ABSTRACT This study employs a system dynamics framework to analyze the impact of climate policy uncertainty and aging populations on global banking stability using data from over 3500 banks (2010–2019). The model uniquely integrates feedback loops, nonlinear interactions, and time delays to assess how these shocks propagate through deposits, loans, profitability, and risk metrics. Key findings reveal that a 30% aging population shock reduces bank profitability by 25.6% due to compressed interest margins, while a 30% CPU shock contracts lending by 10.4% and sustains elevated non-performing loans (NPLs), highlighting systemic vulnerabilities. Risk sentiment shocks amplify financial instability, with a 30% increase triggering a 5.7% profit decline. The study contributes to literature by bridging climate and demographic risks with banking dynamics, offering a stress-testing tool for policymakers. Key policy implications include integrating climate risks into macroprudential regulation, incentivizing adaptive financial products, and enhancing dynamic provisioning to mitigate nonlinear feedback effects.

ABSTRACT Global disruptions have shown that shocks to supply chains can quickly ripple through entire economies, highlighting the need to identify vulnerabilities and evaluate mitigation strategies to build resilience. In this paper, we propose a simulation methodology, Hybrid Integrated Supply-Chain Simulation (HISS), to identify and mitigate potential disruptions in supply chains. We demonstrate HISS using a generic pharmaceutical supply chain model including sourcing, outsourcing, production, packaging, and distribution processes, created using MASON’s hybrid modeling capabilities. We classify disruptions from malicious actors and analyze their timing, impact, and scope. The simulation is further extended to modeling mitigation strategies and assessing their efficacy. Extensive optimization allowed us to identify worst-case disruptions and optimized safety stock strategies reduced impacts by a factor of five, while anomaly detection achieved a high recall of 0.966. The modeling approach proposed in this paper provides a basis for planning tools that support resilience and preparedness of supply chains.

ABSTRACT Offshore wind farm installation requires complex planning under uncertainty, and numerous system models have been developed to support scheduling and resource allocation. However, differences in model formulation, solution methods, and level of detail make it difficult to identify the best approaches for a given project. This paper develops a domain-specific taxonomy to compare and select system models for offshore wind installation planning. The taxonomy is grounded in a systematic literature review of 194 studies and adapts general model comparison criteria to four dimensions: application context, model formulation, solution methods, and model features. The taxonomy is applied to six representative offshore scheduling approaches to illustrate its use. Results show that the taxonomy clarifies model scope, strengths, limitations, and trade-offs, providing a structured basis for model selection and revealing opportunities for methodological refinement. While tailored to offshore wind installations, the approach could be transferable to other domains with heterogeneous system models.

ABSTRACT This study presents a qualitative analysis of the impact of time delays on the transmission dynamics of infectious diseases within a population. A delay differential equation model is formulated, and key mathematical properties, such as non-negativity, boundedness, and the existence and uniqueness of solutions and equilibrium points are established. Particular emphasis is placed on the delay associated with vaccination, identified as a critical factor capable of triggering stability switching in the system. Even small variations in this delay can lead to substantial changes in system behavior, including transitions from a stable disease-free equilibrium to persistent endemic states or oscillatory dynamics. These results underscore the significant influence of time delays in determining disease outcomes and provide new insights into delay-induced stability phenomena in epidemiological modeling. Numerical simulations are performed to support the theoretical findings and to illustrate the dynamic effects of varying delay parameters on system stability.

ABSTRACT This paper explores the use of Multi-Agent Systems (MAS) to model networked Command and Control (C2) systems to support the development of C2 military applications. The study focused on the modeling of systems capable of simulating and evaluating important characteristics of networked C2 systems. Therefore, the study covers the context and concepts for the Armed Forces’ battlefield operations under C2 literature, and the potential applications of MAS in networked C2. Remarkably, the paper highlights the study of three different MAS simulators, namely, NetLogo, Gama, and Repast, observing their ability to co-simulate networked C2 systems with Mininet Wi-Fi (the selected communication network emulator). The reasons for studying these simulators are discussed, along with the metrics to select the most suitable one for further developments. It also highlights the contributions these guidelines offer to researchers and practitioners in military C2 systems, especially those involved in integrating MAS simulators in networked setups.

ABSTRACT This paper presents a simulation system utilizing Long Short-Term Memory (LSTM) forecasting models combined with Monte Carlo methods to predict energy generation and model uncertainty from renewable and fossil fuel sources in the Adrar region of Algeria. The study focuses on short-term load forecasting for the Kabertane wind field, the Adrar solar photovoltaic farm, and overall electrical demand. Separate LSTM models predict each renewable source’s output, which are aggregated and subtracted from load demand forecasts to determine necessary fossil fuel requirements. Monte Carlo methods quantify uncertainty by fitting error distributions to forecast residuals and generating multiple realizations with added noise. This probabilistic approach provides robust assessment of economic costs and environmental impacts of energy production. Results demonstrate significant cost savings and CO2 emission reductions through renewable energy incorporation while emphasizing the critical role of uncertainty modeling in optimizing energy production, cost, and environmental sustainability in the region.

ABSTRACT This study explores how empathy can be integrated into decision-making within three Agent-Based Models (ABMs) of disasters and migrations. We design, implement, and evaluate methods to translate the experiences of simulated agents into empathetic narratives through Large Language Models (LLMs), using GPT-4 as a guiding example. We compare two approaches: a direct method that prompts to create empathetic stories (thus leaving it to GPT’s interpretation of empathy), and an indirect method using style transfer by adopting the voice of well-known empathetic figures. Using a Design of Experiments framework, we evaluate the impact of factors including text length and temperature on readability, sentence accuracy, and character authenticity. Results show the indirect method yields better quality narratives. Human readers generally agree that our generated stories show genuine emotions, although full empathy is limited by the extreme scenarios simulated. Our work is provided open-source to support researchers in transforming their ABMs into narratives.

ABSTRACT This paper navigates the subject of “quality” in simulation from the lens of quality-improvement practices, analytically consolidated into a synthesis of associated practices. These practices were initially extracted from an extensive systematic search comprising agent-based models (ABM), discrete-event simulation (DES), system dynamics (SD) and modelling and simulation (M&S) papers, constructing a dataset of 491 practices (following refinement). Their utility was assessed from the perspective of co-occurrence. A synthesis of practices was then constructed through searching the co-occurrence graph, employing a genetic algorithm to maximise cross-relational popularity, while preserving all stages of the simulation lifecycle. The goal is to understand the most popular quality-improvement practices across all stages of simulation development, forming a multi-methodological view of quality. For new simulationists, this paper presents practices to prioritise in their learning to ensure simulation quality. For experts, it suggests how practices are associated through co-utilisation and occurrence, making an open call for improvement.

ABSTRACT Reliable simulations require accurate input data. Invalid values, missing data, and format inconsistencies can cause crashes or result distortions, compromising the findings. This paper presents a methodology for verifying the validity of input data in simulations, a process we term model input verification (MIV). We implement this approach in FabGuard, a toolset that uses established data schema and validation tools for simulation modelling. We formalize MIV patterns and create a verification pipeline for existing workflows. FabGuard’s applicability is demonstrated across three domains: conflict-driven migration, disaster evacuation, and disease spread models. We also explore Large Language Models (LLMs) for automating constraint generation. In a migration simulation case study, LLMs correctly inferred 22/23 developer-defined constraints, identified errors in existing constraints, and proposed new, valid ones. Our evaluation demonstrates that MIV is feasible on large datasets, with FabGuard processing 300 input files in 140 seconds and maintaining consistent performance across file sizes.