SIMULATION: Transactions of The Society for Modeling and Simulation International

Batchoudi, Gaston; Ramat, Eric; Foucher, Clément

doi: 10.1177/00375497261450985pmid: N/A

As discrete-event systems grow in complexity across domains such as manufacturing, communication, and cyber-physical systems, their modeling and simulation face increasing challenges. Key issues include model reusability, interoperability across heterogeneous simulation platforms, and maintaining consistency of execution semantics. Traditional approaches often rely on simulators bound to specific programming languages, which restricts portability and hinders collaboration across different tools. To overcome these limitations, this paper introduces FPDEVSML, a formal specification language for parallel-DEVS (PDEVS) models. FPDEVSML provides a structured and unambiguous framework for defining system behavior independently of any particular simulator. This language is based on formal foundations in mathematical logic, algebra, and set theory that ensure only well-formed models can be expressed and validated. From a given specification, models are automatically parsed, semantically analyzed, and transformed into a platform-independent representation, which is then used to generate executable code for multiple simulation environments following model-driven engineering (MDE) approach. We demonstrate this approach through the ft4devs (Formal Tool for DEVS) framework, which we developed, providing code generation for both Artis*/C++ and PyPDEVS/Python. Case studies highlight the FPDEVSML language’s expressiveness in modeling parallel and modular systems. The results confirm the feasibility of the approach, bridging the gap between formal modeling and practical simulation for PDEVS modeling formalism, and paving the way for future automatic integration with formal verification.

Bergonzi, Mariana; Castro, Rodrigo; Kofman, Ernesto

doi: 10.1177/00375497251392588pmid: N/A

In this work, we introduce the concept of activity homogeneity in the solutions of ordinary differential equations (ODEs) and characterize it through a metric called Homogeneity Factor. This indicator quantifies the degree of similarity in the temporal evolution of the system state variables. We show that this measure can be related to the convenience of using classic numerical integration schemes or quantization-based methods such as Quantized State System (QSS) algorithms. The developed notions, which are also extended to systems exhibiting discontinuities, provide a theoretical argument that corroborates observations from previous works, indicating that QSS methods offer advantages when activity is heterogeneous, systems are sparse, and/or frequent discontinuities occur. The concepts are applied to two case studies: an advection-diffusion-reaction model and a spiking neural network. Theoretical predictions are compared with empirical results obtained through simulations using different numerical integration methods, confirming that the proposed metric consistently identifies the integration strategy that is more computationally efficient in practice.

Massa, Marco; Bachis, Federico; Brelstaff, Gavin; Deriu, Massimo

doi: 10.1177/00375497261428103pmid: N/A

This paper explores an initial attempt to use the Unity ML-Agents toolkit to model the behavior of people evacuating from indoor fires. The virtual environment was created in the Unity game engine and populated with humanoid agents capable of moving autonomously within the scene. Each agent perceives information from the rendered environment, such as surfaces, directions, and line-of-sight depth and uses it to navigate toward the nearest exit. Agents were trained through reinforcement learning, using the Proximal Policy Optimization (PPO) algorithm to balance rewards and penalties for their actions. We tested five different reward schemes in single-agent simulations to observe how these affect navigation behavior. Among them, the version referred to as mark5 showed the most plausible and efficient evacuation strategy, reaching the exit quickly while avoiding collisions. The same trained agent was then used in multi-agent settings, where its performance remained stable even with groups of up to 20 individuals. These first results suggest that Unity ML-Agents can offer a practical foundation for building more realistic and adaptive evacuation models.

Edali, Mert

doi: 10.1177/00375497261448926pmid: N/A

Simulation modeling is widely used to investigate the impact of interventions on infectious disease transmission. However, individual-based models often involve numerous parameters, making exhaustive exploration computationally prohibitive. Metamodels can address this limitation, and when coupled with interpretable machine learning, they can provide insights into intervention impacts. Moreover, multiple features of epidemic curves should be considered when evaluating interventions. Using the open-source agent-based model Covasim, we examined non-pharmaceutical interventions affecting key epidemic curve features, such as peak day, peak value, and attack rate. We employed random forests as a metamodeling technique to capture nonlinear input–output relationships. Then, we used the Boruta algorithm to identify impactful interventions. Results showed that random forest metamodels explained moderate to high variance across outputs. The Boruta method indicated that influential interventions differed by outcome. Social distancing measures, such as school closures and workplace density reductions, were mostly unimportant in the current Covasim setting, whereas intervention timing, testing, contact tracing, and mask use consistently influenced all outputs to varying degrees. Our metamodel- and variable selection-based framework can provide insights into the effectiveness of multiple interventions across various characteristics of the epidemic curve, thereby supporting public health decision-making at the onset of an epidemic.

Berardi, Abigail S.; Leathrum, James F.; Shen, Yuzhong

doi: 10.1177/00375497261447015pmid: N/A

Reliable, performant code is essential in modeling and simulation, and integration of AI-generated code into simulation software development must not undermine the stability or integrity of models. Generative AI produces variable outputs, resulting in LLM-generated code that does not reliably satisfy standards of correctness or performance. This paper presents an LLM prompt engineering framework, Goal, Performance, Exclusion Architecture (GPE-A), that structures intent, constraints and performance criteria to guide the generation of reliable, performant simulation code. Rather than attempting reproducibility of an implementation, the framework steers generative outputs toward convergence on required behavioral metrics, consistent with requirements-driven development. The framework is evaluated using LLM-generated random number generation code as a representative simulation component across classical and quantum computing. Random number generation is a foundational simulation primitive and domain where quantum methods allow a comparable proof-of-concept implementation against classical implementations. Metric-based comparisons are made to production baselines, assessing correctness, statistical stability, and computational performance. The GPE-A is evaluated across multiple LLMs, through prompt ablation, and against modern prompting methods. Results indicate that structured LLM prompt engineering can increase predictability and quality in AI-generated simulation code, exceeding production-standard baselines for random number generation, and indicate potential extensibility toward emerging computational paradigms.

Johansson, Emil; Lorig, Fabian; Davidsson, Paul

doi: 10.1177/00375497261453360pmid: N/A

This paper explores the combination of agent-based social simulation (ABSS) models. Model combination facilitates the efficient development of more complex models through reuse, enabling a more comprehensive understanding of phenomena and outcomes that individual models cannot provide on their own. Through a narrative literature review of model combination in other simulation paradigms, six different approaches were identified: ensemble techniques, meta-analysis, model merging, models as modules, model integration and model chains. For each approach, examples and relevant literature are presented, and current challenges are identified. To illustrate the different approaches, a number of models of disease spread are then implemented and combined according to each approach. Through this, the paper aims to both provide inspiration to modelers and to identify paths for future research for the combination of ABSS models and model results.

Mushtaq, Shahrukh; Onkal, Dilek

doi: 10.1177/00375497261429524pmid: N/A

The emergence of agentic artificial intelligence (AI) presents a novel frontier for cybersecurity research, yet its potential to simulate complex human behaviours in controlled environments remains underdeveloped. While extensive literature examines employee compliance with cybersecurity policies, it lacks leveraging agentic AI. To bridge this gap, this study implements a novel four-phase research design to identify the configurations predicting employee compliance intentions (CI) with institutional policies in the e-government sector. The proposed approach integrates agentic AI simulations, where AI agents emulate employee responses to multi-scenario vignettes. The study first employs a grounded theory approach, following the Gioia methodology, to code AI-generated qualitative data into theoretically grounded themes. Subsequently, it utilises AI agents to weight quantitative responses. The analysis reveals distinct behavioural archetypes (i.e. embracers, negotiators and resisters). Finally, it reports fuzzy-set qualitative comparative analysis (fsQCA) to move beyond net effects and uncover specific pathways of conditions that consistently lead to high CI. This work foregrounds agentic AI simulations as a pioneering tool for behavioural analysis. It offers a replicable methodology for investigating complex socio-technical phenomena and suggests new avenues for simulation-based inquiry. This research establishes a conceptual foundation for facilitating theory development and methodological innovation using agentic AI in simulation.

Alshareef, Abdurrahman

doi: 10.1177/00375497261434182pmid: N/A

The demand for systems engineering methodologies with integrative artificial intelligence (AI) has been increasing. Model-based systems engineering offers a disciplined, structured methodology. However, it encounters difficulties with semantic interpretation and domain adaptation, especially across different contexts. In this work, we examine the potential of generative AI to address this challenge. We implement a dual approach to enrich the modeling experience by incorporating domain adaptations via large language models and executable semantics via discrete-event simulation. The result is a bootstrapped, end-to-end automated system model construction from minimal entry points, featuring built-in, generic executable capability that adheres to the simulation-to-production system principle. We will demonstrate how a user of such an approach can produce a sound, semantically rich model with advanced simulations from a minimal textual entry. We also discuss mechanisms for incorporating knowledge and expertise through a convenient yet effective human-in-the-loop integration. We demonstrate the approach through a detailed semiconductor wafer fabrication experiment and further illustrate its generality across diverse domains through extensive generative and simulation-based evaluations.

Cerrudo-Herrera, Jesús; Talaván-Vega, Daniel; Rodríguez-Oliver, Paloma; Ziabat-Ziabat, Ahmed; Rico-Gallego, Juan Antonio

doi: 10.1177/00375497261438515pmid: N/A

The increasing complexity of quantum algorithms and the limitations of current Noisy Intermediate-Scale Quantum (NISQ) hardware underscore the importance of efficient classical simulators. To support informed decision-making by users of quantum circuit simulators, we benchmark seven statevector-based quantum circuit simulators (Qiskit, Qulacs, Qibo, Qsimov, Cirq, Pennylane and the Intel Quantum Simulator (IQS)) on a multicore node of the Lusitania high-performance computing (HPC) system. We evaluate their performance in terms of execution time, memory usage and core scalability using Grover’s algorithm, the quantum Fourier transform (QFT), and quantum volume (QV) circuits, across qubit counts ranging from 3 to 30. Our results reveal that Qulacs offers the best performance for circuits below 22 qubits, while Qiskit becomes the fastest for larger and more complex circuits. Qiskit and Qulacs achieve the most efficient parallel performance across multiple cores, while others display limited scaling benefits. IQS shows the lowest memory consumption in QFT and QV benchmarks for systems under 24 qubits; however, it suffers from higher execution times, particularly for Grover’s algorithm. The experimental implementation of Qsimov consistently underperforms in both runtime and scalability; for this reason, it is employed as a baseline in our measurements, serving to highlight the importance of performance optimizations in statevector-based quantum circuit simulators. Previous findings provide a comprehensive performance landscape to guide researchers in selecting appropriate simulators for both standard and large-scale quantum workloads on HPC infrastructures.

Logambal, M; Padmasekaran, S

doi: 10.1177/00375497251411040pmid: N/A

This paper proposes a new approach to delay differential equations and combat the growing heart disease epidemic. A stability analysis of heart disease modeling is conducted using approximate state data, focusing on its connection to cardiovascular disease, heart attacks, and strokes. The model considers the connection of heart disease to specific infections. Analysis is done on qualitative behaviors, like the presence and uniqueness of solutions. In addition, the positivity and boundedness of the solution are investigated. The stability requirements and equilibrium points are satisfied. Since the existing solutions are asymptotically stable locally when R0<1 and globally for R0>1, we next calculate the fundamental reproduction ratio, also known as the reproduction number. In addition, locally stable steady-state solutions for R0<1 are found to be included in infection. Finally, the theoretical insights about heart disease are ultimately confirmed through empirical numerical data.