A Role-Based Cognitive Framework for Strategic Foresight in Multi-Agent Systems

An introductory paper

This post introduces MAS-MIRROR™, a novel cognitive framework that enhances strategic foresight by simulating how diverse actors interpret and respond to ambiguous future events. The framework combines a Multi-Agent System (MAS) architecture with a structured reasoning approach called MIRROR (Memory-Intent-Reflection-Role Response) to capture the cognitive processes that influence decision-making during complex scenarios. Through a case study on disinformation cascades, we demonstrate how MAS-MIRROR reveals institutional friction points, misaligned incentives, and trust erosion dynamics that traditional scenario planning approaches might overlook. Our findings suggest that role-based agent modeling offers a promising approach to strengthen strategic planning by exposing hidden vulnerabilities in organizational response networks. This work contributes to the growing field of agent-based foresight by providing a structured methodology for simulating cognitive biases and institutional constraints in strategic decision environments.

1. Introduction

As organizations increasingly face complex, ambiguous threats in contested information environments, traditional scenario planning approaches often prove insufficient. These methods typically focus on event sequences and probabilities while underestimating the impact of cognitive biases, institutional constraints, and misaligned incentives that affect real-world responses. This gap becomes particularly critical in contexts where diverse stakeholders must coordinate responses under conditions of uncertainty.

The integration of advanced artificial intelligence techniques, particularly large language models (LLMs), has enabled more sophisticated approaches to agent-based modeling (Zhang et al., 2024; Tran et al., 2025). Recent frameworks have demonstrated the value of role-based specialization in multi-agent systems, where different agents take on specific functions within a collaborative environment (Moura, 2025; Dibia, 2025). However, these approaches often focus on task completion rather than cognitive modeling, limiting their utility for strategic foresight applications.

This paper introduces MAS-MIRROR, a novel framework designed to address this gap by simulating how different actors—including military planners, government officials, private organizations, and adversaries—perceive, misinterpret, and react to ambiguous future events. The framework combines a Multi-Agent System (MAS) architecture with a structured reasoning approach called MIRROR (Memory-Intent-Reflection-Role Response) to capture the cognitive processes that influence decision-making during complex scenarios.

The key contributions of this paper include:

1. A novel cognitive framework (MIRROR) that models how agents' past experiences, goals, biases, and role constraints influence their interpretation of ambiguous situations

2. An implementation architecture that integrates this framework within a multi-agent system to simulate interactions between diverse stakeholders

3. A case study demonstrating how the framework reveals critical coordination failures and trust vulnerabilities in disinformation response scenarios

4. A methodological approach for applying the framework in workshop settings to enhance real-world strategic planning

2. Related Work

2.1 Multi-Agent Systems and Role-Based Frameworks

Multi-agent systems have evolved significantly in recent years, particularly with the integration of advanced language models. According to Wang et al. (2024), LLM-based multi-agent systems are now being deployed across various domains, from enterprise applications to scientific research. These systems benefit from the specific roles assigned to each agent, allowing for specialized expertise and focused task execution.

The concept of role-based modeling in multi-agent systems has deep roots in both AI research and organizational theory. Barreteau and Bousquet (2001) pioneered early work combining multi-agent systems with role-playing games for natural resource management, demonstrating that role-playing could effectively explain the content of complex multi-agent systems to stakeholders.

Recent developments have built upon this foundation, with frameworks like CrewAI emphasizing role-based agent design for complex workflows (SuperAnnotate, 2025). As noted by Smythos (2024), CrewAI "excels in scenarios where clear task delegation and specialized agent roles are crucial," making it "particularly well-suited for complex, multi-step workflows that mimic human team dynamics."

2.2 Cognitive Modeling in Strategic Foresight

While role-based frameworks have proven valuable for task execution, cognitive modeling for strategic foresight presents unique challenges. Traditional strategic planning approaches often focus on generating plausible future scenarios without adequately modeling how different stakeholders might interpret and respond to these scenarios based on their cognitive biases and institutional constraints.

Xu et al. (2025) proposed a significant advancement in this area with their work on inner thought reasoning for role-playing language agents. Their MIRROR approach demonstrated how retrieving memories, predicting reactions, and synthesizing motivations could generate more realistic reasoning chains for complex characters. This approach showed particular promise for modeling decision-making processes in ambiguous situations.

The field of agent-based participatory simulations has also contributed valuable insights. Reitsma (2001) demonstrated how role-playing games could "open the black box" of multi-agent systems, making their internal reasoning processes more transparent and accessible to stakeholders. This approach proved particularly valuable for explaining complex models to non-technical audiences in natural resource management contexts.

2.3 LLM-Based Collaborative Systems

The emergence of sophisticated large language models has transformed the landscape of multi-agent systems. According to Tran et al. (2025), LLMs now play several crucial roles in multi-agent collaboration, including facilitating communication between agents, constructing and dynamically adjusting virtual environments, and enabling more natural interactions with human users.

Recent work by Dibia (2025) identified five key trends in multi-agent LLM systems throughout 2024, including the adoption of sophisticated patterns for handling complex tasks, the emergence of models designed specifically for multi-agent reasoning, and the development of benchmarks for evaluating agent performance on realistic tasks. These developments have laid the groundwork for more advanced cognitive modeling in strategic planning contexts.

3. The MAS-MIRROR Framework

3.1 Conceptual Overview

The MAS-MIRROR framework combines two complementary approaches: a Multi-Agent System (MAS) architecture that simulates diverse stakeholders as autonomous agents, and a cognitive modeling framework called MIRROR that captures how these agents process information through distinct reasoning pathways.

Multi-Agent System (MAS): The MAS component enables modeling complex interactions between stakeholders with competing incentives and divergent interpretations. Unlike traditional analytical approaches that view scenarios through a single institutional lens, MAS allows for the emergence of coordination challenges, misalignments, and unforeseen consequences that arise from the interaction of multiple autonomous agents.

MIRROR Framework: MIRROR provides a structured approach to modeling each agent's cognitive processes:

• Memory: What past events shape the agent's perception?

• Intent: What is the agent trying to achieve?

• Reflection: What bias or blind spot might affect interpretation?

• Role Response: What action does the agent take based on its reasoning?

This framework draws on insights from cognitive psychology and decision science to capture how historical experiences, institutional mandates, personal biases, and role-specific constraints influence an actor's interpretation of and response to new information.

3.2 System Components

The MAS-MIRROR system implements this conceptual architecture through several integrated components:

1. Role Cards: Physical or digital cards containing MIRROR prompts that guide participants through the reasoning process of specific agents

2. Scenario Trigger: Situation descriptions aligned to relevant topics (e.g., disinformation campaigns, infrastructure incidents)

3. AI Agent Simulation: LLM-based simulations that produce agent reasoning outputs based on role specifications and memory retrieval

4. Facilitated Dialogue: Structured discussions using MIRROR prompts to explore divergent interpretations

5. Emergent Mapping: Visualization tools to capture unexpected dynamics, misreads, and escalation vectors

3.3 Implementation Architecture

The implementation architecture of MAS-MIRROR adapts key innovations from recent multi-agent frameworks while introducing novel elements specific to cognitive modeling for strategic foresight.

Agent Representation: Each agent in the system is represented as a combination of:

• A role profile describing the agent's institutional position, responsibilities, and constraints

• A memory repository containing relevant past experiences and knowledge

• A reasoning engine that processes information according to the MIRROR framework

• A communication interface that enables interaction with other agents

Memory Retrieval System: Drawing on the approach proposed by Xu et al. (2025), the system implements a sophisticated memory retrieval mechanism that identifies relevant memories based on the current scenario. This mechanism allows agents to ground their reasoning in past experiences, institutional precedents, and domain-specific knowledge.

Theory of Mind Module: A key innovation in the architecture is the integration of a Theory of Mind module that enables agents to predict how other stakeholders might react to potential actions. This capability allows for more realistic modeling of strategic interactions in scenarios where anticipating others' responses is crucial for effective decision-making.

Reflection and Summarization: The system includes a reflection mechanism that helps agents filter irrelevant information and organize their thoughts into coherent reasoning chains. This process ensures that agent outputs represent plausible cognitive processes rather than mere information retrieval.

4. Case Study: Disinformation Cascade

To demonstrate the capabilities of the MAS-MIRROR framework, we present results from a simulation focused on an AI-powered disinformation campaign targeting a NATO partner state during an election period.

4.1 Scenario Design

The scenario involved a highly personalized AI-generated disinformation campaign targeting voters in a NATO partner state just weeks before a national election. The campaign delivered deepfakes, behavioral nudges, and tailored content suggesting an imminent NATO-backed military intervention. Attribution remained unclear, with some evidence pointing to a known adversary state while other signals implicated a Western private data analytics firm with opaque ownership structures.

This scenario was selected for its complexity, ambiguity, and the diversity of stakeholders involved in response efforts. The inherent uncertainty surrounding attribution, the time pressure imposed by the upcoming election, and the potential for trust erosion made it an ideal test case for the MAS-MIRROR framework.

4.2 Agent Configurations

The simulation modeled five key stakeholders responding to this scenario:

1. NATO Strategic Communications Lead

2. Partner Nation Civil Security Minister

3. AI Forensics Team Lead

4. Private Sector Disinformation Consultant

5. Adversary Strategic Influence Analyst

Each agent was configured with:

• A detailed role profile describing institutional position and constraints

• Relevant memory fragments drawn from historical incidents

• MIRROR reasoning templates to guide their cognitive processing

• Communication channels to other relevant agents

4.3 Simulation Results

The simulation revealed several critical dynamics that traditional scenario approaches might miss. Below, we present the detailed reasoning processes of each agent as captured through the MIRROR framework.

NATO Strategic Communications Lead
Memory: "We lost the narrative in 2022 by waiting for perfect attribution. Public trust eroded faster than we could react."
Intent: "The partner state expects clear support. Private firms will deny involvement. The adversary hopes we misstep."
Reflection: "Every hour we wait, conspiracy theories fill the void. But rushing to blame without proof risks losing our credibility."
Response: "A careful release of verified facts, without naming actors, might hold the line while we investigate."

Partner Nation Civil Security Minister
Memory: "Our last national crisis left the population doubting institutions. NATO's silence was interpreted as abandonment."
Intent: "The public wants reassurance. Political opponents will weaponize the information gap."
Reflection: "Waiting is no longer neutral. It reads like complicity."
Response: "I must act decisively—even if that means pre-empting NATO."

AI Forensics Team Lead
Memory: "In 2023, we pushed attribution at 75% confidence under pressure. The backlash damaged our credibility."
Intent: "NATO wants to speak. The partner government wants to move. The firm involved won't talk unless forced."
Reflection: "We're at 80% confidence. It's plausible, but not defendable under scrutiny."
Response: "My job isn't to guess—it's to know."

Private Sector Disinformation Consultant
Memory: "We were dragged into a false attribution in SolarWinds. It took months to recover reputation."
Intent: "NATO will pressure us. The public will misunderstand nuance."
Reflection: "Staying silent protects us—but also implicates us."
Response: "The only safe path is controlled transparency, tightly bounded and legally protected."

Adversary Strategic Influence Analyst
Memory: "Information chaos is most effective when attribution is blurred."
Intent: "NATO will fracture. Their allies will hesitate. The private sector will run scared."
Reflection: "We don't need deniability. We need confusion."
Response: "As long as they argue, we control the frame."

4.4 Key Insights

The simulation revealed several critical dynamics that traditional scenario approaches might miss:

1. No Unified Narrative
   Each actor framed the event differently: NATO saw a containment issue, the partner state saw a crisis, and the private sector saw liability.

2. Institutional Speed Mismatch
   The forensics team couldn't match the speed of public panic or political timelines—reality outpaced the evidence.

3. Strategic Silence as a Weapon
   Adversary success came not from clarity but from ambiguity; the disinformation worked because no one could act decisively.

4. Trust Became the Target
   The actual content of the disinformation mattered less than the alliance's inability to present a coherent response.

5. Perception Diverged from Fact
   The scenario illustrated how actors operate on perceived intent and institutional memory—facts alone don't drive action.

These insights demonstrate how MAS-MIRROR can reveal vulnerabilities in response frameworks that might remain hidden in traditional scenario planning approaches. By modeling not just what might happen, but how different stakeholders would perceive and respond to events based on their distinct cognitive processes, the framework provides a more nuanced understanding of potential coordination failures and trust erosion dynamics.

5. Implementation Methods

The MAS-MIRROR system can be implemented through several complementary methods:

5.1 Workshop Format

For interactive sessions, the system uses printed role cards with MIRROR prompts, providing participants with character backgrounds and reasoning frameworks. Participants assume roles and work through responses to the scenario trigger, guided by facilitators who capture emergent patterns.

This format has proven particularly effective for engaging decision-makers in strategic planning contexts, as it allows them to experience firsthand the cognitive and institutional constraints that might affect coordination during a crisis.

5.2 AI Simulation

For more extensive analysis, the system leverages LLM simulations to generate agent reasoning chains based on character profiles and scenario inputs. This approach enables rapid exploration of multiple agent perspectives and can be scaled to include dozens of stakeholder viewpoints.

We implemented this approach using GPT-4o, which demonstrated strong performance in generating plausible reasoning chains based on character profiles and memory repositories. The implementation leverages recent advances in role-playing agent frameworks (Xu et al., 2025) while introducing novel elements specific to strategic foresight applications.

5.3 Hybrid Implementation

The most effective implementation combines human and AI elements, with participants engaging in facilitated dialogue informed by AI-generated reasoning chains. This hybrid approach leverages both human creativity and the systematic consistency of AI simulation.

In this implementation, AI-generated reasoning chains serve as starting points for human discussion, allowing participants to explore variations, challenge assumptions, and identify potential interventions that might improve coordination or build resilience against adversarial exploitation.

6. Discussion

6.1 Theoretical Implications

The MAS-MIRROR framework makes several contributions to the theory of strategic foresight and multi-agent systems.

First, it provides a structured approach to modeling cognitive processes in strategic contexts, extending beyond the task-oriented focus of most multi-agent frameworks to capture the reasoning pathways that influence decision-making under uncertainty.

Second, it demonstrates the value of role-based modeling for strategic foresight, showing how institutional positions, past experiences, and role constraints shape stakeholders' interpretations of ambiguous situations.

Third, it highlights the importance of theory of mind in strategic modeling, illustrating how agents' ability (or inability) to accurately predict others' responses can significantly impact coordination outcomes.

6.2 Practical Applications

The MAS-MIRROR framework has several practical applications for strategic planning and crisis response:

Policy Development: By revealing potential coordination failures before they occur in real-world settings, the framework enables more resilient policy design that accounts for cognitive biases, institutional constraints, and adversarial exploitation of systemic vulnerabilities.

Training and Preparedness: The workshop implementation provides a valuable tool for training decision-makers to recognize and mitigate cognitive biases and institutional friction points that might emerge during a crisis.

Resilience Assessment: Organizations can use the framework to assess the resilience of their coordination mechanisms against adversarial exploitation, identifying potential vulnerabilities in trust networks and communication channels.

Scenario Enhancement: The framework can enhance traditional scenario planning by adding a cognitive dimension, helping planners understand not just what might happen but how different stakeholders would interpret and respond to events.

6.3 Limitations and Future Work

Despite its promising results, the MAS-MIRROR framework has several limitations that suggest directions for future research.

Validation Challenges: While the framework produces plausible reasoning chains that align with expert expectations, formal validation of its predictive accuracy remains challenging. Future work should explore methods for validating cognitive models against historical cases of coordination failure and success.

Scaling Complexity: The current implementation focuses on a limited number of key stakeholders. Future research should explore methods for scaling the approach to include dozens or hundreds of agents while maintaining interpretability and computational feasibility.

Integration with Existing Methods: Further work is needed to develop methodologies for integrating MAS-MIRROR with traditional scenario planning approaches, ensuring that insights from cognitive modeling inform broader strategic planning processes.

Enhanced Theory of Mind: While the current implementation includes basic theory of mind capabilities, future versions could benefit from more sophisticated models of how agents predict and respond to others' likely actions and reactions.

7. Conclusion

The MAS-MIRROR Foresight Engine represents a significant advancement in strategic foresight methodology, moving beyond linear event prediction to capture the complex cognitive and institutional dynamics that shape real-world responses to ambiguous situations. By simulating how different actors interpret, misread, and react to emerging events, the system reveals vulnerabilities in coordination systems that traditional approaches might miss.

The case study on disinformation cascades demonstrates the framework's ability to identify critical friction points in multi-stakeholder response networks, including narrative fragmentation, institutional time horizon mismatches, and trust erosion dynamics. These insights enable more resilient policy design and strengthen strategic planning across domains from cybersecurity to public health.

As organizations increasingly face complex, ambiguous threats in contested information environments, approaches like MAS-MIRROR that capture cognitive and institutional dimensions of response will become essential components of effective strategic foresight.

References

Barreteau, O., & Bousquet, F. (2001). Role-playing games for opening the black box of multi-agent systems: Method and lessons of its application to Senegal River Valley irrigated systems. Journal of Artificial Societies and Social Simulation, 4(2).

Dibia, V. (2025). AI Agents 2024 Rewind - A Year of Building and Learning. Retrieved from https://newsletter.victordibia.com/p/ai-agents-2024-rewind-a-year-of-building

Moura, J. (2025). Multi-agent LLMs in 2024 [+frameworks]. SuperAnnotate. Retrieved from https://www.superannotate.com/blog/multi-agent-llms

Reitsma, R. (2001). A laboratory experiment was conducted to investigate the effects of various kinds of access to a simulation model on water-resources negotiation outcomes and processes. ResearchGate.

Smythos. (2024). Multi-Agent Systems Frameworks: A Comprehensive Overview of Tools and Technologies. Retrieved from https://smythos.com/ai-agents/multi-agent-systems/multi-agent-systems-frameworks/

Tran, K.T., Dao, D., Nguyen, M.D., Pham, Q.V., O'Sullivan, B., & Nguyen, H.D. (2025). Multi-Agent Collaboration Mechanisms: A Survey of LLMs. arXiv preprint.

Wang, X., Xu, R., & Xiao, Y. (2024). Multi-Agent Systems Advancements 2024. Restackio.

Whiteley, J. (2025). Role-driven AI reasoning: What We Learned from Simulating Arctic Thought. Retrieved from https://www.foresightnavigator.com/p/role-driven-ai-reasoning

Xu, R., Wang, M., Wang, X., Lu, D., Tan, X., Chu, W., & Xu, Y. (2025). Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents. arXiv:2503.08193v1.

Zhang, S., et al. (2024). Best 5 Frameworks To Build Multi-Agent AI Applications. GetStream. Retrieved from https://getstream.io/blog/multiagent-ai-frameworks/

Photo by Patrick Perkins on Unsplash