Hunger relief networks consist of agencies that work as independent partners within a food bank network. For these networks to effectively and efficiently reduce food insecurity, strategic alliances between agencies are crucial. Agency preference for forming alliances with other agencies can impact network structure and network satisfaction. In this paper, we explore the compatibility and satisfaction achieved by alliances between different agencies. We introduce two agency norms: conservative and diversifying. We develop an agent-based simulation model to investigate alliance formation in a network. We evaluate network satisfaction, satisfaction among different types of agencies, and alliance heterogeneity. We test the statistical significance of satisfaction within a norm and between norms for different agencies. Findings reveal that the ‘diversifying’ norm in the network reduces gaps in satisfaction between strong and weak agencies, ensuring fairness for weaker agencies in the network, whereas the ‘conservative’ norm favors moderate agencies in the network.
Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.
Agent-based simulations for networked anagram games, often taking advantage of experimental data, are useful tools to investigate collaborative behaviors. To confidently incorporate statistical analysis of experimental data into agent-based simulations, it is crucial to conduct sufficient validation for the underlying statistical models. In this work, we propose a systematic approach to evaluate the validity of statistical methods of players' action sequence modeling for networked anagram experiments. The proposed method can appropriately quantify the effect and validity of expert-defined covariates for modeling the players' action sequence data. We further develop a Large Language Model (LLM)-guided method to augment the covariate set, employing iterative text summarization to overcome token limits. The performance of the proposed methods is evaluated under different metrics tailored for imbalanced data in networked anagram experiments. The results highlight the potential of LLM-driven feature discovery to refine the underlying statistical models used in agent-based simulations.
Although Large Language Models get a lot of attention, Generative Artificial Intelligence encompasses a variety of methods such as Generative Adversarial Networks, Variational Autoencoders or Diffusion Models, that all work very differently but are all capable of generating synthetic data. These methods have considerable potential to make simulation studies more efficient, especially through the creation of artificial data sets, automatic model parameterization and assisted result analysis. The aim of this study is to systematically classify generative methods and their applicability in the context of simulation studies. Based on a comprehensive literature review, applications, trends and challenges of generative methods that are used in combination with simulation are analyzed and structured. This is then summarized in a conceptual workflow that shows how and in which phase generative methods can be used advantageously in simulation studies.
Advances in Artificial Intelligence have enabled more accurate and scalable modeling of complex social systems, which depend on realistic, high-resolution population data. We introduce a novel methodology for generating hierarchical synthetic populations using differentiable programming, producing detailed demographic structures essential for simulation and analysis. Existing approaches struggle to model hierarchical population structures and optimize over discrete demographic attributes. Leveraging feedforward neural networks and Gumbel-Softmax encoding, our approach transforms aggregated census and survey data into continuous, differentiable forms, enabling gradient-based optimization to match target demographics with high fidelity. The framework captures multi-scale population structures, including household composition and socio-economic diversity, with verification via logical rules and validation against census cross tables. A UK case study shows our model closely replicates real-world distributions. This scalable approach provides simulation modelers and analysts with, high-fidelity synthetic populations as input for agent-based simulations of complex societal systems, enabling behavior simulation, intervention evaluation, and demographic analysis.
