Review of Advances in Multiple-Resolution Modeling for Distributed Simulation

Abstract

Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, particularly within distributed simulation environments such as military command and control systems. This paper provides a structured review and comparative analysis of prominent MRM methodologies, including multi-resolution entities (MRE), agent-based modeling (from a federation viewpoint), hybrid frameworks, and the novel MR mode, synchronizing resolution transitions with time advancement and interaction management. Each approach is evaluated across critical dimensions such as consistency, computational efficiency, flexibility, and integration with legacy systems. Emphasis is placed on the applicability of MRM in distributed military simulations, where it enables dynamic interplay between strategic-level planning and tactical-level execution, supporting real-time decision-making, mission rehearsal, and scenario-based training. The paper also explores emerging trends involving artificial intelligence (AI) and large language models (LLMs) as enablers for adaptive resolution management and automated model interoperability.

1. Introduction

Multiple-resolution modeling (MRM) is a vital framework in modern simulation, enabling the integration of models with varying levels of detail within a unified environment. Supporting multiple representations of the same phenomenon, ranging from computationally intensive, high-resolution models to abstract, low-resolution counterparts, accommodates diverse user requirements and operational needs [1]. This adaptability is especially critical in distributed simulations, where systems must operate seamlessly across domains and scales [2]. MRM’s scalability allows it to bridge the gap between strategic, aggregate-level overviews and granular, detail-focused scenarios, meeting the needs of stakeholders at all levels. This capability is particularly evident in domains like military operations, where commanders require high-level summaries, while field personnel depend on detailed analyses [3].

The rise in distributed simulation systems, such as those leveraging high-level architecture (HLA), underscores the importance of MRM. Distributed systems often involve independently developed simulations with inherent differences in data formats, communication protocols, and abstraction levels [4]. MRM enhances these systems by enabling dynamic resolution transitions, ensuring interoperability, and promoting composability. For instance, in military applications, MRM allows for the seamless integration of simulations at different scales, such as combining strategic-level scenarios with entity-level details, while optimizing computational resources. This dynamic capability supports real-time decision-making, mission rehearsal, and training scenarios, demonstrating the transformative potential of MRM in complex, distributed environments [5].

Military simulation is a compelling MRM application driven by economic, operational, and technological factors. Economically, MRM reduces costs by allowing existing high- and low-resolution models to interoperate, maximizing the utility of legacy systems and avoiding the expense of developing new models for every scenario. Operationally, MRM enables simulations to switch between aggregated and detailed views dynamically, a capability essential for tasks such as mission rehearsal and battlefield coordination. Technologically, MRM facilitates the integration of live, virtual, and constructive (LVC) simulations, creating unified, realistic environments where live systems interact seamlessly with virtual and algorithm-driven entities [6]. This integration supports advanced training scenarios, such as pilots in flight simulators interacting with live command centers and simulated adversaries, enhancing realism and operational readiness [7].

Despite its benefits, MRM faces challenges in achieving seamless integration and consistent performance. Interoperability remains a significant hurdle, as variations in communication protocols, software architectures, and data formats can hinder cohesive operation among simulations. Temporal mismatches and synchronization issues further complicate integration, particularly in distributed systems where resolution updates occur at different rates. Composability is another critical challenge, requiring the dynamic assembly of simulation components that align both technically and conceptually. Models with differing abstraction levels and underlying assumptions often require re-engineering to function cohesively, increasing system complexity and development costs. Finally, resolution consistency is a persistent concern, as aggregation and disaggregation processes can lead to data loss or semantic mismatches. Maintaining accuracy across different levels of detail demands sophisticated algorithms and middleware solutions, adding further complexity to system design [8].

MRM is a cornerstone of modern distributed simulation, offering unmatched flexibility and scalability for diverse applications. From its transformative role in military operations to its ability to optimize resource allocation and enhance decision-making, MRM exemplifies the potential of integrated modeling frameworks. However, addressing the challenges of interoperability, composability, and resolution consistency is essential to unlocking its full capabilities. Advances in standardization, dynamic resolution management, and middleware development will be pivotal in overcoming these hurdles and ensuring MRM’s continued evolution as a transformative technology in distributed simulation systems.

The remainder of this paper is organized as follows: Section 2 presents the literature review methodology. Section 3 describes the theoretical foundations of modeling and simulation (M&S) in military training and strategy, and MRM as a federated simulation approach. Section 4 presents methodologies for building MRM systems. Section 6 provides a comparison of different MRM approaches. Section 7 concludes the paper, including remarks on integrating artificial intelligence (AI) and large language models (LLMs) into MRM, and outlines future directions.

2. Methodology of the Literature Review

The literature review used Web of Science (WOS) database articles. The search strategy involved several queries employing keywords related to MRM, distributed simulation, and high-level architecture (HLA). All searches were performed using the Topic field in WOS, which includes terms appearing in the title, abstract, author keywords, and Keywords Plus^®. The following search queries were applied: (“Multi-Resolution Modeling” OR “Multiple Resolution Modeling” OR “Multiresolution Modeling”) AND “distributed simulation”; (“Multi-Resolution Modeling” OR “Multiple Resolution Modeling” OR “Multiresolution Modeling”) AND (“High Level Architecture” OR “HLA”); (“Multi-Resolution Modeling” OR “Multiple Resolution Modeling” OR “Multiresolution Modeling”) AND “simulation”. The articles retrieved through the final query span 1997 to 2025. The flowchart for the literature review of the 2020 PRISMA Statement [9] is presented in Figure 1.

To complement the database search and ensure broader coverage of the literature, an additional query was conducted using Google Scholar. The search string used was: (“Multi-Resolution Modeling” OR “Multiple Resolution Modeling” OR “Multiresolution Modeling”) AND “distributed simulation” AND (“High Level Architecture” OR “HLA”), which returned a total of 288 results. The results were screened with the same inclusion criteria to identify peer-reviewed and academically relevant publications. This supplementary search aimed to capture any relevant studies that may not be indexed in the Web of Science database.

A systematic review of 22 papers highlighted MRM’s role in creating models that represent the same phenomena at different resolutions, enabling both detailed mission planning (via high-resolution models) and a comprehensive battlefield overview (via low-resolution models) [10]. Key findings indicate that HLA has emerged as the preferred framework for MRM implementation, supporting a universally distributed simulation architecture for various applications, including training and analysis. While MRM is essential for resolving data discrepancies across systems with different resolutions, most practical implementations have focused on integrating virtual and constructive simulations.

Using agent-based models (as a component of federated systems) underscores the need for models that simulate realistic, dynamic behaviors and decision-making processes. Additionally, integrating diverse information sources, including empirical data, expert knowledge, and historical accounts, enhances model calibration and validation [11]. Emphasis is also placed on developing tools that support context-dependent aggregation and disaggregation to simplify high-resolution models and include explanation capabilities to enhance transparency and analytical rigor. These contributions advance the field by promoting more exploratory, interpretable, and context-sensitive modeling practices.

The introduction of quotient space-based MRM (QMRM), founded on granular computing and discrete event system specification (DEVS), provides key principles such as internal/external consistency and true-/false-preserving transformations, which ensure reliable model behavior across resolutions [12].

Regarding the application of formal specification methods, the multi-resolution translational DEVS (MRT-DEVS) embeds state and event translation functions, simplifying implementation and reducing execution costs while maintaining theoretical soundness [13].

Aggregation and disaggregation methods, along with dedicated resolution converters, address the challenge of resolution mismatches in distributed simulations, enabling seamless communication between models at different abstraction levels [14].

The design and implementation of MRM frameworks based on HLA standardize and accelerate the development of distributed multi-resolution models, supporting rapid integration and extension [3].

HLA enables the integration of multiple models at varying resolutions (high, medium, low) into a single simulation, allowing for dynamic switching between levels of detail as needed [15]. Regarding federation structures, each model (federate) joins an HLA federation, where HLA services manage communication, synchronization, and data exchange, supporting concurrent operation and real-time resolution changes [1].

HLA supports the aggregation (combining detailed entities into a higher-level group) and disaggregation (breaking down groups into detailed entities), which is crucial for efficient and accurate multi-resolution simulations [15].

MRM has gained prominence in this evolving landscape, supporting broad and detailed analytical perspectives. In [16], Davis introduces the concept of multi-resolution, multi-perspective modeling (MRMPM), advocating for model families that operate at different resolution levels and incorporate alternative viewpoints. This approach enables analysts to zoom in and out as necessary and to switch perspectives in line with stakeholder needs. Davis emphasizes that lower-resolution models should not be mere aggregations of detailed ones but should instead be designed for sense-making and narrative support.

The search results reveal a limited number of publications addressing the intersection of MRM, distributed simulation, and HLA. Despite the growing importance of these topics in simulation research, only a few studies have been published that directly explore their integration. This scarcity highlights a gap in the literature and suggests that further investigation in this area is necessary and timely.

3. Theoretical Foundations

The evolution of warfare has increasingly leaned on technological advancements to maintain a competitive edge. Success on the battlefield now hinges not only on skilled soldiers and sound tactics, but also on adapting to the complexities of modern, high-tech weaponry. However, the cost and sophistication of these advanced systems often make real-world training impractical. A single Tomahawk missile, for instance, costs USD 1.3 M, while an F-35 fighter jet comes with a price tag of roughly USD 100 M. In this context, military simulation has emerged as an indispensable tool for training, strategic planning, and operational analysis, offering a safer, cost-effective alternative to live exercises.

Military simulations replicate real-world scenarios through models and simulations that capture the dynamics of combat environments. They allow forces to test strategies, enhance skills, and evaluate equipment in a controlled, risk-free setting. These tools provide critical insights and foster preparedness across all levels of military operations by simulating complex operations, from tactical battlefield maneuvers to large-scale campaign strategies.

3.1. Modeling and Simulation (M&S): Military Training and Strategy

Modeling involves creating a simplified representation of real-world systems, balancing realism and simplicity for analytical and experimental purposes. Simulation goes a step further by enabling the evaluation of a system’s performance under varied conditions over time. Together, modeling and simulation (M&S) minimize risks, prevent resource bottlenecks, and optimize decision-making processes.

M&S plays a pivotal role in training soldiers, analyzing decisions, acquiring weapons, and conducting war games in the military domain. These systems fall into two broad categories as follows:

• Stand-Alone Simulations: Independent, purpose-built systems like flight simulators.
• Federated Simulations: Integrated systems that connect multiple simulators, enabling broader, collaborative applications.

Federated systems, particularly when paired with HLA, offer flexibility in integrating simulations with varying levels of detail, thereby enhancing their utility in complex, distributed environments [17,18].

Military simulations address diverse needs through live, virtual, and constructive (LVC) paradigms as follows [19]:

• Live Simulations: Real people operate real systems in actual environments, such as air combat training with real aircraft.
• Virtual Simulations: Real people interact with simulated systems, like pilots using flight simulators.
• Constructive Simulations: Simulated entities interact within a simulated environment, often used for large-scale strategic planning.

Each paradigm serves a unique purpose, from individual skill-building to comprehensive, multi-level operational analyses. For example, constructive simulations enable commanders to test strategies without risking personnel or assets, while virtual simulations provide immersive, task-specific training.

Decision-making in the military operates on three distinct levels as follows:

• Strategic Level: High-level planning to achieve long-term objectives, supported by campaign-level simulations.
• Operational Level: Resource coordination to meet strategic goals, aided by mission-specific simulators.
• Tactical Level: Ground-level execution of missions, often relying on detailed, real-time tactical simulations.

3.2. MRM: A Federated System Approach

MRM represents the cutting edge of military simulation, integrating simulations at different levels of resolution to meet diverse user needs. MRM enables seamless transitions between high-resolution, detail-rich models and low-resolution, aggregate-level overviews. This flexibility makes MRM invaluable for federated systems, where stakeholders such as commanders and soldiers require varying levels of detail to perform effectively.

Advantages of MRM:

• Economic Efficiency: MRM minimizes the need for costly new developments by integrating existing systems.
• Strategic Insights: Multi-resolution environments reveal new perspectives, improving strategies and designs.
• Computational Optimization: Dynamic resolution adjustments reduce computational demands while maintaining accuracy.

Challenges of MRM:

• Data Consistency: Ensuring aggregated and disaggregated data align without loss or distortion.
• Chain Disaggregation: Preventing cascading computational demands triggered by excessive disaggregation.
• Time Synchronization: Addressing timing mismatches between models operating at different update frequencies.

By addressing these challenges, MRM continues to enhance its role as a transformative framework in military simulation, bridging the gap between technological advancements and operational requirements. Its application ensures both cost-effectiveness and a profound improvement in readiness and strategic agility for modern military forces.

4. Methodologies for Building MRM Systems

MRM encompasses a diverse range of approaches, each tailored to address the challenges of integrating simulations across varying levels of resolution. As simulations grow increasingly complex, the need for methods that ensure consistency, flexibility, and scalability has become paramount. MRM approaches seek to bridge the gap between low-resolution aggregate models and high-resolution, detailed simulations, enabling dynamic transitions to meet the specific demands of different scenarios. Whether focusing on centralized control mechanisms, decentralized systems, or hybrid frameworks, these methodologies cater to unique operational requirements and constraints.

This section explores the prominent approaches to MRM, highlighting their core principles, advantages, and limitations. From the structured hierarchical frameworks of Integrated Hierarchical Variable Resolution (IHVR) to the flexibility of agent-based modeling (ABM) and the efficiency-focused hybrid methods, each approach presents a distinct solution to the challenges of multi-resolution integration. Additional methods, such as Regulation as Middleware and the module-based approach, address practical implementation challenges. At the same time, innovations like multi-resolution entities (MREs) and resolution converters focus on ensuring logical consistency and interoperability. Through a comparative lens, this section aims to provide a comprehensive understanding of how these approaches contribute to the evolving landscape of MRM.

4.1. Integrated Hierarchical Variable Resolution (IHVR)

IHVR is a structured approach in MRM that organizes variables into a hierarchical tree, with low-resolution variables at the top and high-resolution variables at the bottom. Each level of this hierarchy provides progressively more detailed information, allowing for aggregation at higher levels and disaggregation at lower levels. This structured organization enables simulations to dynamically transition between levels of resolution, accommodating the varying needs of users and scenarios. For example, in a military context, the speed of a regiment at the top level of the hierarchy can be derived by aggregating detailed speeds of individual soldiers, vehicles, or units at lower levels. This ability to relate variables across multiple resolutions makes IHVR a powerful tool for balancing computational efficiency with the need for detailed analysis.

However, while IHVR facilitates modular calibration and establishes clear procedural relationships, it faces challenges defining precise equations linking variables across hierarchical levels [20]. The complexity of real-world systems often results in interdependencies that are difficult to model mathematically, limiting the approach’s flexibility in certain scenarios. Despite these limitations, IHVR remains a valuable simulation framework that requires a balance between simplicity and realism.

A hierarchical variable tree (Figure 2) represents the structure of the IHVR approach, showcasing the relationship between aggregated and disaggregated variables. Figure 2 highlights how higher-level variables encapsulate broader, summarized information while lower-level ones provide granular details. For instance, a top-level variable representing the operational capability of a division might aggregate sub-level variables such as logistics readiness, personnel availability, and vehicle performance. The tree-like structure organizes these variables hierarchically, ensuring that transitions between levels are systematic and logical [20]. By visually emphasizing the progression from broad aggregates to detailed components, Figure 2 underscores the utility of IHVR in achieving computational efficiency while retaining the capacity for detailed, context-specific analysis.

4.2. Module-Based Approach

A module-based approach introduces a decentralized mechanism for managing aggregation and disaggregation in MRM [21]. In this approach, each federate within the simulation is equipped with its regulator module. These regulators are responsible for handling resolution transitions, ensuring that interactions between federates occur smoothly without disrupting the simulation’s logical coherence. While this approach decentralizes resolution management, offering flexibility, it also introduces several significant challenges that can hinder its scalability and efficiency [21].

• Network Flooding: Every disaggregation event initiated by a federate triggers interaction with all other federates in the simulation. This significantly increases network traffic, overloading the system and potentially causing delays or inefficiencies.
• Development Complexity: Custom regulators must be developed for each federate, tailored to their specific requirements. This increases the development time, debugging efforts, and overall complexity, especially in large-scale simulations.
• Scalability Issues: Adding new federates to the simulation requires creating and integrating bespoke regulators for each, making the system less adaptable to expansion and more resource-intensive over time.

Figure 3 illustrates the structure and functionality of this method. The diagram shows each federate with its regulator module, which communicates directly with other federates. For instance, if one federate requests a resolution change, the corresponding regulator manages the aggregation or disaggregation process and exchanges the necessary data with other federates. This modular design allows federates to operate semi-independently but requires continuous communication among regulators to maintain consistency.

Figure 3 also highlights the inherent complexity of this approach. Each regulator must handle interactions, manage data consistency, and synchronize updates with the rest of the simulation, resulting in a web of interconnected regulators. While the module-based approach provides localized control over resolution changes, the lack of a centralized regulatory mechanism exacerbates network load, system complexity, and scalability issues, limiting its utility for larger or more dynamic simulation environments.

4.3. Regulation as Middleware

Regulation as Middleware is an effective approach in MRM that centralizes the aggregation and disaggregation processes through a dedicated middleware layer [21]. This regulatory middleware acts as an intermediary between the simulation federates and the runtime infrastructure (RTI), managing interactions and ensuring consistent communication across different levels of resolution. By handling these processes independently of the federates, the middleware significantly reduces network congestion and streamlines the data flow, alleviating the computational load typically borne by the RTI. Middleware often leverages standardized interfaces, such as RTI Application Programming Interfaces (APIs), to efficiently synchronize data and ensure federated compatibility. Despite its advantages, this approach requires careful design and tight integration with federates, creating dependency challenges and necessitating additional development effort [22].

Figure 4 illustrates the structure and functionality of this method. The diagram highlights the middleware as a distinct regulatory layer between the federates and the RTI. This layer mediates interactions and routing requests for aggregation or disaggregation from one federate to another while ensuring these transitions occur seamlessly and consistently. For example, when a low-resolution federate sends a request for higher-resolution data, the middleware processes this request, retrieves the necessary information, and relays it back to the requesting federate. Similarly, the middleware manages updates from high-resolution federates, consolidating them into a format compatible with low-resolution models.

4.4. Regulator as Federate

Regulator as Federate is an approach in MRM where a standalone federate manages aggregation and disaggregation processes [21]. This regulator federate interacts directly with the main simulation federate and determines when and how transitions between high-resolution and low-resolution models should occur. By centralizing these responsibilities, the regulator ensures consistent and efficient resolution of changes within the simulation environment. Additionally, the regulator maintains a database of relevant algorithms, rules, and data required for aggregation and disaggregation, allowing it to dynamically adapt to changing simulation scenarios.

Figure 5 provides a conceptual illustration of this approach. It depicts the regulator federate as a central decision-making node within the MRM system. For example, the main federate (representing a low-resolution model, such as a command post simulation) communicates simulation data to the regulator. The regulator evaluates the incoming information and determines whether disaggregation is necessary. Suppose a high-resolution interaction is required, such as simulating the detailed behavior of a tank or an aircraft. In that case, the regulator activates the appropriate high-resolution federate (e.g., a tank simulation or flight simulator). Conversely, if disaggregation is not required, the regulator allows the simulation to continue using the low-resolution main federate, conserving computational resources.

The regulator also facilitates seamless integration by translating and relaying data between federates of different resolution levels. For instance, it ensures that high-resolution simulators receive the detailed parameters they need to function accurately while updating the main federate with aggregated data from these simulations. By acting as a bridge between federates, the regulator enhances the overall coherence and efficiency of the simulation, making it a key component in advanced MRM systems. This structured, modular design improves scalability and simplifies the integration of additional federates, enabling a more flexible and adaptive simulation environment.

4.5. Resolution Converter

Resolution converters play a critical role in addressing discrepancies in data format and time synchronization between high-resolution and low-resolution simulations. Acting as intermediaries, these converters translate, align, and standardize data to facilitate seamless communication across federations operating at different levels of resolution. By ensuring that data shared between simulations is consistent and compatible, resolution converters help maintain the logical coherence of the overall system [14]. They also manage temporal discrepancies, aligning time steps between simulations with varying update frequencies and mitigating potential synchronization issues.

While resolution converters offer flexibility in bridging gaps between heterogeneous simulation systems, their implementation requires significant customization tailored to the specific requirements of each simulation environment. This reliance on bespoke configurations limits their applicability as a universal solution, as the effort and complexity involved in developing converters for diverse systems can be substantial. Despite these challenges, resolution converters remain a valuable tool in MRM, particularly in scenarios where existing simulations must interoperate without substantial redesigns or where maintaining resolution-specific fidelity is critical to the simulation’s success.

4.6. Selective Viewing

Selective viewing is a powerful approach in MRM that ensures consistency by continuously running high-resolution models. This method allows users to dynamically “zoom” between different levels of detail, providing tailored views based on their needs. By maintaining high-resolution data throughout the simulation, selective viewing preserves accuracy and consistency across all interactions, ensuring that decision-makers can rely on detailed, up-to-date information when evaluating scenarios. This approach is particularly advantageous in command-and-control environments, where precise, real-time information can be critical. However, its reliance on maintaining high-resolution models always makes it resource-intensive, demanding substantial computational power. Additionally, selective viewing is less flexible for integrating disparate simulations, as it typically requires a cohesive system designed with consistent rules from the outset [23].

The MVC approach (Figure 6) offers a conceptual representation of selective viewing, drawing parallels to the model–view–controller (MVC) design pattern [24]. In this setup, the model represents the high-resolution data and logic that underpin the simulation. The view serves as the user interface, presenting different levels of detail based on the user’s requirements. At the same time, the controller acts as the intermediary, managing the interactions between the model and the view. This architecture facilitates seamless transitions between levels of resolution, allowing users to focus on specific areas of interest while maintaining a comprehensive understanding of the simulation environment [23]. Figure 6 illustrates how selective viewing relies on this structured separation of concerns to ensure users can access and interact with high-fidelity data consistently and intuitively. Despite its computational demands, the method’s ability to provide granular control and detailed insights makes it invaluable in high-resolution decision-making scenarios.

4.7. Aggregation and Disaggregation

Aggregation and disaggregation are cornerstone methods in MRM, enabling simulations to adapt resolution levels based on situational demands dynamically. Aggregation involves consolidating multiple high-resolution entities (HREs), such as individual vehicles or personnel, into a single low-resolution entity (LRE), like a battalion or unit. This process simplifies system representation, reduces computational requirements, and provides a broader operational overview. Conversely, disaggregation reverses this process by breaking down an LRE into its constituent HREs, allowing for detailed, fine-grained analysis when a higher resolution is necessary. These mechanisms are integral to multi-resolution environments, ensuring scalability and precision without compromising performance [25].

Modified approaches have been developed to address the computational challenges inherent in full aggregation and disaggregation. Partial disaggregation selectively transitions only a subset of entities to a higher resolution, focusing resources on critical areas of the simulation [26]. Pseudo-disaggregation, a further refinement, simulates the effect of disaggregation without fully generating all high-resolution entities, instead creating proxy data to approximate detailed interactions. These methods, along with subset disaggregation, where only essential elements of a unit are transitioned, strike a balance between maintaining simulation fidelity and minimizing computational overhead. By employing these tailored approaches, MRM systems achieve greater efficiency and flexibility, dynamically adapting to the evolving requirements of complex.

4.8. Multi-Resolution Entity (MRE)

Multi-resolution entities (MREs) provide a sophisticated mechanism for ensuring consistency and logical coherence across varying levels of resolution in simulations. Unlike traditional methods, MREs maintain a comprehensive set of attributes at all resolution levels, allowing them to interact seamlessly with high-resolution entities (HREs) and low-resolution entities (LREs). This holistic approach ensures that any changes or interactions at one level are immediately reflected across other levels, preserving the integrity of the simulation. The backbone of this functionality is the consistency enforcer within the MRE, a dynamic computational mechanism that calculates, updates, and synchronizes attributes in real-time. By managing data relationships and dependencies across resolutions, the consistency enforcer ensures that every interaction adheres to the logical structure and objectives of the simulation.

While this approach achieves unparalleled accuracy and consistency, it requires significant computational resources due to the need for constant attribute maintenance and real-time updates.

Figure 7 illustrates the conceptual architecture of an MRE, emphasizing its ability to maintain state information concurrently at multiple levels of resolution [27]. Figure 7 demonstrates how the MRE is a central repository, dynamically updating and reconciling attributes to ensure that both HREs and LREs operate with accurate and consistent data. On the other hand, Figure 8 provides a closer look at the enforcer’s functionality, showcasing its role in dynamically managing the effects of incoming interactions. The consistency enforcer maps the results of interactions at one resolution level to corresponding attributes at other levels, ensuring logical consistency across the simulation environment. This feature enables the MRE to address complex multi-resolution challenges effectively, making it a cornerstone for advanced simulation systems that demand high fidelity and coherence across diverse scenarios.

4.9. Hybrid Approach

The hybrid approach in MRM strategically combines the strengths of MRE and aggregation–disaggregation methods to enhance simulation efficiency and consistency. By maintaining a core set of essential attributes, the hybrid method focuses on the critical data points necessary for smooth interactions across multiple levels of resolution [27]. This core set acts as the foundational information to generate additional attributes on demand, enabling the system to adapt dynamically without excessive computational overhead. Such a mechanism ensures logical consistency across resolutions and minimizes the processing requirements typically associated with MRE’s exhaustive attribute maintenance. However, the effectiveness of this approach depends heavily on carefully selecting core attributes, which must adequately represent the system’s key elements to avoid inconsistencies or data loss during transitions.

Figure 9 illustrates the hybrid method’s ability to address the challenges of chain disaggregation—a phenomenon where disaggregated entities trigger further disaggregation in a cascading effect, overwhelming computational resources. The diagram demonstrates how maintaining a core set of attributes eliminates the need for full-scale attribute generation at every resolution change, thereby reducing the frequency and intensity of disaggregation processes. This streamlined approach alleviates computational strain and preserves the fidelity of interactions across varying levels of detail, making the hybrid method an efficient and reliable solution for complex simulation environments.

4.10. Agent-Based Modeling as a Component of Federated Systems

ABM is a robust and flexible approach that captures complex behaviors and dynamic interactions within a system. By representing entities as autonomous agents with distinct properties and decision-making capabilities, ABM allows for detailed modeling of real-world phenomena. Agents can interact with their environment and other agents, making decisions based on predefined rules or adaptive behaviors. This capability to represent nuanced interactions makes ABM highly effective for scenarios where individual or group behaviors significantly influence system dynamics. For example, in military simulations, ABM can represent soldiers, vehicles, and command units as independent agents, each responding to changes in battlefield conditions, enabling detailed and realistic analyses of operational strategies in federated systems.

One of the most significant advantages of ABM is its inherent ability to facilitate aggregation and disaggregation. Agents are designed to function at various levels of detail, allowing seamless transitions between high-resolution (individual entity) and low-resolution (aggregated unit) representations. This feature is particularly advantageous in MRM, as it reduces the complexity of aligning different levels of detail across simulations. However, ABM faces challenges when integrating with legacy systems, as traditional simulation models may not be designed to accommodate agents’ decentralized and autonomous nature. Despite these challenges, ABM’s flexibility, scalability, and adaptability to evolving simulation requirements make it a preferred choice for new simulation environments and complex, behavior-driven analyses.

4.11. MR Mode

MR mode (Multi-Resolution Time Advance Mode) is a novel methodology that addresses the challenges of integrating dynamic resolution switching with time advancement in distributed MRM. This approach is designed to enhance both the accuracy and efficiency of simulations that operate concurrently in high-resolution and low-resolution models. The key innovation of MR mode lies in its ability to synchronize resolution transitions with time management processes seamlessly, thereby maintaining logical coherence and computational efficiency across the simulation environment.

A distinctive feature of MR mode is its interaction table, which decouples interaction requests and responses. This table dynamically manages interactions, minimizing delays and ensuring smooth communication between federates. Additionally, the methodology introduces an Interactive Deviation Index (Ecum), a quantitative metric that monitors cumulative interaction errors to determine optimal moments for resolution transitions. By incorporating these features, MR mode ensures that resolution switching is context-sensitive and computationally optimized, reducing unnecessary transitions and preserving system resources.

MR mode’s capability to handle complex, high-random environments makes it suitable for advanced applications such as manufacturing, automotive testing, and military simulations. For instance, in a combat simulation, MR mode enables precise resolution adjustments as battlefield conditions change, ensuring both strategic overview and tactical detail are appropriately represented. MR mode exemplifies an adaptable and robust solution for modern MRM systems by balancing the computational cost and simulation fidelity trade-offs.

Figure 10 illustrates the dynamic mechanism of MR mode, where interactions between simulation entities are managed through an interaction table. Figure 10 illustrates the pivotal role of the deviation index (Ecum) in determining when resolution transitions are necessary. By tracking the cumulative deviation of attributes, MR mode ensures that high-resolution models are activated only when critical thresholds are exceeded, maintaining accuracy while optimizing computational resources. The visual depiction highlights the method’s adaptability in striking a balance between precision and efficiency in complex, multi-resolution simulations.

5. Comparison of MRM Approaches

The columns in

Table 1. Comparison of MRM methodologies.

Approach	Consistency	Computational Efficiency	Flexibility/Scalability	Integration with Legacy Systems	Implementation Complexity	Cost-Effectiveness
IHVR	High for hierarchical relationships; struggles with process-based consistency.	Moderate; depends on the complexity of variable relationships.	Limited to hierarchical systems.	Requires significant adaptation for non-hierarchical systems.	High; defining relationships between variables is complex.	Moderate; effective for hierarchical systems but costly to scale.
Module-Based Approach	Moderate; each federate handles its consistency.	Low; network flooding increases computational load.	Low; adding new federates requires bespoke regulators.	Limited; bespoke regulators complicate integration.	High; debugging and customization are required for each federate.	Low; time-intensive with limited reuse potential.
Regulation as Middleware	High; central regulator enforces consistent aggregation and disaggregation.	High; reduces RTI workload and minimizes unnecessary interactions.	Moderate; depends on middleware design and compatibility.	Moderate; requires standardized interfaces.	Moderate; developing middleware can be complex but manageable.	Moderate; centralized design reduces overall resource usage.
Regulator as Federate	High; centralized control ensures consistent interactions.	Moderate; computational load depends on regulator design.	High; easily scales with additional federates.	High; standalone regulator supports diverse federates.	Moderate; regulator design requires tailored logic.	High; efficient aggregation/disaggregation reduces costs.
Resolution Converter	Moderate; consistency depends on translation accuracy.	Moderate; data translation increases overhead.	Low; limited to specific scenarios or systems.	Moderate; requires extensive customization for legacy systems.	High; building converters for each scenario is resource-intensive.	Moderate; custom solutions are not universally applicable.
Selective Viewing	High; ensures consistency by maintaining high-resolution models.	Low; resource-intensive, as all high-resolution models are always active.	Low; less adaptable for integrating disparate simulations.	Limited; works best for single, cohesive systems.	High; requires consistent rules across models.	Low; inefficient for computational resource usage.
Aggregation/Disaggregation	Moderate; depends on the accuracy of triggers and algorithms.	Moderate; risks of chain disaggregation impact efficiency.	Moderate; effective but can face bottlenecks with larger models.	High; adaptable for integrating various federates.	Moderate; trigger and algorithm design add complexity.	High; widely used and well-established in military systems.
MRE	Very High; consistency enforcer maintains logical coherence across levels.	Low to Moderate; requires significant computational resources.	Moderate; better suited for newer systems than legacy integration.	High; can leverage COTS systems effectively.	High; consistency enforcer logic is complex to implement.	Moderate; computational resource demands increase costs.
Hybrid	High; balances consistency with efficiency through core attributes.	High; reduces overhead by focusing on essential attributes.	Moderate; identifying core attributes can be challenging.	Moderate; adaptable but requires careful planning.	High; core design and attribute generation functions are complex.	High; balances efficiency and cost with focused attribute usage.
Agent-Based	High; autonomous agents maintain internal consistency.	Moderate to High; computational load depends on agent complexity.	Very High; easily integrates new agents or models.	Moderate; less suited for legacy systems without redesign.	High; building agents requires significant effort.	High; offers flexibility and scalability for modern systems.
MR Mode	Very High; dynamic resolution selection ensures consistency.	High; adaptive selection reduces computational strain.	High; scales seamlessly across resolutions in simulations.	Moderate; integrates effectively with legacy and new systems alike.	Moderate; complex algorithms but mangeable with structured workflows.	High; cost-efficient due to reduced resource usage and streamlined integration.

represent key dimensions used to evaluate and compare various MRM methodologies. Each dimension addresses a critical aspect of implementing MRM systems, providing insights into the strengths and limitations of different approaches.

Consistency reflects an MRM approach’s ability to maintain logical coherence across multiple levels of resolution. It is essential to ensure that data and behaviors at high and low resolutions align, avoiding inaccuracies during transitions between aggregated and disaggregated states. Inconsistent systems can lead to flawed decision-making, particularly in military simulations, where aggregated command-level models must align with detailed unit-level simulations.

Computational efficiency effectively measures an MRM method’s use of computational resources. It considers whether a method can balance high-resolution detail with computational constraints, such as processing power and memory. Efficient approaches reduce redundant calculations, enabling real-time applications and reducing overall operational costs. Methods like hybrid approaches aim to optimize computational load by focusing on critical attributes.

Flexibility/scalability assesses how easily an MRM system adapts to new scenarios or scales with increasing complexity. Flexible systems can integrate additional models or federates without significant reengineering. Scalability, however, ensures that the system can handle growing demands, such as more entities or higher levels of detail, without losing performance.

Integration with legacy systems evaluates how well an MRM approach can incorporate pre-existing simulations or models. Many organizations rely on legacy systems, which are often difficult to modify. Effective integration ensures seamless communication and data exchange, bridging the gap between old and new systems to maximize resource utilization and reduce redundancy.

Implementation complexity underscores the challenges associated with designing, developing, and maintaining an MRM approach. High implementation complexity may involve intricate algorithms, specialized components, or bespoke solutions, making deployment resource-intensive. Simpler systems, while easier to implement, may sacrifice advanced features such as consistency and flexibility.

Cost-effectiveness considers the balance between an approach’s benefits and its resource requirements. This includes development costs, operational efficiency, and scalability. Cost-effective methods, such as agent-based or hybrid models, maximize returns by reducing resource demands without compromising performance or accuracy. This dimension is critical for choosing an approach that aligns with an organization’s operational and financial constraints.

Gene Lee and Luis Rabelo [1] compared MRM approaches based on interaction consistency, cost-effectiveness, and scalability, identifying MRE and agent-based methods as particularly effective. MREs ensure high consistency and seamless integration with COTS (commercial-off-the-shelf) systems, making them well-suited for maintaining logical coherence across multiple resolution levels. Meanwhile, agent-based simulations excel in flexibility and detailed behavioral modeling, offering a scalable and adaptive approach for federated systems. With the introduction of MR mode, a novel methodology, a new balance of consistency, computational efficiency, and scalability emerges, adding another strong contender to the MRM landscape.

The comparison of MRM approaches highlights distinct advantages and limitations across these methodologies. For consistency, MRE and regulator-based approaches, such as Regulation as Middleware and Regulator as Federate, stand out for their robust ability to maintain logical coherence during aggregation and disaggregation processes. MRE achieves this through a consistency enforcer that dynamically manages interactions across resolution levels, although this precision incurs high computational costs. Similarly, MR mode introduces an innovative layered structure that ensures high consistency by synchronizing multiple resolution levels with minimal resource overhead, making it a computationally efficient alternative. In contrast, simpler approaches like IHVR and selective viewing ensure consistency through structured hierarchies and continuous high-resolution operation, respectively, but are constrained by limited flexibility and scalability, making them less adaptable to diverse or legacy systems.

From a cost-effectiveness perspective, the hybrid approach, agent-based modeling, and MR mode emerge as strong candidates. The hybrid method strikes a balance between efficiency and consistency by focusing on core attributes, thereby reducing computational demands while maintaining accuracy. Agent-based modeling offers unmatched flexibility and scalability, facilitating the seamless integration of new entities or models. However, its integration with legacy systems can be challenging without redesigning. MR mode enhances cost-effectiveness by reducing the computational complexity typically associated with MREs, while maintaining their core benefits, such as high consistency and seamless multi-resolution transitions. Meanwhile, the Regulation as Middleware and Regulator as Federate approach balances functionality and cost by centralizing aggregation–disaggregation logic, thereby reducing network and computational overheads. Conversely, methods like module-based approaches and resolution converters are less cost-effective due to their higher implementation complexity and limited scalability, making them less suitable for large-scale or dynamic MRM environments.

5.1. Practical Implementations

Table 2. Examples of practical implementation.

MRM Methodology	Practical Implementation Example
Integrated Hierarchical Variable Resolution (IHVR)	Used in campaign-level military simulations; variables like logistics, personnel, and firepower are structured hierarchically to support mission planning.
Module-Based Approach	Implemented in air traffic control training simulations, where each aircraft simulator operates semi-independently but communicates resolution changes through local modules.
Regulation as Middleware	Applied in spaceport operations simulations, middleware synchronizes data between launch vehicle systems and ground control models.
Regulator as Federate	Used in distributed tank training simulations where a central federate dynamically activates high-resolution simulations for specific vehicles.
Resolution Converter	Adopted in smart city traffic simulations to connect fine-grained vehicle data with aggregate traffic flow models across different simulation tools.
Selective Viewing	Implemented in command and control systems where commanders can zoom into detailed battlefield sections while retaining a complete high-resolution model.
Multi-Resolution Entities (MRE)	Utilized in logistics networks where warehouse operations and vehicle routing are modeled simultaneously across resolution levels using consistent attributes.
Hybrid Approach	Applied in disaster response simulations, core variables like response time and medical readiness are preserved across granular and aggregated views.
Agent-Based Modeling (ABM)	Used in crowd behavior simulations where each agent represents a soldier or civilian, adapting behavior and resolution based on scenario conditions.
MR Mode	Implemented in battlefield simulations to dynamically adjust resolution based on cumulative interaction deviation, optimizing performance in high-intensity scenarios.

shows examples of practical implementation for each reviewed MRM methodology, illustrating their real-world applicability. These examples help bridge the gap between theoretical concepts and practical application by demonstrating how each approach can be effectively applied within simulation environments. Including these examples enhances understanding of each methodology’s strengths, limitations, and potential for integration into existing systems.

5.2. Evaluation Metrics and Methodological Contributions

Table 3. Metrics and methodological contributions.

Measure	Supporting References	Contribution
1. Consistency	“Consistency maintenance in multiresolution simulation.” ACM TOMACS [27]	Introduces the consistency enforcer in MREs, emphasizing logical consistency across resolution levels.
	“Engineering Principles of Combat Modeling and Distributed Simulation.” [25]	Discusses semantic and syntactic consistency in distributed simulation.
2. Computational Efficiency	“MRE: A flexible approach to multi-resolution modeling.” PADS Conference [26]	Describes hybrid methods for reducing computational load in real-time systems.
	“Experiments in Multiresolution Modeling (MRM).” RAND Corporation [20]	Analyzes performance trade-offs in IHVR and aggregation methods.
3. Flexibility and Scalability	“Development of the Multi-Resolution Modeling Environment through Aircraft Scenarios.” SAE Technical Paper [19]	Highlights scalability issues in LVC and flexibility in dynamic tactical-level modeling.
	“Multiple Resolution Modeling: A Particular Case of Distributed Simulation.” [3]	Discusses extensibility of MRM systems in evolving distributed architectures.
4. Integration with Legacy Systems	“Aggregation/Disaggregation in HLA multiresolution distributed simulation.” [21]	Describes challenges and middleware designs aimed at supporting legacy systems.
	“A Distributed Environment for Spaceports.” SAE Transactions, Journal of Aerospace [18]	Addresses legacy integration with HLA and XML-based interfaces.
5. Implementation Complexity	“A Resolution Converter for Multi-resolution Modeling/Simulation on HLA/RTI.” Asia Simulation Conference [14]	Highlights implementation burden of resolution converters and bespoke translation layers.
6. Cost-Effectiveness	“Engineering Principles of Combat Modeling and Distributed Simulation.” [25]	Frames cost–benefit analysis in model fidelity vs. resource utilization.
	“Weapon Combat Effectiveness Using Big Data and LVC Simulation.” SAE Technical Paper [7]	Discusses ROI and performance trade-offs in multi-resolution combat simulation.

comprehensively analyzes key metrics such as consistency, computational efficiency, flexibility, scalability, integration with legacy systems, implementation complexity, and cost-effectiveness. This analysis highlights each methodology’s distinct contributions and trade-offs, offering a clear overview to support informed selection and application in various simulation contexts.

6. AI and LLMs in MRM

Emerging research highlights the transformative role that artificial intelligence (AI), particularly large language models (LLMs), can play in enhancing multiple-resolution modeling (MRM). AI and LLMs have been identified as critical enablers for addressing interoperability, consistency, and computational challenges inherent in MRM frameworks. The fundamental technologies enabling the recent advancements in AI and LLMs have been rooted in deep learning methods. Notably, there is already seminal work illustrating the integration of deep learning with core MRM-related technologies such as distributed discrete-event simulation [29]. Recent studies further demonstrate that AI-driven systems, especially LLMs, effectively automate complex processes such as aggregation and disaggregation. By intelligently interpreting semantic discrepancies across diverse simulation models, LLMs enhance interoperability among distributed simulations, streamlining the integration process [30]. This capability is particularly beneficial in dynamic resolution management scenarios, where automated, context-aware transitions between simulation resolutions are critical. AI-based adaptive resolution management can dynamically adjust resolution levels, balancing computational load with model fidelity and accuracy, thus optimizing simulation performance and resource utilization [31]. Moreover, current methodologies within MRM, MRE, and ABM, could significantly benefit from explicit integration of AI and LLM technologies:

• Middleware-based approaches: LLMs have the potential to automate real-time interpretation and translation of data exchanges among heterogeneous federates. This reduces development complexity, mitigates network flooding, and accelerates the integration of legacy systems [32].
• MREs: AI-driven algorithms can dynamically optimize attribute synchronization and calculation processes. LLMs can intelligently approximate entity attributes, significantly reducing computational overhead and improving consistency [33].
• ABM: Leveraging AI within ABM allows for adaptive, context-sensitive behaviors among simulation agents, greatly enhancing the model’s realism and scalability. AI-enhanced agents can autonomously manage resolution adjustments and behavioral rules in response to evolving scenarios [34].

To facilitate a comparative evaluation,

Table 4. Metrics and Methodological Contributions.

Approach	AI/LLM Integration Potential
IHVR	Moderate; AI can dynamically automate hierarchical calibration.
Module-Based	Moderate; AI could dynamically manage localized resolution decisions.
Approach
Regulation as Middleware	High; LLMs simplify semantic translation and streamline federate integration.
Regulator as Federate	Moderate; AI facilitates centralized decision-making and efficient resolution transitions.
Resolution Converter	High; LLMs automate semantic and temporal translations across resolution levels.
Selective Viewing	Moderate; AI dynamically adjusts views based on real-time contextual requirements.
Aggregation/Disaggregation	Moderate; AI enhances dynamic attribute management and improves disaggregation precision.
MRE	High; AI significantly optimizes attribute synchronization and intelligent approximation processes.
Hybrid	High; AI optimizes core attribute selection, effectively minimizing computational overhead.
Agent-Based	High; AI enhances adaptive agent behaviors and autonomously manages resolution transitions.
MR Mode	High; AI dynamically adjusts resolution selections, improving computational efficiency and consistency.

below has expanded Table 1 to explicitly incorporate the integration potential of AI and LLM technologies within established MRM methodologies:

Current and ongoing research highlights AI and LLM technologies as central to future advancements in MRM. Future empirical studies will explicitly investigate practical integration scenarios involving AI and LLMs, focusing on semantic interoperability, intelligent adaptive resolution management, and AI-driven computational optimization. These empirical investigations aim to validate the practical effectiveness of AI and LLM enhancements within complex distributed simulation environments, ensuring robust interoperability and improved performance for next-generation MRM frameworks.

7. Conclusions and Future Directions

MRM methodologies have emerged as frameworks within modern distributed simulations, significantly enhancing interoperability, scalability, and analytical versatility across varied resolution levels. This structured review and comparative analysis have shown that methodologies such as MRE, IHVR, Regulation as Middleware, and ABM offer distinct strengths and tailored solutions for addressing critical challenges including interoperability, consistency, and computational efficiency.

Our review highlights that MREs provide robust mechanisms for maintaining data consistency across multiple resolutions, albeit at the cost of higher computational resources. In contrast, hybrid, module-based, and middleware approaches effectively balance computational load and complexity, ensuring smoother resolution transitions while minimizing resource consumption. Agent-based modeling and novel approaches such as MR mode demonstrate promise in flexibility and scalability, accommodating increasingly dynamic and complex simulation scenarios.

The analysis also underscores that incorporating AI and particularly LLMs represents a critical evolution in the MRM field. These technologies, grounded in deep learning methods, can significantly improve automated aggregation, disaggregation, and dynamic resolution transitions, thereby greatly enhancing interoperability and computational efficiency. Initial research integrating deep learning and distributed discrete-event simulation has demonstrated substantial promise, laying a foundational pathway for broader adoption and innovation in AI-driven MRM methodologies.

Nevertheless, critical challenges remain. These include achieving seamless integration with legacy systems, maintaining synchronization across diverse federates, and addressing implementation complexity. Thus, future research efforts must focus on developing advanced AI-driven middleware and standardized frameworks capable of bridging existing methodological gaps. Empirical validations through practical implementations in realistic operational environments will be essential for verifying the effectiveness and efficiency of these emerging approaches.

Finally, ongoing and future research should specifically investigate empirical integration scenarios involving AI and LLMs. Key areas for exploration include intelligent semantic interoperability, adaptive resolution management, and computational optimization driven by deep learning. Addressing these aspects will facilitate the practical realization of robust, adaptive, and highly interoperable MRM solutions capable of meeting the sophisticated requirements of modern distributed simulation environments, especially in demanding fields such as military training, strategic planning, and operational readiness.