Mobile Stroke Units (MSUs), which are specialised ambulances equipped with brain imaging devices and trained medical personnel, offer the potential for rapid on-site diagnosis and treatment, improving patient outcomes in prehospital stroke care. To fully realise their benefits, it is essential to strategically allocate. However, identifying optimal locations within a region for MSU deployment is typically computationally challenging due to the vast number of possible combinations of ambulance stations. Existing methods suffer from computational inefficiency, as they consider the whole search space when looking for the optimal solution to the MSU allocation problem. In the current paper, we propose a framework, Quality Clustering for Reducing the Search Space (QCRSS), which uses clustering as a preprocessing step to significantly reduce the number of candidate MSU locations while maintaining high solution quality for the MSU allocation problem. In a real-world use case study, we evaluate our proposed framework in Sweden’s southern healthcare region. Extensive experiments across multiple MSU settings demonstrate that QCRSS achieves the optimal solution for two, three, and four MSUs, and a highly satisfactory solution even for the larger and more complex case of five MSUs. The proposed framework reduces the search space by 5x, 11x, 26x, and 67x and for two, three, four, and five MSUs, respectively. We illustrate the performance of QCRSS through both qualitative and quantitative analyses.

Stroke is the second leading cause of death worldwide, and the time to treatment is the most important factor to increase the chances of desirable recovery. To ensure proper treatment of stroke patients, a diagnosis must first be made to ensure that the correct treatment is provided. This requires a computed tomography scan, which traditionally necessitates transporting the patient to an acute hospital. To reduce the time to treatment, so-called Mobile Stroke Units (MSUs) have been introduced. A mobile stroke unit is a specialized ambulance where stroke patients can be diagnosed and provided with certain types of treatment. For many stroke patients, the use of mobile stroke units can lead to reduced time to treatment improving their chances of recovery. However, mobile stroke units are very expensive, making it important to locate them in a way that maximizes their benefit. In the current paper, we apply Multi-State Particle Swarm Optimization (MS-PSO) to solve the problem of identifying optimal locations for mobile stroke units in a geographical region. To illustrate our method, we applied it to allocate three mobile stroke units in Sweden’s southern health care region. The objective was to find mobile stroke unit locations to maximize the share of the population that is expected to receive treatment within 1 h. For the best-found solution, about 81% of the population is expected to receive treatment within 1 h.

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 rates when introduced in new settings. This paper explores the use of Agent-Based Social Simulation (ABSS) to inform organ donation policymaking. Simulations provide policy actors with a safe environment to investigate the consequences of different policy interventions without the risk of harming people. We present an agent-based model of the Swedish organ donation system, where we can investigate the impact of Spain’s policy approach, which produced the highest DOD rates in Europe. The results highlight the potential of ABSS as a tool for designing policy interventions in complex healthcare systems. Further developments can enable policymakers to identify successful strategies and monitor their effect to evaluate policy progression.

Agent-based models have long been argued a useful toolto support policy analysis, variably targeting the assessment of policydesign, as well as establishing its performance. Challenging, however,remains appropriate empirical parameterization and validation of suchmodels. This paper contributes to the development of rigorous accountsof policy modelling primarily driven by policy documents in order to develop general conceptual model. Such models can then serve as a basis forearly validation by subject matter experts, but more importantly, informthe subsequent inquiry relevant for the parameterization of such models, while at the same time offering the opportunity to detect deviationsfrom regulated practice. Relying on the scenario of organ donation basedon the Swedish legislation, we explore the merits of such an approach,and sketch the individual steps from policy documents to conceptualmodel. Supporting the methodological process, this paper employs theInstitutional Grammar 2.0, which offers selected features supporting theproposed modelling approach.

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.