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 is presented and current challenges are identified. 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.

We present an interactive agent-based model that showcases Spain's organ donation policy approach if applied to Sweden. The gamified experience fosters an understanding of complex public health policies.

Healthcare policy making is complex, requiring both human expertise and data-driven support. Agent-based Social Simulations (ABSS) provide a powerful tool for testing the potential consequences of healthcare policy interventions in a controlled environment. By integrating computational modeling with expert knowledge, ABSS enable hybrid-human policy collaborations, where simulation results support human decision-making. This approach facilitates policy refinement and scenario analysis through participatory modeling, leading to more adaptive and socially sustainable policies. We argue that ABSS enhances decision-making by complementing simulation-based insights with qualitative expertise, enabling more sustainable and evidence-based healthcare policies.

Mobile stroke units (MSUs), which are specialized ambulances equipped with a brain imaging device and staffed with trained healthcare personnel, have the potential to provide rapid on-site diagnosis and treatment for stroke patients. To maximize the efficiency of utilizing MSUs, it is crucial to strategically allocate these units. When solving the MSU allocation problem, the current methods search the whole search space when looking for the optimal solutions, which causes slow convergence. In the current paper, we propose the Quality Clustering for Reducing the Search Space (QCRSS) framework to reduce the search space by filtering out ambulance locations without negatively affecting the quality of the solution too much when solving the MSU allocation problem. By narrowing down the set of possible locations, the problem becomes more manageable, leading to faster convergence when solving the MSU problem. Extensive experiments under the multiple MSU settings show that the QCRSS is large ly faster in convergence toward the optimal solution by reducing the search space by 5x, 11x, 26x, and 67x for two, three, four, and five MSUs, respectively. We illustrate the performance of the QCRSS through both qualitative and quantitative analyses.

Existing courses on agent-based modeling and simulating (ABMS) are mainly aimed at doctoral students and many modelers have acquired their ABMS skills by teaching themselves. This paper reports and reflects on the development of an undergraduate course on ABMS of social systems. It presents a problem-based approach to teaching ABMS of social systems, the Integrated Learning Outcomes (ILOs), and the course structure. This paper discusses the constructive alignment of the syllabus, presents the results from the course evaluation, and draws conclusions for further editions of the course. Rather than proposing how such courses should be structured, we discuss the feasibility of the pursued research-based learning approach. Our goal is to inspire other researchers and teachers to develop similar courses, to encourage the establishment of a general curriculum, and to promote ABMS in undergraduate education.

The application of Agent-Based Modeling and Simulation (ABMS) has few established guidelines and oftensuffers from insufficient model documentation. We assess the prevalence of best practices associated withdifferent types of model documentation in light of the European Union’s AI Act (AI Act). Our analysisreveals that best practices are often implemented together but ultimately reinforce the pre-existing viewthat ABMS frequently lacks adequate model documentation. This deficiency hinders evaluability, makingit difficult to conduct quality assurance prior to application and meaningful evaluation post application.We propose a framework that highlights the importance of different types of model documentation and theattributes they enable, which are valuable to both modelers and policy actors, albeit for different reasons.The AI Act provides a valuable opportunity to improve model documentation. By proactively developingand establishing guidelines, we can stay ahead of emerging legal requirements.

Organ donation is a crucial aspect of healthcare, yet, the number of donors is insufficient to cover the demand for transplant procedures. In the European Union, around 15 people die each day waiting for a life-saving organ.  National policies differ greatly among countries, but it is unclear how successful policies affect Deceased Organ Donation when introduced in other settings. This paper explores the use of Agent-Based Social Simulation (ABSS) to inform organ donation policymaking. It provides policy actors with a safe environment to investigate the consequences of different policy interventions without the risk of harming people. We present a simulation model of the Swedish organ donation system, where we can investigate the impact of Spain's policy approach, which has the highest DOD rates in Europe. The results highlight the potential of ABSS as a tool for evaluating policy interventions in complex healthcare systems, enabling policymakers to identify and evaluate strategies before implementation.

There is increasing recognition of the role that artificial intelligence (AI) systems can play in managing health crises. One such approach, which allows for analysing the potential consequences of different policy interventions is agent-based social simulations (ABSS). Here, the actions and interactions of autonomous agents are modelled to generate virtual societies that can serve as a “testbed” for investigating and comparing different interventions and scenarios. This piece focuses on two key challenges of ABSS in collaborative policy interventions during the COVID-19 pandemic. These were defining valuable scenarios to simulate and the availability of appropriate data. This paper posits that drawing on the research on the “everyday” digital health perspective in designing ABSS before or during health crises, can overcome aspects of these challenges. The focus on digital health interventions reflects a rapid shift in the adoption of such technologies during and after the COVID-19 pandemic, and the new challenges this poses for policy makers. It is argued that by accounting for the everyday digital health in modelling, ABSS would be a more powerful tool in future health crisis management.

Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the duration for an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to delayed emergency response times, potentially impacting patient outcomes. In the current paper, we propose a novel framework for accurately estimating ambulance travel times using machine learning paradigms, employing real-world spatiotemporal ambulance data from the Skane region, Sweden. Our framework includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. First, through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. Then, we present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trip scenarios and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy. Our experiments indicate that the aforementioned factors play a significant role when estimating the travel time.

Mobile stroke units (MSUs), which are specialized ambulances equipped with a brain imaging device and staffed with trained healthcare personnel, have the potential to provide rapid on-site diagnosis and treatment for stroke patients. However, efficient access to prehospital stroke care requires optimizing the placement of MSUs. The MSU allocation problem has been previously solved using a traditional genetic algorithm that utilizes random starting solutions. The use of random starting solutions can, however, cause the algorithm to converge slowly. This can be especially problematic if the initial solutions are significantly far from the global optimum. To address this problem, we propose an enhanced genetic algorithm with clustering (EGAC), which is a time-efficient method to solve the MSU allocation problem by identifying the optimal locations of MSUs in a geographic region. By leveraging clustering, the EGAC provides diverse and comprehensive coverage, avoiding the pitfalls of starting with closely located and potentially less optimal solutions, thereby effectively steering and accelerating its convergence towards the optimal MSU placements. Our experimental results show that the EGAC significantly outperforms the traditional genetic algorithm, without cluster-based starting solutions, by achieving remarkably faster convergence toward the optimal solution for different number of MSUs to allocate. We validate the performance of the EGAC through qualitative and quantitative analyses.