriving them of the many cues and signals that herald an attack in the wild ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=attacks,fight%E2%80%9D%20%E2%80%93%20to%20experience%20the)). 
+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 can realistically represent *multi-modal* attacks – specifically cyber-attacks *and their precursors in social media campaigns* ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=OBJECTIVE%3A%20Develop%20a%20simulation%20model,developing%20and%20improving%20scenarios%20for)) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=hybrid%20attacks,collection%20of%20related%20hybrid%20cyber)). In other words, the Navy seeks an integrated training simulation where a cyber incident is preceded and accompanied by social-media disinformation, online recruitment, and other information operations (“social-cyber maneuvers”), allowing trainees to *“train as they fight”* in a richly layered scenario. The envisioned system would enable live, virtual, constructive exercises in which information conflict plays a key role, complete with tools to help exercise planners create and manage these complex scenarios ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=hybrid%20attacks,collection%20of%20related%20hybrid%20cyber)). The final product is expected to explain and visualize scenario dynamics and provide **White Cell adjudication support** (controls and observers) for the exercise ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=author%20and%20manage%20these%20exercises,desired%20in%20the%20final%20product)). In short, the Navy needs a realistic, rapidly updatable simulation environment that blends cyber and information warfare elements for training purposes.
+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 four integrated components working in tandem:
+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):** A simulation core that combines agent-based modeling of actors/networks with large language model (LLM) generation of dynamic social and narrative content. This engine can simulate the spread of information (or misinformation) across a population and generate realistic messages, posts, and reports in natural language to immerse participants in the scenario context.  
-2. **Real-Time Scenario Adaptation via Reinforcement Learning:** An AI-driven “director” agent that uses reinforcement learning (RL) to adjust scenario parameters on the fly based on participant performance and unfolding events. This allows the exercise to dynamically “speed up or slow down” and inject new events in real time ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=could%20be%20changed%2C%20with%20the,launched%20during%20the%20exercise%20itself)), keeping the challenge calibrated to the trainees – avoiding situations that are too easy or overwhelming ([](https://arxiv.org/pdf/2308.12726#:~:text=and%20video%20games%20,avoid%20boredom%20and%20frustration%2C%20which)).  
-3. **Gamified User Interfaces for Red, Blue, and White Cells:** A set of role-specific front-end interfaces that engage participants (Red team adversaries, Blue team defenders, and White cell controllers). These interfaces provide interactive tools for decision-making, adjudication, and real-time communication. They are designed to mimic real information environments (e.g. social media feeds, network dashboards) and include visualization and control tools so that White cell adjudicators can monitor and guide the exercise ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=author%20and%20manage%20these%20exercises,desired%20in%20the%20final%20product)).  
-4. **Crowdsourced Actors via Amazon Mechanical Turk:** Integration of human crowd participants into the simulation by leveraging Amazon Mechanical Turk (MTurk) to populate certain roles. Red and Blue team roles can be supplemented with crowd workers (to introduce unpredictability and human cleverness), and **Grey actors** – neutral or background personas – can be crowdsourced to act as “noise” in the information environment (e.g. simulating the general public’s chatter). This approach adds realism by incorporating diverse human behaviors at scale in a cost-effective manner ([Mechanical Turk - an overview | ScienceDirect Topics](https://www.sciencedirect.com/topics/computer-science/mechanical-turk#:~:text=Mechanical%20Turk%20,effective%20and%20efficient%20manner)).
+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.
 
-Together, these components form a cohesive system that can fulfill the SBIR’s goals of creating realistic, multi-modal training exercises for information/cognitive warfare. The platform will allow a training audience (e.g. a Blue team defending unit) to experience a fully interactive cyber-information attack scenario: from the initial social media manipulation and narrative buildup, through the coordinated cyber strike, and into the post-attack information battle for public perception. All of this will run on a cloud-based architecture enabling rapid updates and scalability. In the following sections, we describe each component in detail, explain how they integrate into a unified system architecture, and outline our Phase I plan to demonstrate feasibility. We also discuss the technical risks and mitigation strategies, and how the system will be extended in Phase II to deliver a robust prototype ready for Navy evaluation.
+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  
-At the heart of the platform is a hybrid simulation engine that fuses **agent-based modeling (ABM)** with **large language model (LLM)**-driven content generation. This design allows us to simulate both the *quantitative* aspects of an information warfare scenario (actors, networks, events) and the *qualitative* aspects (narrative content, language, social context) in a seamless, integrated way.
+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.
 
-**Agent-Based Model of Cognitive Conflict:** We employ an agent-based model to represent the key entities in a cognitive warfare scenario. Agents can include individual personas (e.g. social media users, hackers, analysts), groups (crowd populations or botnets), and institutions (news outlets, organizations). The ABM defines the behavior rules and interactions among these agents. For example, a set of agents might represent adversary propagandists on a social network who attempt to **influence other agents** by spreading disinformation, while some agents represent defenders or the general public who may believe, amplify, or counter these messages. Agent behaviors are based on established frameworks of information maneuver. We draw on concepts such as the BEND model of social-cyber tactics ([](https://sbp-brims.org/2023/papers/working-papers/2023_SBP-BRiMS_FinalPDF_18%20(1).pdf#:~:text=BEND%20provides%20a%20framework%20for,authors%20and%20should%20not%20be)), which defines maneuvers like *boosting* or *neutralizing* information, and the MITRE ATT&CK framework for cyber actions (adapted to include social techniques) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=these%20hybrid%20maneuvers%20to%20provide,tools%20and%20decision%20aids%20to)). The simulation engine can thus model how an online influence campaign unfolds in parallel with a cyber-attack. Prior research has shown that agent-based simulation is well-suited to capturing such complex social-cyber dynamics. For instance, Hicks & Carley (2023) developed **“an agent-based model that simulates social media users conducting social-cyber maneuvers… and visualizes two sides conducting maneuvers against each other and the effects of those maneuvers.”** ([](https://sbp-brims.org/2023/papers/working-papers/2023_SBP-BRiMS_FinalPDF_18%20(1).pdf#:~:text=Abstract,Results%20suggest%20that%20explain%20and)) Their system (BEND Battle) provided insights into how different influence tactics succeed or fail on a simulated social network. We build on this state-of-the-art by introducing a flexible, modular ABM where parameters (e.g. network structures, agent attributes, attack playbooks) can be easily modified to represent different scenarios. The ABM runs on discrete time-steps or event triggers, updating the world state: for example, calculating how many users have been swayed by a false narrative, which systems have been compromised by a cyber exploit, and how various factions (Red/Blue/Grey) are reacting.
-
-**LLM-Generated Narrative and Social Content:** While the ABM tracks numerical states and logical outcomes, the large language model component provides the *human-readable storyline* that brings the scenario to life. At appropriate simulation steps, the engine queries an LLM to produce natural language outputs: social media posts, chat messages, news reports, intelligence briefings, etc. This capability is critical for realism – trainees must see and respond to scenario developments as they would in real operations, via content on platforms and communications, rather than through dry inject descriptions. Recent advances in generative AI make it possible to automate such open-ended narrative generation within simulations ([Open-Ended Wargames with Large Language Models](https://arxiv.org/html/2404.11446v1#:~:text=real,%E2%80%9D)) ([Open-Ended Wargames with Large Language Models](https://arxiv.org/html/2404.11446v1#:~:text=been%20recognized,%E2%80%9D)). Our system will incorporate a state-of-the-art LLM (such as an open-source model fine-tuned for military and social-media contexts) to serve as a *scenario content generator*. For example, if the ABM determines that at time T a rumor is starting to trend on social media as part of the Red team’s campaign, the engine will prompt the LLM with the current context (the theme of the rumor, the platform, the personas involved) and generate a batch of fake posts or messages reflecting that development. These generated messages might include angry tweets blaming a target organization for a fabricated incident, or a rallying call in a chat group urging supporters to join a DDoS attack. The LLM’s output is then injected into the Blue team’s feed in the user interface, so the Blue participants actually *read* the misinformation and must decide how to react. Similarly, the LLM can generate feedback when Blue takes actions – e.g. a press release drafted by Blue, or a public response from a neutral authority – and even internal narrative such as an intelligence report summarizing detected indicators. By leveraging an LLM in this way, we ensure the simulation produces rich, contextually appropriate, and varied content on demand, far beyond what pre-scripted injects could cover. This dramatically **broadens the scope and scalability of the wargaming simulation** by automating content creation ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Using%20AI%2C%20game%20designers%20can,traditional%20wargame%2C%20the%20analyst%20can)), enabling exercise designers to create multiple scenario threads and narrative branches at low cost ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Using%20AI%2C%20game%20designers%20can,traditional%20wargame%2C%20the%20analyst%20can)).
-
-The combination of ABM and LLM yields a powerful hybrid engine. The ABM provides **ground truth and causality** (who does what, when, and with what effect in the simulation), while the LLM provides the **story and social texture** that make those events perceptible and meaningful to human players. For instance, in our prototype use case scenario (detailed below), an agent-based model will simulate the stages of a coordinated cyber campaign – initial recruitment of hacktivists, resource distribution, target selection, and attack execution ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=For%20example%2C%20a%20Distributed%20Denial,attackers%3B%20the%20distribution%20of)) – and the LLM will narrate each stage through messages and media that players see (such as online forum posts recruiting volunteers for a “digital protest,” emails between conspirators sharing malware, and news articles reporting on the ensuing service outage). This hybrid approach directly addresses two key deliverables of the SBIR topic: it creates **“a collection of hybrid cyber and social-cyber data”** by logging all these simulated events and generated content ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20develop%3A,tools%20and%20decision%20aids%20to)) (providing a realistic dataset of an information attack unfolding), and it implements a **“framework for information maneuvers”** by encoding behavior rules (in the ABM) and narrative patterns (via LLM prompts) that correspond to known attack and influence tactics ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=these%20hybrid%20maneuvers%20to%20provide,tools%20and%20decision%20aids%20to)). The use of an LLM also ensures the system can be rapidly updated with new scenarios – changing the narrative script no longer requires manual writing of many injects, but rather reconfiguring the prompts or training data for the model. In Phase I, we will demonstrate this hybrid engine on a focused scenario (e.g. a social-media-facilitated DDoS attack) and show that it can produce a sequence of realistic events and content. This will validate the feasibility of using advanced AI (LLMs) in military training simulations, building on recent studies that show **synthetic agents and data can effectively mirror human behavior** in such exercises ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Therefore%2C%20rather%20than%20directly%20relying,toward%20divergent%20viewpoints%20and%20debate)). Our design is modular: the ABM and LLM communicate via clearly defined interfaces (the ABM passes state context, the LLM returns text outputs), allowing the LLM component to be improved or swapped (for instance, upgraded to a domain-specialized model in Phase II) without altering the simulation logic.
+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  
-A standout feature of our platform is its ability to intelligently adapt the scenario in real time through reinforcement learning. Traditional exercises are usually static – once the script is written, the events unfold in a predetermined way regardless of how participants perform. In contrast, our system includes an AI “game master” that monitors the exercise and dynamically adjusts it to optimize training value. This addresses the SBIR requirement that the system support rapid scenario updates, even to the point of allowing the scenario to be **“sped up or slowed down based on participant performance” and for new injects to be launched during the exercise** ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=could%20be%20changed%2C%20with%20the,launched%20during%20the%20exercise%20itself)). 
-
-The reinforcement learning component functions as a *scenario controller agent*. Its goal is to keep the training experience within an optimal difficulty range and to ensure key learning objectives are met, all while maintaining realism. We formulate this as a sequential decision-making problem: at each time step (or decision point) in the simulation, the controller agent can choose to introduce, modify, or withhold certain events in the scenario. Examples of actions the RL agent might take include: triggering an additional piece of misinformation if the Blue team is countering the current narrative too easily, accelerating the timeline of the next cyber attack phase if Blue’s response has been slow (to pressure them), or conversely, delaying or dialing back some Red activities if Blue is overwhelmed and missing critical cues. The agent could also adjust environmental parameters – for instance, increasing the volume of background noise (chatter) to raise the challenge, or having a usually neutral actor inject a helpful hint if the trainees are truly stuck. The RL agent makes these decisions based on the *state* of the exercise, which can be represented by features such as Blue team’s performance metrics (success/failure of recent actions, time taken to respond to an alert, etc.), the level of Red’s success (e.g. how far the malware spread, or how many people believe a false story), and even trainee workload or stress indicators if available. We design a reward function that encapsulates training effectiveness – encouraging scenarios that push participants to their learning edge without overwhelming them. This concept aligns with the theory of **dynamic difficulty adjustment (DDA)** from the gaming domain: *“automatic real-time adjustment of scenarios, parameters, and behaviors… to follow the player’s skill and keep them from boredom (too easy) or frustration (too difficult)”* ([](https://arxiv.org/pdf/2308.12726#:~:text=range%20of%20fields%2C%20including%20e,avoid%20boredom%20and%20frustration%2C%20which)). In essence, our RL-driven adaptation seeks to implement DDA in a serious game/training context, keeping the exercise flow in the optimal zone (often referred to as the “flow channel” between anxiety and boredom ([](https://arxiv.org/pdf/2308.12726#:~:text=Based%20on%20Csikszentmihalyi%E2%80%99s%20flow%20theory,improving%20user%20experience%2C%20engagement%2C%20and))). By doing so, we aim to maximize engagement, immersion, and skill uptake; research shows that such adaptive difficulty can significantly improve learning outcomes in training games ([](https://arxiv.org/pdf/2308.12726#:~:text=Paraschos%E2%80%99s%20and%20Koulouriotis%E2%80%99s%20review%20paper,their%20assignments%20are%20slightly%20more)).
-
-Technically, the RL agent can be trained in simulation (offline, before the exercise) using repeated runs of the scenario. Because our environment (ABM + LLM) is largely software-based, we can run hundreds or thousands of simulation episodes with varied trainee models to let the agent learn effective strategies. We will likely employ a deep reinforcement learning approach (e.g. Deep Q-Network or policy gradient methods) given the complexity of the state and action space. Even with partial information, the agent can learn heuristics – for example, if Blue has neutralized the first phishing attack very quickly in past runs, it might learn that introducing a second-layer attack (like a ransomware attempt) yields more learning opportunities; if Blue is floundering, the agent learns to slow down the pace to avoid a total breakdown of the scenario. During an actual training run, the RL policy (which can also incorporate some rule-based safety overrides set by the exercise designers) will execute in real time, observing the simulation state and applying appropriate adaptations. Importantly, this does not remove the human control element – the White cell can always supersede or guide the AI director (and we will design the system such that the White cell UI shows what the RL agent is doing and allows approval or veto of major adjustments if desired). 
-
-By introducing RL-based adaptation, each exercise play-through becomes a tailored experience. If participants excel, the simulation organically becomes harder and presents new twists (maintaining challenge); if they struggle, the simulation gives them a chance to recover and learn by ensuring critical cues aren’t all missed at once. This adaptivity also means scenarios need not be aborted or reset when trainee actions diverge from expectations – the system simply adapts to the new situation in a plausible way. In Phase I, we will implement a simplified version of this adaptive logic to prove the concept. For instance, we might define a basic reward for the RL agent such as “ensure Blue’s success probability stays around 50%” and allow it to choose one of a few discrete injects in a test scenario, then train it in simulation. Even a rudimentary demonstration like adjusting the timing of a second cyber attack wave based on Blue’s real-time performance will illustrate the power of this approach. Ultimately, this RL-driven dynamic scripting fulfills the SBIR’s vision of an exercise environment that can be updated rapidly (even *within* an exercise run) and improved over time. In effect, the more the system is used (and trained), the smarter its scenario control becomes. It learns what narrative branches or surprise events best elicit the desired reactions from trainees, continuously **improving the scenario efficacy through AI**. This is a novel application of reinforcement learning in the training domain – turning the wargame into a two-sided adaptive experience rather than a fixed scenario. The end result is a training platform that keeps participants in that ideal *“zone of proximal development”* where they are challenged just beyond their current skill level ([](https://arxiv.org/pdf/2308.12726#:~:text=DDA%20in%20improving%20user%20experience%2C,12)), which is known to be optimal for learning.
+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  
-To engage participants and orchestrate the human element of the simulation, we will develop a suite of **gamified user interfaces** tailored to the Red team (adversary role), Blue team (defender/trainee role), and White cell (control/evaluator role). These interfaces are crucial in translating the complex simulation data into an accessible, interactive experience for humans. They also provide the means for participants to make decisions and take actions in the virtual scenario in a manner that feels authentic and immersive.
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-**Blue Team Interface:** The Blue team UI is designed for the trainees who are defending against cognitive/information attacks. It will present them with a rich, multi-modal picture of the scenario as it unfolds. For example, the interface may resemble a combination of an intelligence analyst’s dashboard and a social media monitoring tool. A **feed view** will display incoming information in real time – such as social media posts (generated by the LLM engine) that Blue should notice, news bulletins, system alerts from network monitoring (if a cyber component is active), and communications from other team members or stakeholders. Rather than being briefed by an instructor, the Blue team will *discover* events through this feed, as in real operations. They might see a suspicious trending hashtag, then a report of increased phishing emails, then a system alarm for unusual traffic – all hinting at a developing campaign. Alongside the feed, the Blue UI will provide interactive tools to respond. These could include: a console to perform investigative actions (e.g. query a database of user profiles, scan a server for malware indicators), a communication module to issue responses or commands (e.g. drafting a public affairs message to counter a false narrative, or instructing a cyber unit to block an IP address), and a decision log or menu for taking higher-level actions (for instance, invoking an incident response plan or requesting help from a higher authority). We will incorporate **game design elements** such as clear objectives, timers, and feedback on actions. For example, if the scenario goal is to prevent a cyber attack by identifying it early, the Blue interface might have a progress indicator of the attack preparation; effective Blue actions slow or stop the progress (which they can visualize), whereas missed clues let it advance. The interface can score Blue’s decisions (e.g. correctly flagging a disinformation post gains points, missing it loses points), providing immediate feedback and a bit of competitive incentive. By structuring the Blue experience with these gamified elements, we keep trainees invested in the exercise as an interactive challenge rather than a passive drill.
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-**Red Team Interface:** The Red team UI is for those assuming the adversary role. In many training exercises, dedicated red cell players (or controllers) actively simulate the enemy. Our platform supports this by giving Red players a console to conduct their information operations within the simulation. The Red interface could be thought of as the “attacker’s dashboard.” It would present Red with tools to initiate maneuvers like launching a misinformation campaign, deploying a cyber exploit, or amplifying certain messages. For instance, Red might have a menu of tactics (aligned with the framework of social-cyber maneuvers) to choose from: they could select “Spread Rumor X on Platform Y” and target it at a certain community, which would then cue the simulation engine to generate that event. They might have an interface to compose a fake news article or doctored image (with assistance from the LLM for plausible text). If the Red role is filled by AI (which it partially will, via the simulation engine), this interface still exists for the Red *virtual* agents – essentially it is how the simulation executes Red tactics. But if a human is controlling Red (for example, an instructor or an adversary role-player in a more competitive exercise), the interface ensures they operate under the same conditions as a real adversary (with limited information and specific capabilities). They may see a simplified view of what effects their actions have (e.g. a gauge of how much they have influenced public opinion or how far their malware has spread). By gamifying Red’s interface, we allow human red teamers to compete against Blue in a structured way – essentially turning the exercise into an interactive game where Red tries to achieve their objectives (e.g. cause confusion, successfully carry out the attack) and Blue tries to thwart them. Notably, the system can accommodate a **hybrid Red** approach: AI-driven adversaries handle routine actions and background behavior, while a human red teamer can jump in for creative or high-level moves. The UI supports this by letting the human trigger or customize AI actions on the fly.
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-**White Cell Interface:** The White cell (or control cell) are the referees, facilitators, and evaluators of the exercise. Their interface is perhaps the most feature-rich, as it must provide situational awareness of the entire simulation and tools to intervene or guide as needed. The White cell UI will include a **scenario overview dashboard** showing key indicators of the exercise state: timelines of major events, statuses of Red and Blue objectives, maps or network diagrams if applicable (for instance, a network diagram might show systems that are under attack, and a social network diagram might show communities and influence levels). Visualization is a core aspect – recalling that **“the capability to explain [and] visualize”** the scenario is highly desired ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=author%20and%20manage%20these%20exercises,desired%20in%20the%20final%20product)). We plan to incorporate visualization of the information environment, such as graphs of sentiment over time in the population, or a color-coded map of which regions have been impacted by propaganda. The White cell can use these visual aids to quickly assess how well Blue is doing and where the story is headed. Additionally, the White interface provides **adjudication tools**. For example, if Blue takes an action that requires an outcome decision (say, they try an unplanned tactic like contacting a social media company to remove a piece of content), the White cell can adjudicate the result through their UI – perhaps selecting an outcome (“content removed after 1 hour delay”) which the simulation will then honor. The interface also allows White to inject events manually if needed. While the RL-driven adaptation and the Red agents will handle most injects, the White cell might still want to introduce a custom curveball or insert a teaching point. The UI might have a library of injects that can be triggered on demand (e.g. “introduce unrelated real-world news event to distract players”), or even a text input to send a direct message to a participant (in character, if White chooses to role-play as a certain actor). Essentially, the White cell UI is the control panel for the exercise’s behind-the-scenes director – it can monitor everything and shape the scenario as a safety net or for instructional purposes.
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-All three interfaces are **gamified and user-friendly**. By gamification, we mean they incorporate elements like scoring, progression, and perhaps even narrative rewards (e.g. a debrief screen that shows how the scenario progressed based on player actions). However, we avoid an overly “arcade” feel; the styling will be professional and relevant to military users, just with modern UX principles to keep it engaging. The use of these role-based interfaces moves the exercise away from the dry, scripted nature of a typical tabletop. It becomes a live, competitive simulation exercise – Red vs Blue with White overseeing – similar to a multiplayer simulation game but grounded in real-world information warfare dynamics. Notably, the platform can accommodate multiple participants on each side: for instance, a Blue team of several individuals can each have the Blue UI and perhaps take on different responsibilities (one focusing on cyber defense, another on public affairs, etc.), collaborating through the system. The cloud-based nature of the platform means these participants can be distributed geographically and still partake in the same virtual exercise. 
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-Finally, the interfaces serve as the conduit for data capture and **after-action review (AAR)** as well. They will log all actions taken by participants, which can later be replayed or analyzed by the White cell to provide feedback. For example, the White UI could have an AAR mode that shows a replay timeline with annotations of what happened when, allowing instructors and trainees to walk through the scenario afterward and discuss decision points. This further enhances the training value by connecting the immersive experience with reflective learning. In Phase I, we will develop prototype UIs for at least the Blue and White roles (as those are most critical to demonstrate). For instance, a simple web-based Blue dashboard showing a feed of LLM-generated “tweets” and a set of action buttons (monitor, respond, ignore) can already illustrate the concept. A basic White panel to pause/resume the simulation or inject an event will also be built. These prototypes will be refined with user feedback (from subject matter experts or test users) to ensure the design effectively balances realism with playability. By Phase II, we anticipate a polished interface set that makes participating in the exercise intuitive and engaging, lowering the barrier for adoption across units.
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-## Crowdsourcing Actors with Amazon Mechanical Turk  
-In addition to AI-driven agents and dedicated participants, our platform introduces an innovative use of **crowdsourcing** to enhance scenario realism: employing Amazon Mechanical Turk (MTurk) workers as on-demand human actors in the simulation. The idea is to leverage the power of the crowd to simulate the broad and diverse behavior of populations in the information environment – particularly the *Grey* space (neutral or undecided individuals, background noise, bystanders) – and to even bolster Red or Blue teams when needed with human ingenuity.
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-**Grey Actors as Neutral Noise:** Cognitive warfare scenarios often involve a large population of neutral parties who can be influenced one way or another. For example, social media is full of ordinary users who might unwittingly amplify a false story or express confusion during a cyber crisis. Simulating each of these “background” individuals with full AI fidelity is computationally expensive and can sometimes result in repetitive or stale behavior. Instead, we can task crowd workers to play the role of some of these neutral personas in a limited capacity. Using MTurk, we would create Human Intelligence Tasks (HITs) that ask workers to, say, **“Imagine you are a regular person on social media who just saw [a specific post]. Write a comment expressing your thoughts.”** The simulation can supply each worker with a brief, anonymized context (the content of the fake post or news they are reacting to, and perhaps a one-line persona description like “you are a 45-year-old teacher”). Dozens of crowd responses can be gathered within minutes, which the system then injects as a flurry of unique, human-generated comments. This creates a burst of organic-seeming noise: some workers might agree with the post, others might doubt it, some could ask questions or spread it further. The **cost-effective nature of MTurk** – coordinating supply and demand of human intelligence tasks rapidly and cheaply ([Running experiments on Amazon Mechanical Turk | Cambridge Core](https://www.cambridge.org/core/journals/judgment-and-decision-making/article/running-experiments-on-amazon-mechanical-turk/BBD787F3B4DDB61119CBB215927CA39E#:~:text=Core%20www,require%20human%20intelligence%20to%20complete)) ([Mechanical Turk - an overview | ScienceDirect Topics](https://www.sciencedirect.com/topics/computer-science/mechanical-turk#:~:text=Mechanical%20Turk%20,effective%20and%20efficient%20manner)) – makes it feasible to gather a crowd of Grey voices for each major inject. These Grey actors are *“neutral noise”* in that they are not orchestrated by Red or Blue, but their reactions can nonetheless shape the trajectory of the scenario (just as real public opinion does). Blue will have to sift through these responses to gauge the impact of the adversary’s narrative and adjust their strategy (for instance, if many people start panicking due to a rumor, Blue knows the rumor is taking hold and must react). The diversity of real human input ensures that the exercise avoids the predictable patterns that pure AI simulation might produce. It injects true unpredictability and realism – participants know some of those social media comments were written by actual people, adding weight to the exercise. 
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-**Crowdsourced Red and Blue Augmentation:** While Grey roles are a natural fit for crowdsourcing, we can also use MTurk to support Red and Blue roles in certain contexts. For the Red team, we could crowdsource ideas or content to broaden the playbook of adversarial behavior. For example, a MTurk task might be: *“As an adversary, what misleading message might you spread to exploit news of a cyberattack?”* The collected answers can populate a repository of disinformation ideas that the Red AI or human controllers can draw from, reflecting perspectives the designers may not have thought of. In real time, one could even have MTurk workers vote on or craft variations of a Red narrative to simulate how an adversary might A/B test their messaging on a real population. For Blue team, crowdsourcing is less likely to be used during an actual training (since Blue are typically the trainees themselves), but it could be used in the development phase to anticipate Blue responses. Alternatively, MTurk could furnish additional friendly personas in an exercise – for instance, simulate the perspectives of allied agencies or the local populace that Blue might consult. A MTurk worker might be assigned to role-play a local official who Blue can query for information during the scenario, adding an interactive human element. 
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-One particularly valuable use of crowd input is **during scenario design and validation**. In Phase I, as we build our initial scenario, we will leverage MTurk to validate the plausibility of our content. We can present workers with snippets of LLM-generated narrative (without telling them it’s AI) and ask if it seems like something real users would say or do. Their feedback helps us refine prompts or choose the best outputs to ensure realism. Furthermore, crowd responses become part of our hybrid dataset of social-cyber maneuvers ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20develop%3A,tools%20and%20decision%20aids%20to)) – they are real data indicative of how people might react, which can be used to train or fine-tune the LLM (creating a virtuous cycle between human data and AI generation). 
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-Integrating MTurk into a live simulation in Phase II will require careful orchestration. We will need to ensure tasks are launched at the right time and that responses are filtered for appropriateness (to avoid any truly off-base or toxic content making it into the exercise). Our system architecture accounts for this by having a **Crowd Integration Module** that interfaces with the MTurk API. This module can automatically post tasks when certain events occur in the simulation (e.g., when Red’s big propaganda drop happens, trigger a Grey reaction task). It will collect responses over a short window and subject them to an automated moderation filter (and/or a quick White cell review) before injecting into the scenario. Any content that violates exercise rules or goes off-topic can be discarded. The remaining crowd-generated content is then labeled as coming from various simulated user accounts in the exercise world and delivered to Blue’s feed. Because tasks are micro in scope and require no specialized knowledge (everyone knows how to react to a piece of news in their own way), the crowd should be able to contribute meaningfully with minimal briefing. 
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-From a cost and practicality standpoint, using MTurk for training exercises is novel but feasible. Each crowd inject might cost only a few tens of dollars given typical MTurk reward rates, which is trivial in the context of a large exercise and far cheaper than recruiting dozens of role-players or programming countless AI personas. Moreover, it **democratizes the creation of scenario content** – harnessing a wide range of perspectives from the global internet population ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Therefore%2C%20rather%20than%20directly%20relying,toward%20divergent%20viewpoints%20and%20debate)). There is evidence that such synthetic-yet-human data can mirror real subpopulation responses ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Therefore%2C%20rather%20than%20directly%20relying,toward%20divergent%20viewpoints%20and%20debate)), which is exactly what we need in cognitive warfare scenarios. By Phase II, we will have tested this concept in smaller scales (e.g. using 10–20 workers to simulate Grey noise in Phase I trials) and will develop procedures to reliably scale it up (perhaps hundreds of workers for larger exercises). An important note is that all scenario context given to MTurk will be unclassified and fictional, framed as a simulation exercise, so there are no security issues with involving public crowd workers. In fact, this approach could also double as a public resilience tool (workers themselves might learn about disinformation tactics by participating!). 
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-In summary, the MTurk integration brings a **human-in-the-loop** element that complements our AI agents. Red and Blue teams get the benefit of human creativity and unpredictability, while Grey background noise gets the authenticity of real human reactions. This blend of **“artificial artificial intelligence”** (the original Mechanical Turk concept) with actual AI models creates a rich tapestry of interactions in the simulation. Our platform will be one of the first training systems to use crowdsourcing in this manner, potentially setting a precedent for more dynamic, crowd-involved exercises. It ensures that our cognitive warfare simulation is not happening in a vacuum, but echoes the *live nature of the information environment* – where countless real people are continuously shaping the narrative.
+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  
-Bringing together the components described above into a unified platform requires a robust, scalable, and secure architecture. We adopt a **cloud-native, modular architecture** to ensure each component can develop and function independently yet interoperate seamlessly via the cloud. Figure 1 illustrates the high-level design of the system (described conceptually below):
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-**Modular Microservices:** Each major functional element of the system is implemented as a microservice or a set of microservices. For example, the **Simulation Engine Service** encapsulates the hybrid ABM + LLM logic. Within this service, there might be sub-modules (an agent-based simulation module and a content generation module), but to other parts of the system it behaves as one service: it accepts scenario configurations and agent actions, advances the scenario, and emits events/content. The **RL Adaptive Controller** is another service that subscribes to simulation state updates and decides on adaptation actions, feeding those back into the simulation. The **User Interface services** (Red UI, Blue UI, White UI) will likely be web-based frontends that communicate with the back end via APIs or web sockets. Each UI is backed by a service that handles that role’s logic – e.g. a Blue Service that queries the simulation for Blue-relevant data and sends user actions (decisions) back to the simulation. Likewise, a **Crowd Integration Service** manages communication with Mechanical Turk’s external API, handling the posting of tasks and retrieval of results, then passing processed results into the simulation as events. By modularizing in this way, we ensure that improvements or changes in one component (say, swapping out the LLM model for a new one) do not ripple undesirably into other parts of the system, as long as the interface contracts (APIs/messages) remain consistent.
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-**Cloud Infrastructure and Scalability:** The entire system will be deployed on a cloud platform (e.g. AWS GovCloud or commercial AWS for development) using containerization (Docker containers orchestrated by Kubernetes or similar). Each microservice runs in its own container, enabling horizontal scaling where needed. For instance, if multiple training sessions are running in parallel, we can spin up multiple instances of the simulation service, or if the LLM content generation is heavy, we can scale that component separately (perhaps even using serverless functions for bursty generation tasks). Cloud deployment also eases integration with AWS services such as MTurk. Being cloud-native means the system can leverage managed services for certain tasks: we might use a managed database service for storing scenario data and logs, and a pub/sub messaging service (like AWS SNS/SQS or Kafka) for event communication between modules. The simulation will likely produce a stream of events (like “Agent A posted message X at time t”) which needs to be broadcast to relevant UIs and possibly the RL agent. A publish-subscribe pattern suits this: the Simulation Engine publishes events to topics (e.g. “Blue_feed” topic for things Blue should see, “global_log” topic for all events, “RL_feedback” topic for the RL agent with condensed state), and subscribers (UI services, RL service) receive them in real time. This decoupling via messaging makes the system robust to delays or failures – if a UI disconnects, the simulation can continue and the UI can catch up from the event log when reconnected.
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-**Data Management:** A core data repository will store scenario definitions, agent profiles, and the content library. This includes both *static data* (like pre-defined scenario elements, templates, the framework ontology of maneuvers) and *dynamic data* (runtime logs, user decisions, outcomes). We will design a schema that connects to the information maneuver framework mentioned in the SBIR (akin to MITRE ATT&CK for social-cyber) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=these%20hybrid%20maneuvers%20to%20provide,tools%20and%20decision%20aids%20to)). Each scenario can be represented as a sequence or network of events mapped to that framework. This not only helps in authoring (by providing a structured way to build scenarios), but also in explaining and visualizing the scenario during and after execution – since events can be labeled by type (e.g. “Phishing Attack” or “Propaganda Boost”) and linked to objectives. The White cell UI will leverage this to show an **explainable timeline** of the exercise, fulfilling the “capability to explain” requirement ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=author%20and%20manage%20these%20exercises,desired%20in%20the%20final%20product)). Data management also entails storing all generated synthetic data (social media content, etc.) which can be massive. We will use cloud storage solutions and potentially compression or filtering (for example, we don’t need to store every single neutral tweet from every run, just a representative sample or those relevant to outcomes). All data will be tagged by scenario run and version to support later analysis and improvement (machine learning on this data can reveal patterns of trainee behavior or scenario balance, guiding refinements).
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-**Integration of AI Components:** The LLM will likely be deployed as a separate scalable service – possibly using a model serving framework (such as HuggingFace Transformers or TensorFlow Serving) with GPU support in the cloud for performance. This **LLM Service** will receive prompt requests from the Simulation Engine and return generated text. We will implement caching for the LLM outputs when appropriate (to reuse previously generated content for repeated scenarios or multiple trainees facing the same inject, if uniqueness is not crucial, thereby saving computation). The **RL agent** might be co-located with the simulation or separate; if separate, it will interface through the messaging system or a control API. Training of the RL agent is done offline, but during execution the trained policy can run very fast (a lightweight model that observes state variables and outputs an action). Ensuring the RL decisions are taken at sensible intervals and with White cell oversight will be part of the integration: e.g. the RL service might propose an adaptation and send it to the White UI for a quick approve/decline, unless it’s been pre-approved for automatic execution (which could be a setting depending on exercise autonomy desired).
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-**Security and Access Control:** As a multi-user system with potentially sensitive scenarios, we will build in user authentication, role-based access, and data isolation per exercise. Each exercise session will have its own instance or namespace in the cloud deployment to avoid crosstalk. Communication channels will be encrypted. If deploying on government networks, the architecture can be containerized and delivered to a secure cloud or on-premises servers as needed (cloud-native design does not mean it must always run on public cloud – it simply uses cloud technologies that can be mirrored in private clusters). The modular design also facilitates code security reviews and testing of each component in isolation.
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-**Extensibility:** The modular architecture is inherently extensible. New modules can be added in Phase II and beyond – for example, if we want to integrate a virtual reality component (for more immersive visualization for trainees) or a detailed network traffic simulation (for a deeper cyber-physical element), these could be additional services that plug into the core via the pub/sub system. The scenario framework could be extended to multi-domain (cognitive warfare combined with kinetic operations), by connecting our information simulator with a physical wargame simulator through an API. Our choice of open standards and common protocols (likely RESTful APIs, WebSockets, and possibly data formats like JSON or ProtoBuf for messages) ensures interoperability. This also means third-party tools could be integrated: e.g. if the Navy has an existing analytics dashboard, it could subscribe to our event stream; or an external AI system (say a specialized deepfake video generator) could be triggered by our simulation when needed.
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-In summary, the system architecture is designed to be **flexible, scalable, and maintainable**. Cloud-native microservices allow us to rapidly iterate on individual components during Phase I development. They also enable the **“produce realistic scenarios in under 1 month”** and **“scenario updates in 24 hours”** goals of the topic ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20be,launched%20during%20the%20exercise%20itself)) by making the software easy to update (a single service can be redeployed with improvements without overhauling the whole system). If a new information warfare tactic emerges in the real world, we could update the simulation module for that behavior and deploy it quickly. Or if a better language model becomes available, we swap it in and instantly the content quality improves. This agility is only possible with a loosely coupled design. Our integration testing will ensure that despite the loose coupling, the end-to-end behavior meets the requirements: the data flows from the simulation to the UIs must be timely (<1 second latency for real-time feel), the RL agent’s interventions must synchronize correctly (not causing race conditions with human injects), and the Mechanical Turk responses must enter the system in a controlled way. We will use simulation logs extensively to verify that each component’s input/output is as expected. By the end of Phase I, we plan to have a working integrated prototype of this architecture on a cloud testbed, demonstrating that all pieces – simulation, AI, UI, crowd – can work in concert.
+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  
-The proposed Phase I effort focuses on establishing the core feasibility of this approach and delivering foundational components and demonstration results. The work in Phase I will be structured into several tasks aligned with the SBIR Phase I guidelines ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=PHASE%20I%3A%20Collect%20and%20validate,Prepare%20a%20Phase%20II%20plan)), culminating in a proof-of-concept simulation platform and a roadmap for Phase II.
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-**Task 1: Use Case Selection and Data Collection.** We will begin by selecting a concrete use case scenario of hybrid cyber and cognitive warfare to model in detail. The SBIR topic provides an example of a **Distributed Denial of Service (DDoS) attack facilitated by social media** ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=For%20example%2C%20a%20Distributed%20Denial,attackers%3B%20the%20distribution%20of)), which is an excellent candidate. We will refine this scenario with input from Navy stakeholders or subject matter experts – outlining the sequence of adversary actions (e.g. Stage 1: adversary uses social media to rally supporters and anger them against the target; Stage 2: recruitment of volunteer “hackers”; Stage 3: distribution of attack tools via online channels; Stage 4: coordinated timing of the DDoS strike; Stage 5: the attack and its aftermath in media). For our chosen use case, we will **collect and/or generate relevant data**. This includes scraping open-source social media data or news related to similar real incidents (if unclassified examples exist) to understand realistic language and indicators. We will also consult existing knowledge bases like MITRE ATT&CK for the cyber aspects (to identify what technical signs a DDoS has, etc.) and any available databases of misinformation campaigns for the cognitive side. The goal is to have a **“collection of related hybrid cyber and social-cyber data indicative of these hybrid maneuvers”** ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20develop%3A,tools%20and%20decision%20aids%20to)) as a ground truth reference and as training data for our AI components. Part of this task may involve using MTurk in a preliminary way – e.g. asking crowd workers to produce example social media posts given a hypothetical scenario prompt, to enrich our dataset of adversary and public reactions. We will validate any collected data for realism and relevance, effectively building a small *library of scenario content*. 
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-**Task 2: Initial Framework and Modeling Approach.** In parallel with data collection, we will formalize the **framework for information maneuvers** that will underpin our simulation ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=these%20hybrid%20maneuvers%20to%20provide,tools%20and%20decision%20aids%20to)). This means defining the ontology of events (both cyber and information events) and relationships/stages for our scenario. For the DDoS use case, we outline stages such as “Preparation – Call to Arms”, “Coordination – Tool Distribution”, “Attack Execution”, etc., and map these to specific agent behaviors and expected Red/Blue actions. This framework will draw from known models (like the cyber kill chain, extended with social elements). We will document the *red flags* or indicators at each stage that Blue should be trained to catch ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=material%20indicative%20of%20an%20impending,Prepare%20a%20Phase%20II%20plan)) – for example, a spike in new social media accounts advocating an action could be a red flag for upcoming recruitment. Establishing this framework in Phase I guides the design of both our ABM rules and our evaluation metrics for Blue performance. Essentially it’s a mini-doctrine or schema for the scenario that ensures the simulation we build is *valid and logically sound*. 
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-**Task 3: Prototype Hybrid Simulation Engine Development.** Here we implement the core of our hybrid engine for the chosen use case. This involves coding a basic agent-based model representing the key actors (e.g. adversary influencer agents, follower agents, Blue defender agent or sensors, etc.) and integrating an LLM for content generation. We will likely start with a smaller pre-trained language model (for example, a 6B-parameter range model that can run on available hardware) fine-tuned on a corpus of social media and cybersecurity text to give it the appropriate style and vocabulary. Even in Phase I, we aim to demonstrate that the LLM can produce **“synthetic material indicative of an impending cyber-attack”** as described by the topic ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=a%20particular%20use%20case%20or,attack%20has%20several%20stages)). Concretely, we’ll test prompts such as “Write a series of tweets from a hacktivist leader trying to convince others to target [the exercise target]” and see that the model outputs believable content. The ABM and LLM will be connected such that the ABM’s state triggers LLM calls – we will likely hard-code a few trigger points in the scenario (e.g. at Stage 1, generate social posts; Stage 3, generate a phishing email content; Stage 5, generate a news report on the attack). This prototype engine will then be executed to simulate the full scenario timeline. We expect to produce a demonstration where, for example, we can show a console log or simple UI of events: “Day 1: 1000 tweets appear, rumor about Navy base – here are samples [LLM outputs]… Day 3: A pastebin link with attack instructions circulates [LLM output]… Day 5:  coordinated attack traffic detected (simulation event)… Day 5: News media report website down [LLM output]”. We will verify that the chain of events is consistent with our framework and data (this checks the **feasibility of the simulation model** to bring together the data and framework into realistic scenarios ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=guide%20the%20development%20of%20social,attacks))). If time permits, we will incorporate preliminary RL adaptation in the prototype – perhaps in a simplistic way like a rule-based logic that mimics what an RL would do (since fully training an RL in Phase I might be ambitious). For instance, we could add a toggle that if Blue catches the early warning, the Red will switch tactics (to simulate adaptive behavior). Even a manual trigger can demonstrate the concept of branching scenario.
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-**Task 4: User Interface Mockup and Minimal Implementation.** We will create a basic front-end for the Blue role (and possibly a minimal White control panel) to illustrate how users would interact with the simulation. This can be a web application that connects to the running simulation engine to display content. For Phase I demo purposes, the UI can be simplistic – e.g. a refreshable feed that shows the latest messages (with timestamps and sender tags), and a couple of buttons for Blue to indicate actions (like “flag this post as false” or “deploy counter-narrative”). We will have the UI actions feed back into the simulation logic (even if that simply updates a log or slightly alters the scenario outcome). The White interface in Phase I could be as simple as a command-line or admin panel to start/pause the simulation and view a summary of state (since building the full White GUI is more of a Phase II activity). The primary purpose in Phase I is to demonstrate end-to-end flow: a human sees the scenario through the UI, takes an action, and the simulation responds. This also allows us to involve a small number of evaluators (perhaps our own team members acting as test players, or friendly users) to carry out a trial run of the prototype and give feedback on usability and realism.
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-**Task 5: Integration of Mechanical Turk in Prototype (feasibility test).** As a stretch goal in Phase I, we will conduct a limited test of the MTurk integration. For example, during a prototype run, we might simulate the “call to arms” stage and actually post a MTurk task asking workers to respond with what they would do or say after seeing the adversary’s call. We can compare those responses with our LLM-generated ones to see if the crowd adds new dimensions. If it’s not feasible to do live integration at this stage, we will at least design the interface (the API calls and task format) and possibly perform an offline MTurk experiment to gather sample data for use in Phase II. The aim is to ensure we understand the process and have resolved any basic issues (like how to encode context for workers succinctly, how fast responses come in, etc.). Success criteria would be that we obtain useful, on-topic contributions from crowd workers that can be fed into the scenario. This will validate the concept of crowdsourced Grey noise and set the stage for a larger role in Phase II.
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-**Task 6: Demonstration and Phase I Deliverables Preparation.** We will assemble all the components into a coherent Phase I demonstration. This involves packaging the prototype system (likely on a single cloud VM or a small cluster) and walking through the use case scenario from start to finish with one or more human in the loop participants. We will demonstrate specific SBIR-relevant capabilities, such as: the generation of synthetic scenario data (showing our collected real data vs. AI-generated data for comparison), the ability to update scenario content quickly (perhaps by tweaking a prompt or swapping in new data and rerunning to show a different story, thereby highlighting the rapid update potential), and the partial dynamic adaptation (manually or via simple AI). We will document the results, including any performance metrics (e.g. how many realistic messages were generated, how accurate were players in reacting, etc.), to show technical feasibility. The deliverables from Phase I will include:
-- **Initial Data Set and Framework:** A documented set of hybrid social-cyber data for the use case, and the defined framework (stages, maneuvers, indicators) that informed our model ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=material%20indicative%20of%20an%20impending,Prepare%20a%20Phase%20II%20plan)).
-- **Prototype Simulation Software:** The code for the ABM+LLM engine and any supporting scripts, along with a demonstration user interface. This serves as a proof-of-concept tool that the Navy can run to see the concept in action (likely in an unclassified environment).
-- **Feasibility Study Results:** A report on the performance of the LLM in generating content, the behavior of the simulation, and the outcome of any test runs (including feedback from any evaluators). We will specifically note how this approach meets or exceeds the realism of tabletop methods, and identify any gaps to address in Phase II.
-- **Phase II Development Plan:** A detailed plan for building out the full system in Phase II, informed by our Phase I lessons. This will cover how we will enlarge the use case set (perhaps adding a second scenario to ensure generality) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Enlarge%20the%20use%20cases%20from,authoring%20tools%20to%20assist%20exercise)), how we will improve the data synthesis capability (possibly training a custom LLM as suggested in the topic) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Develop%20a%20catalog%20of%20use,live%2C%20virtual%20constructive%20exercise%20for)), the design of the scenario authoring tools for exercise planners ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=synthesis%20capability%20,for%20validation%20of%20the%20system)), and the approach to integrate everything into a working prototype for a full exercise demonstration by the end of Phase II.
-
-Throughout Phase I, we will pay attention to technical risks such as: LLM hallucination or inconsistency (mitigated by careful prompt design and possibly fine-tuning with our scenario data), complexity of RL integration (we keep it simple in Phase I, deferring full training to Phase II after we have more data), and user interface complexity (we will focus on core functions first). By the conclusion of Phase I, we expect to **establish the feasibility of the key innovative aspects** – namely that an AI-driven simulation can generate realistic multi-modal scenarios of a cognitive attack, and that real-time adaptation and crowd integration are achievable enhancements. This will give the Navy evaluators confidence that the concept warrants Phase II investment. In fact, as part of the deliverables, we anticipate providing a short demo to Navy stakeholders (perhaps remotely via the cloud deployment) so they can witness a scenario play out with our system. This hands-on experience often speaks louder than reports, in showing that our modular cognitive warfare simulator can indeed revolutionize training.
+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 take the validated Phase I foundation and expand it into a comprehensive, deployable training platform that meets all the objectives of topic N252-110 at scale. Phase II will focus on enhancing capability, robustness, and usability, delivering a full system ready for real-world exercises.
-
-**Scaling to Multiple Use Cases:** While Phase I focused on a single scenario, Phase II will **“enlarge the use cases”** to cover a wider range of cognitive warfare scenarios ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Enlarge%20the%20use%20cases%20from,authoring%20tools%20to%20assist%20exercise)). We will develop a *catalog of scenarios* encompassing different types of information warfare challenges. For example, additional use cases might include: an election interference scenario (where Red spreads disinformation to influence an election outcome), an insider threat scenario amplified by social media rumors, or a crisis response scenario with competing narratives (e.g. after a cyber-induced infrastructure failure, Red tries to sow panic while Blue seeks to reassure the public). For each new scenario, we will collect relevant data and extend the framework so that it **“broadly encompass cyber and social-cyber maneuvers”** across various contexts ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=these%20hybrid%20maneuvers%20to%20provide,tools%20and%20decision%20aids%20to)). The result will be a **catalog of use cases and related information** that provides exercise planners with options and building blocks for training ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Develop%20a%20catalog%20of%20use,live%2C%20virtual%20constructive%20exercise%20for)). These scenarios will be stored in the system and selectable via the planner’s interface.
-
-**Advanced Data Synthesis and Specialized LLMs:** Phase II will significantly enhance the data generation component. We plan to develop or integrate a **“special use large language model”** tailored for information warfare simulation ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Develop%20a%20catalog%20of%20use,live%2C%20virtual%20constructive%20exercise%20for)). This could involve training a custom LLM on a corpus of military-specific and adversarial narrative data, possibly including data we generated or collected in Phase I. The advantage of a specialized model (or fine-tuned model) is greater control and authenticity in outputs – it can learn jargon, cultural references, and context relevant to Navy scenarios, reducing the chance of irrelevant or implausible text. If needed, we will explore **reinforcement learning from human feedback (RLHF)** using our accumulated dataset of crowd responses and expert judgments to fine-tune the LLM’s behavior (so it stays within desired bounds and produces content that is effective for training). By mid-Phase II, the platform should be capable of producing **“realistic volumes of synthetic data for information warfare exercises”** on demand ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Develop%20a%20catalog%20of%20use,live%2C%20virtual%20constructive%20exercise%20for)). This means if a planner needs a new variant of a scenario, the AI can quickly spin up fresh narrative content – achieving the rapid scenario generation goal (update in 24 hours or less). We will also incorporate multi-modal outputs beyond text if beneficial: for instance, using image generation (DALL·E/Midjourney style) to create fake screenshots or profiles that add realism, or generating simple video clips (Phase II might not fully implement video deepfakes, but we can include placeholders or external tools if available). The data synthesis pipeline will be validated and possibly **“augmented… to validate synthetic data”** against real patterns ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=Enlarge%20the%20use%20cases%20from,authoring%20tools%20to%20assist%20exercise)) (for example, ensuring our synthetic social network metrics align with what’s seen in organic social media behavior).
-
-**Fully Realizing RL Adaptive Scenarios:** In Phase II, the reinforcement learning scenario adaptation will be fully implemented and rigorously tested. We will train RL agents for each scenario or even a generalized meta-RL agent capable of adapting across scenarios. Leveraging the multiple scenarios in our catalog, we can train the adaptation agent to handle a variety of conditions (e.g. one policy that knows how to adjust a narrative campaign or a cyber attack timeline for optimal difficulty). We will also integrate the RL logic with the user-facing system in a more transparent way – possibly giving the White cell a “dial” for how adaptive or challenging to make the scenario, which the RL then uses as guidance (almost like a difficulty setting that the RL interprets). We’ll conduct user studies or pilot tests with actual military personnel (if possible) to fine-tune the adaptation behavior, ensuring it indeed improves training effectiveness. The expectation is that by end of Phase II, the scenario adaptation is proven to produce better outcomes (measured via metrics like higher knowledge retention, more consistent detection of indicators, etc., compared to a static scenario). Essentially, the RL should function as an **intelligent assistant to the exercise planner**, doing on-the-fly scenario adjustment so that planners don’t have to script out every branch in advance – fulfilling the goal of making exercises *rapidly updatable and responsive* to trainees.
-
-**Development of Authoring Tools for Planners (White Cell Tools):** A major focus will be creating a user-friendly **scenario authoring and planning interface** for exercise designers (likely White cell or support staff). This interface will allow users to construct or modify scenarios using the underlying framework without needing to code. We envision a tool where the planner can define the narrative arc by selecting from a library of possible events or using a timeline editor, and the system will fill in details using the AI engine. The SBIR explicitly calls for **“authoring tools and decision aids to guide the development of social-media facilitated cyber-attacks”** ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=cyber%20and%20social,attacks)). In response, our authoring tool might offer suggestions (powered by the LLM) for what social precursor to add given a chosen cyber attack, or highlight if a scenario is missing certain counter-actions. For example, if a planner drags a “malware attack” into the scenario, the tool could prompt: “Do you want to include a phishing email phase as a precursor? It’s commonly how malware is delivered.” These decision aids would be informed by our framework (like MITRE-style matrices of tactics). The planner can simulate-run partial scenarios right in the tool to see how they play out, adjusting parameters via a GUI instead of editing config files. By lowering the expertise needed to create scenarios, we make it feasible for the Navy to **produce realistic scenarios in under 1 month** and update them in a day or two by tweaking variables ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20be,launched%20during%20the%20exercise%20itself)), as per the requirement. 
-
-Additionally, the authoring environment will integrate **adjudication logic customization** – allowing planners to set rules for how to score Blue actions or what conditions trigger an automatic success/failure. This ensures that the tool is not just creating storylines but also encoding the training objectives and evaluation criteria, which the system will use during execution (and for after-action reports). Essentially, by Phase II we deliver a *scenario IDE (Integrated Development Environment)* for cognitive warfare exercises.
-
-**Enhanced User Interface and Multi-User Support:** We will take the Phase I UI prototypes and expand them into polished applications. The Blue and Red interfaces will be refined through iterative design, likely with feedback from trial exercises. We’ll aim for cross-platform accessibility (so participants can use standard web browsers, which simplifies deployment). Features such as chat functionalities (for Blue team coordination or for White cell to inject messages in character), mapping tools (if geography is relevant to the narrative), and notification systems will be added. For the White cell, beyond the real-time dashboard, we will implement comprehensive **logging and after-action review tools**. This includes the capability to generate an after-action report automatically at the end of each session, with a timeline of events and flags where, for instance, Blue missed an indicator or where an inject was adapted by the RL – effectively explaining how the scenario responded to their actions. Visualization of complex interactions (like propagation of a rumor through a network graph over time) can be included to debrief participants on what actually happened in the info-space during the exercise. Multi-user support will be fully enabled, meaning we can have a team of, say, 5 Blue users and 2 Red users concurrently in the exercise, each with their own login and seeing possibly slightly different perspectives (based on their role or viewpoint in the scenario). We will also integrate voice or video communication channels if needed (some exercises might involve live role-play communication in addition to the simulated content; while not a core requirement, our platform can facilitate that by providing a channel for participants to talk, which can be recorded for AAR).
-
-**Extensive Testing and Refinement:** A large portion of Phase II will be testing the system in progressively more realistic settings. We will run internal trials of full scenarios, then invite representatives from the Navy or training experts to evaluate. Based on feedback, we’ll refine content (ensuring, for example, that military terminology is correctly used by the LLM, or that the difficulty feels appropriate). We’ll also conduct stress tests: can the system handle a high volume of events and messages? does the cloud infrastructure scale to many simultaneous participants? is latency low enough for smooth interactions? These are important for transitioning to actual use. We aim to demonstrate the platform in a **live, virtual constructive (LVC) exercise environment** by the end of Phase II ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=using%20the%20framework%20and%20catalog,for%20validation%20of%20the%20system)). This could mean integrating with an exercise that also has live components (perhaps linking our cognitive warfare scenario with a live cyber range event or a command post exercise). If possible, we’ll coordinate with a Navy training event and run our simulation as part of it, validating the system’s effectiveness with real users in an operational context. Their performance and feedback will be collected as a final measure of success.
-
-**Preparation for Phase III Transition:** As Phase II concludes, we will focus on documenting and packaging the system for deployment. We’ll ensure the modular components are well-documented for Navy IT personnel, and that the system meets security and compatibility requirements for Navy networks (including any Authority to Operate considerations if needed). The architecture’s modular nature means parts of it have dual-use potential. For example, cybersecurity companies (as mentioned in the topic) might use our simulation engine and data to train their analysts ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=PHASE%20III%20DUAL%20USE%20APPLICATIONS%3A,purpose%20of%20training%20cybersecurity%20professionals)). We will explore commercial spin-off opportunities such as a SaaS platform for corporate cyber awareness training that uses our cognitive simulation to run drills (where employees must discern phishing attempts amid social media noise, etc.). These plans underscore the adaptability of the system beyond just Navy use, increasing its sustainability.
-
-In summary, Phase II will deliver a **fully functional Cognitive Warfare Simulation Platform** with: a library of scenarios, powerful AI-driven content and adaptation, intuitive interfaces for all roles, and tools for both conducting and designing exercises. This platform will be demonstrated in relevant environments and readied for adoption. By project’s end, the Navy will possess a cutting-edge training capability that can be continuously updated and expanded as cognitive warfare threats evolve. Our approach inherently allows the system to stay current – new data can train the models further, new tactics can be added to the framework, and new scenarios can be authored swiftly by the trainers themselves. This positions the Navy at the forefront of training for the information age, where cognition and information are the new strategic high ground. We expect that Phase II’s outcome will not only satisfy the SBIR requirements but exceed them by providing a flexible system that can integrate with larger training ecosystems (for example, linking with other wargame simulators or intelligence exercise tools). It effectively operationalizes the vision of a rapid, realistic, and adaptive cognitive warfare exercise capability.
+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  
-In conclusion, we propose to develop a cloud-native, modular cognitive warfare simulation platform that transforms how information warfare training is conducted. By integrating an agent-based simulation of social and cyber behaviors with the generative power of large language models, we can create rich, believable multi-modal scenarios that engage trainees in *“train as you fight”* experiences ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=attacks,fight%E2%80%9D%20%E2%80%93%20to%20experience%20the)). The addition of reinforcement learning-driven real-time adaptation means each exercise can intelligently respond to participants, keeping them in the optimal learning zone and enabling rapid scenario adjustments on the fly ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=could%20be%20changed%2C%20with%20the,launched%20during%20the%20exercise%20itself)) ([](https://arxiv.org/pdf/2308.12726#:~:text=range%20of%20fields%2C%20including%20e,avoid%20boredom%20and%20frustration%2C%20which)). Our emphasis on gamified, role-specific user interfaces ensures that Red, Blue, and White cell participants are fully immersed and empowered with the tools they need to act and adjudicate, supported by clear visualizations and explanatory aids ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=author%20and%20manage%20these%20exercises,desired%20in%20the%20final%20product)). Moreover, by harnessing Amazon Mechanical Turk for crowdsourced actors, we inject a novel source of human realism and variability into the simulation, cost-effectively simulating the behavior of the masses in the information environment ([Mechanical Turk - an overview | ScienceDirect Topics](https://www.sciencedirect.com/topics/computer-science/mechanical-turk#:~:text=Mechanical%20Turk%20,effective%20and%20efficient%20manner)). 
-
-The synergy of these components results in a cohesive system squarely aligned with the Navy’s SBIR objectives: a platform to generate **“realistic, validated augmented”** scenario data combining cyber and social dimensions ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20develop%3A,tools%20and%20decision%20aids%20to)), a framework encompassing the full spectrum of information maneuvers, and tools to help planners build and manage exercises with unprecedented speed and flexibility ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=cyber%20and%20social,attacks)) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20be,launched%20during%20the%20exercise%20itself)). Technically, our approach is grounded in state-of-the-art research and practices – from NATO’s early explorations in cognitive warfare simulation ([CW-SINON:
-
-Cognitive Warfare Simulation, artificial Intelligence & Neural 
-
-networks for modeling human behaviors in Operations, 
-
-population and social Networks](https://www.liophant.org/projects/cw-sinon.html#:~:text=CW,1%20evaluates%20impacts)) to cutting-edge uses of LLMs in wargaming that **automate qualitative scenarios** and broaden participant roles ([Open-Ended Wargames with Large Language Models](https://arxiv.org/html/2404.11446v1#:~:text=real,%E2%80%9D)) ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Therefore%2C%20rather%20than%20directly%20relying,toward%20divergent%20viewpoints%20and%20debate)). We leverage these advancements while introducing original innovations (like crowd integration and adaptive scenario control) that push the envelope of training technology. 
-
-During Phase I, we will demonstrate feasibility through a focused prototype that simulates a social-media-facilitated cyberattack, producing authentic narrative injects and allowing a trainee to interact with the unfolding events. This prototype, along with our data collection and framework development, will serve as a proof-of-concept that convinces the technical evaluation panel of the viability of our solution. We will show that our hybrid AI engine can generate indicators and signals that a defender must parse – something not possible with static “white card” methods – and that our system architecture can meet performance and integration requirements. Any challenges (such as ensuring LLM outputs stay on message, or balancing realism with control in adaptation) will be addressed with clear mitigation strategies and reflected in our Phase II plan.
-
-Looking ahead to Phase II, we have a clear pathway to scale up and generalize the platform, delivering a polished product for Navy use. The resulting system will allow the Navy to **rapidly create, modify, and execute cognitive warfare exercises** that keep pace with the evolving tactics of adversaries in the information domain. It will cultivate more resilient and cognitively aware warfighters, who have *experienced* the fog and friction of the information battlefield in simulation before they ever face it in reality. Beyond military applications, this technology has dual-use potential for cybersecurity training, intelligence analysis drills, and even public safety exercises (preparing responses to influence campaigns targeting civilian populations). 
-
-By embracing modern cloud software design and AI-driven content generation, our approach drastically reduces the cost and labor of scenario design while **increasing the richness of training**. It aligns with the Navy’s priorities in advanced computing, cyber, and training modernization ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=MODERNIZATION%20PRIORITIES%3A%20Advanced%20Computing%20and,Sustainment%20%26%20Logistics)). Ultimately, this project will yield a platform that can be continuously updated (with new data, new AI improvements, new scenarios) to remain on the cutting edge, much as adversaries continuously adapt their cognitive warfare techniques. We are confident that the proposed system will meet and exceed the Navy’s requirements for SBIR N252-110, providing a capability that is not only technically innovative but also practical and directly impactful on training effectiveness. We look forward to the opportunity to develop this platform and help the Navy pioneer the next generation of training for cognitive warfare – a critical need in safeguarding our forces and institutions against the ever-growing threat of information and influence attacks. 
-
-**Sources:**
-
-1. Bruzzone, Agostino G., et al. *“CW-SINON: Cognitive Warfare Simulation for Modeling Human Behaviors.”* NATO ACT R&D Project, 2023. – *Describes a NATO-commissioned cognitive warfare simulator using human behavior models to reproduce the impact of cognitive attacks and hybrid warfare* ([CW-SINON:
-
-Cognitive Warfare Simulation, artificial Intelligence & Neural 
-
-networks for modeling human behaviors in Operations, 
-
-population and social Networks](https://www.liophant.org/projects/cw-sinon.html#:~:text=CW,1%20evaluates%20impacts)).  
-2. Navy SBIR Topic N252-110. *“Modeling and Simulation for Multi-modal Exercises.”* 2024. – *SBIR topic description outlining the objective of simulating cyber-attacks with social media precursors for training, including requirements for data, framework, tools, and a simulation model* ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=attacks,fight%E2%80%9D%20%E2%80%93%20to%20experience%20the)) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=hybrid%20attacks,collection%20of%20related%20hybrid%20cyber)) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20develop%3A,tools%20and%20decision%20aids%20to)) ([topic_N252-110_Modeling and Simulation for Multi-modal Exercises.PDF](file://file-VJN3xRLUT4w595qtdwnC49#:~:text=The%20desired%20deliverable%20would%20be,launched%20during%20the%20exercise%20itself)).  
-3. Csikszentmihalyi, M. *Flow: The Psychology of Optimal Experience.* 1990. (cited in Rahimi et al.) – *Conceptual foundation for balancing challenge and skill in training; basis for dynamic difficulty adjustment to avoid boredom or frustration* ([](https://arxiv.org/pdf/2308.12726#:~:text=range%20of%20fields%2C%20including%20e,avoid%20boredom%20and%20frustration%2C%20which)) ([](https://arxiv.org/pdf/2308.12726#:~:text=Paraschos%E2%80%99s%20and%20Koulouriotis%E2%80%99s%20review%20paper,their%20assignments%20are%20slightly%20more)).  
-4. Rahimi, M., et al. *“Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game.”* arXiv:2308.12726, 2023. – *Demonstrates that reinforcement learning can auto-adjust game difficulty in real time to match player skill, improving engagement and learning outcomes* ([](https://arxiv.org/pdf/2308.12726#:~:text=range%20of%20fields%2C%20including%20e,avoid%20boredom%20and%20frustration%2C%20which)) ([](https://arxiv.org/pdf/2308.12726#:~:text=Paraschos%E2%80%99s%20and%20Koulouriotis%E2%80%99s%20review%20paper,their%20assignments%20are%20slightly%20more)).  
-5. Hicks, Matthew & Carley, Kathleen. *“BEND Battle: An Agent Based Simulation of Social-Cyber Maneuvers.”* SBP-BRiMS Conference, 2023. – *Introduces an agent-based model that simulates two sides conducting information maneuvers (BEND framework) on social media and examines their effects* ([](https://sbp-brims.org/2023/papers/working-papers/2023_SBP-BRiMS_FinalPDF_18%20(1).pdf#:~:text=Abstract,Results%20suggest%20that%20explain%20and)) ([](https://sbp-brims.org/2023/papers/working-papers/2023_SBP-BRiMS_FinalPDF_18%20(1).pdf#:~:text=BEND%20provides%20a%20framework%20for,authors%20and%20should%20not%20be)).  
-6. Hogan, Daniel P., & Brennen, Andrea. *“Open-Ended Wargames with Large Language Models.”* arXiv:2404.11446, 2024. – *Proposes an LLM-driven system (“Snow Globe”) for playing out text-based wargames, showing that LLMs enable automation of qualitative, narrative-rich scenarios previously requiring human input* ([Open-Ended Wargames with Large Language Models](https://arxiv.org/html/2404.11446v1#:~:text=real,%E2%80%9D)).  
-7. Saif, Farhad (CSIS). *“It Is Time to Democratize Wargaming Using Generative AI.”* Center for Strategic & International Studies, Sept 2023. – *Argues for using generative AI in wargaming to reduce costs and broaden participation; notes that AI can create synthetic players and multiple scenario variations cheaply* ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Therefore%2C%20rather%20than%20directly%20relying,toward%20divergent%20viewpoints%20and%20debate)) ([It Is Time to Democratize Wargaming Using Generative AI](https://www.csis.org/analysis/it-time-democratize-wargaming-using-generative-ai#:~:text=Using%20AI%2C%20game%20designers%20can,traditional%20wargame%2C%20the%20analyst%20can)).  
-8. Masakowski, Y., et al. *“Mitigating and Responding to Cognitive Warfare.”* NATO STO Technical Report HFM-ET-356, 2023. – *Explains the concept of cognitive warfare (CogWar) and its characteristics, highlighting how it exploits human cognition and the challenges it poses* ([Mitigating and Responding to Cognitive Warfare. | National Technical Reports Library - NTIS](https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/AD1200226.xhtml#:~:text=as%20availability%20and%20access%20to,ICT%29%2C%20neuroscience)).  
-9. Mechanical Turk Overview – ScienceDirect Topics. – *Describes Amazon Mechanical Turk as an online platform enabling cost-effective recruitment of human participants for tasks and experiments* ([Mechanical Turk - an overview | ScienceDirect Topics](https://www.sciencedirect.com/topics/computer-science/mechanical-turk#:~:text=Mechanical%20Turk%20,effective%20and%20efficient%20manner)).  
-10. Navy SBIR Reference: *Marine Corps Doctrinal Publication 8 – Information*, 2022. – *Marine Corps doctrine emphasizing the information environment in operations (indicative of the importance of training for information/cognitive warfare).*
-
+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.