Epidemics are long-lasting and transboundary crises that challenge traditional approaches. Given the complexity and interconnectedness of modern cities, interventions can lead to unintended consequences or maladaptation. Although adaptation is central to resilience, crisis management often focuses on short-term response, leaving a gap in understanding urban adaptation and maladaptation. This study examines the impacts of uniform interventions across diverse urban districts to assess this (mal)adaptive process. We use the COVID-19 pandemic in The Hague, Netherlands, as a case study, employing a large-scale agent-based model. We find that without an intervention, the high-contact city centre becomes an infection hotspot due to the transient population it attracts. Conversely, the outer residential district, with fewer amenities, experiences infections primarily among its residents. A uniform lockdown policy significantly reduces infections in the city centre by limiting mobility and social interactions, but inadvertently increases risk in the outer residential district. Using the Urban Adaptation Index (UAI), we demonstrate that these uncontextualised policies can constitute maladaptation, confirming the unintended consequences of ’one-size-fits-all’ approaches. Our results underscore this need, leading us to propose an updated, equity-oriented crisis management framework that accounts for the heterogeneous nature of modern cities.

Data on supply chains is often sparse due to reluctance among actors to share their data, making supply chain simulation modeling difficult. As a result, supply chain simulation models suffer from parametric and structural uncertainties, and there is a large variety of plausible simulation models that would align with the sparse observations about the real-world supply chain. Constructing a diverse set of models that fit sparse data is not an easy task. A relatively unknown approach to generating this diverse set of plausible models is the Quality Diversity (QD) algorithm. This study evaluates the feasibility of using QD to generate a diverse ensemble of supply chain simulation models for a varying degree of data sparseness. The results show that QD is able to generate a diverse ensemble of supply chain models, including the ground truth. As expected, QD successfully identifies the structure of the ground truth most frequently for a low level of data sparseness. When the sparseness of the data increases, QD is prone to overfitting, identifying supply chain structures that are more complex than the ground truth. Further research should focus on reviewing the calibration metric for sparse data, to reduce the overfitting of complex network structures.

Urban resilience and vulnerability are often paired conceptually, but the dynamics of their relationships are rarely tested with space-time-based data. We tracked the 2019 European heatwave across Amsterdam, Rotterdam, and The Hague, combining hour-by-hour ambulance calls with district profiles identified from demographic, socioeconomic, health, and built environment attributes. We find significant differences in the factors driving vulnerability. The familiar rule of ‘more vulnerable, less resilient’ only partially holds: some vulnerable districts showed high resilience at particular times of the day, while seemingly less vulnerable districts showed low resilience. These swings point to the importance of local adaptive behaviours and urban social fabric in shaping dynamic vulnerability-resilience relationships. Our findings call for dynamic, district-specific planning: vulnerability assessments must look beyond averages, and resilience measures should flex with daily rhythms. Effective heatwave policy demands context-aware tools that treat resilience and vulnerability as intertwined, shifting properties of the urban social fabric.

The police control room determines where to send available police units to intercept a fleeing fugitive. Models can support the police with decision-making for fugitive interception. The police have, at most, a few minutes to determine an interception strategy. Therefore, a timely calculation of the interception positions is essential to support police interception operations. The number of nodes in the network, each being a crossing where routes of the fleeing suspect can split, greatly contributes to the computation time. Graph coarsening is a promising approach to reduce the complexity of the network, and therefore the computation time. We compare four graph coarsening algorithms on five road networks and assess their impact on computation time and solution quality for the fugitive interception problem. Based on the comparison, we propose and test a new method specifically for fugitive interception. This method, Search Space Representation, improves the quality of the best solutions obtained by the optimization algorithm with up to 12%, improves the reliability of the optimization to find high-quality solutions, and decreases the number of function evaluations required to obtain high-quality solutions to 5000–10,000 depending on the size and complexity of the road network, which is feasible for real-time decision-making. Search Space Representation can be applied to reduce the computation time of other network-based optimization problems.

One of the tasks of police is catching fleeing suspects, where the police interception positions depend on the fleeing suspect’s route choices. Various conceptualizations of route choice decision-making of fleeing suspects exist. However, we do not know the effects of these different models of fugitive behavior on the calculated police interception strategy. Therefore, we operationalize two models of route choice and implement these in a simulation. Police interception strategies are obtained by optimization. The resulting sets of routes and the calculated police interception positions are subsequently compared and interpreted. The experiments show that the different route-choice models result in different escape routes and, therefore, different calculated police interception positions. The differences are larger when the road network is complex and contains non-uniform obstacles. In other words, the robustness of the calculated police interception positions for each model largely depends on the network topology.

Responsible AI (RAI) governance is increasingly understood not as a static checklist of principles, but as a dynamic process embedded in institutional, organisational, and sociotechnical contexts. While several ethical frameworks exist, translating high-level principles into situated organisational practices remains challenging. Empirical studies examining how public sector organisations operationalise RAI remain fragmented, limiting cumulative insights. To address this gap, we conduct a realist synthesis review of 21 empirical studies. Our analysis shows that similar interventions in different contexts activate distinct mechanisms and produce divergent outcomes with varying degrees of alignment to RAI principles. From these variations, we identify three cross-cutting dynamics explaining outcomes: organisational embeddedness, power- expertise tensions, and trust-transparency relationships. Together, we term it the situated dynamics of RAI governance. This approach moves beyond asking whether interventions “work” to explain why similar interventions succeed in some contexts and fail in others.

Urban vulnerability is affected by changing patterns of hazards due to climate change, increasing inequalities, rapid urban growth and inadequate infrastructure. While we have a relatively good understanding of how urban vulnerability changes in space, we know relatively little about the temporal dynamics of urban vulnerability. This paper presents a framework to assess urban vulnerability over time and space to address this gap. We apply the framework to Amsterdam, Rotterdam, and The Hague, the Netherlands. Using high-resolution, anonymised ambulance calls and socio-economic, built environment, and proximity data, we identify three temporal patterns: ’Midday Peaks’, ’Early Birds’, and ’All-Day All-Night’. Each pattern represents a unique rhythm of risk arising from the interaction of people with diverse demographic and socio-economic backgrounds and the temporal flow of their daily activities within various urban environments. Our findings also highlight the polycentric nature of modern Dutch cities, where similar rhythms emerge in areas with varying population densities. Through these case studies, we demonstrate that our framework uncovers the spatio-temporal dynamics of urban vulnerability. These insights suggest that a more nuanced approach is necessary for assessing urban vulnerability and enhancing preparedness efforts.

Illicit supply chains for products like counterfeit Personal Protective Equipment (PPE) are characterized by sparse data and great uncertainty about the operational and logistical structure, making criminal activities largely invisible to law enforcement and challenging to intervene in. Simulation is a way to get insight into the behavior of complex systems, using calibration to tune model parameters to match its real-world counterpart. Calibration methods for simulation models of illicit supply chains should work with sparse data, while also tuning the structure of the simulation model. Thus, this study addresses the question: “To what extent can various model calibration techniques reconstruct the underlying structure of an illicit supply chain when varying the degree of data sparseness?” We evaluate the quality-of-fit of a reference technique, Powell's Method, and three model calibration techniques that have shown promise for sparse data: Approximate Bayesian Computing, Bayesian Optimization, and Genetic Algorithms. For this, we use a simulation model of a stylized counterfeit PPE supply chain as ground truth. We extract data from this ground truth and systematically vary its sparseness. We parameterize structural uncertainty using System Entity Structure. The results demonstrate that Bayesian Optimization and Genetic Algorithms are suitable for reconstructing the underlying structure of an illicit supply chain for a varying degree of data sparseness. Both techniques identify a diverse set of optimal solutions that fit with the sparse data. For a comprehensive understanding of illicit supply chain structures, we propose to combine the results of the two techniques. Future research should focus on developing a combined algorithm and incorporating solution diversity.

Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception. The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time. We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.

Schedule design in the transportation and logistics sector is a widely studied problem. Transport service providers, such as the train industry and aviation, aim for schedules to be on-time according to the planning (i.e., on-time performance or OTP) in order to increase the service level by ensuring that passengers actually make their connections and to reduce costs. Transportation services also aim for schedules that serve a high variety of destinations and frequency of connections (i.e., connectivity). OTP and connectivity are both highly dependent on buffer time: more lucrative connections can often be offered by reducing the buffer time in the schedule, while more delay can be absorbed by more buffer time. Given strict constraints on the minimum turnaround time of aircraft and minimum (and maximum acceptable) transfer times of passengers, assigning buffer time in an already tightly planned schedule to optimize OTP and connectivity simultaneously is a big challenge. This research presents a novel multi-objective formulation of a daily flight schedule where buffer scheduling is used to ensure the optimal balance between OTP of the schedule and the passenger connections as connectivity, given the tight restrictions. This problem formulation is solved using a simulation–optimization framework. Specifically, we use the Multi-Objective Evolutionary Algorithm (MOEA) BORG. As a proof of concept, a daily European flight schedule of a large international airline is optimized on both OTP and connectivity. The results demonstrate that the presented multi-objective formulation and associated solving through simulation–optimization can result in candidate schedules with both better on-time performance and a higher connectivity.

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review of supply chain visibility and data quality literature, this study proposes to characterize data sparseness as a lack of data quality across the entire supply chain, where data sparseness can be classified into three dimensions: noise, bias, and missing values. The quantitative analysis relies on a stylized simulation model of a moderately complex illicit supply chain. Scenarios are used to evaluate the combined effect of the individual dimensions from actors with different perspectives in the supply chain, either supply or demand-oriented. Results show that when a data sparseness of 90% is applied, supply chain visibility reduces to 52% for noise, to 65% for bias, and to 32% for missing values. The scenarios also show that companies with a supply-oriented view typically have a higher supply chain visibility than those with a demand-oriented view. The classification and assessment offer valuable insights for improving data quality and for enhancing supply chain visibility.

Model-based predictive signal control is a popular method to pro-actively control traffic and to reduce the effects of congestion in urban networks. In combination with structure-free controllers, which adapt signal settings in arbitrary order and combination (no imposed cycles), these predictive control methods have a high potential to increase system performance by adapting to individual vehicle patterns, which are increasingly available due to new technology. However, most of these control methods assume perfect predictions, while in practice there are prediction errors due to various reasons. In this paper, the sensitivity of the system performance to these prediction errors is analyzed, for an urban corridor with spillback. In a microscopic simulator, first the ideal world is created for the structure-free model-based predictive signal controller, in which perfect predictions are made and the controller can reach its optimal performance. Then prediction errors are introduced in this perfect world, distinguished in aggregation errors that arise using a macroscopic prediction model and biases that represent structural errors in the prediction model or in its demand and state input. The effects of these prediction errors on the system performance are analyzed, as a function of the prediction horizon and update frequency of the control system. The results show that, even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length. A high update frequency dampens the influence of prediction errors, enabling the structure-free controller to correct mistakes faster. However, there remains a significant performance loss due to aggregation errors and biases in the prediction model, indicating a promising performance gain of more reliable predictions and the incorporation of information on individual vehicles in future control applications. Moreover, for all model quantities one direction of the bias has more impact on the system performance than the other direction, indicating guidelines towards a more robust control system that suffers less from erroneous predictions.

Enabling technologies concerning hardware, networking, and sensing have inspired the development of context-aware IT services. These adapt to the situation of the user, such that service provisioning is specific to his/her corresponding needs. We have seen successful applications of context-aware services in healthcare, well-being, and smart homes. It is, however, always a question what level of trust the users can place in the fulfillment of their needs by a certain IT-service. Trust has two major variants: policy-based, where a reputed institution provides guarantees about the service, and reputation-based, where other users of the service provide insight into the level of fulfillment of user needs. Services that are accessible to a small and known set of users typically use policy-based trust only. Services that have a wide community of users can use reputation-based trust, policy-based trust, or a combination. For both types of trust, however, context awareness poses a problem. Policy-based trust works within certain boundaries, outside of which no guarantees can be given about satisfying the user needs, and context awareness can push a service out of these boundaries. For reputation-based trust, the fact that users in a certain context were adequately served, does not mean that the same would happen when the service adapts to another user’s needs. In this paper we consider the incorporation of trust into context-aware services, by proposing an ontological conceptualization for user-system trust. Analyzing service usage data for context parameters combined with the ability to fulfill user needs can help in eliciting components for the ontology.

Project Risk Management helps with achieving project objectives, especially those concerning cost, time and quality. In the current APM definition, project risks are uncertain events or circumstances that may or may not happen. Risks are either threats that hurt the project objectives, or opportunities that are positive for the project objectives. Over time, project professionals have expressed concern with the idea that risks can be ‘positive’, since it does not align with the thinking in the non-project world, where risks are always negative. There was also the feeling that opportunities are often not identified, or treated differently from threats.

Project Risk Management helps with achieving project objectives, especially those concerning cost, time and quality. In the current APM definition, project risks are uncertain events or circumstances that may or may not happen. Risks are either threats that hurt the project objectives, or opportunities that are positive for the project objectives. Over time, project professionals have expressed concern with the idea that risks can be ‘positive’, since it does not align with the thinking in the non-project world, where risks are always negative. There was also the feeling that opportunities are often not identified, or treated differently from threats.

Integrated Assessment Models (IAMs) vary widely in complexity and underlying assumptions. There have been considerable efforts to increase the complexity of IAMs for improved representation of socioeconomic and environmental outcomes. However, less attention has been given to the foundational assumptions of these models and their distributional consequences. These assumptions are fraught with deep and normative uncertainty and can significantly impact IAM projections. If these assumptions are not explicit, IAMs can perpetuate existing mistakes and exacerbate inequalities due to their black-box nature. This paper introduces a novel IAM called JUSTICE (Justice Universality Spatial Temporal Integrated Climate Economy) to explore the influence on distributive justice outcomes due to underlying modelling assumptions across model components and functions: the economy and climate components, and the damage and social welfare functions. JUSTICE is a simple IAM inspired by the long-established RICE and is designed to be a surrogate for more complex IAMs for eliciting normative insights. As illustrated in Figure 1, JUSTICE contains two distinct economic and climate sub-models, three damage functions, and four social welfare functions (SWFs), each based on fundamentally different assumptions. This modular structure enables JUSTICE to uncover assumptions with nontrivial normative and distributional consequences. Also, the simplicity of JUSTICE makes it suitable for assessing the consequences of these modelling assumptions under deep and normative uncertainty using MS-MORDM and EMODPS frameworks, promoting a more equitable approach to decision-making. Using JUSTICE, we investigate the effects of three SWFs—Utilitarianism, Egalitarianism, and Prioritarianism—on global temperature rise, with two levels of aggregation. We also explore the sensitivity of distributional outcomes for two different climate models. Our findings reveal that different assumptions lead to significantly distinct optimal abatement pathways, underscoring the importance of explicating assumptions and exploring their uncertainties to facilitate deliberation and identify common ground among policymakers with diverse perspectives.

The COVID-19 pandemic in Indonesia has led to a significant change in human mobility. It is also considered the most serious threat to the inter-island trade network since the economic crisis in 1998. Leveraging two-year historical port call data (covering 6,000 records in total) of Indonesian domestic cargo vessels from the Automatic Identification System (AIS), this study examines the spatiotemporal changes of maritime freight transport network connectivity and cargo shipping capacity throughout the COVID-19 outbreaks period. We constructed two directed graphs, one in 2019 and another in the 2020 period, based on 1,283 Indonesian domestic cargo ship trajectories that connect 25 main Indonesian ports through 370 links. This study calculated and compared the four metrics of complex network analysis, including assortative coefficient, average degree, betweenness centrality, and clustering coefficient, to figure out the shipping network pattern changes. The result shows that the network connectivity and its shipping capacity changed in 2020, although the national port call trend is not significantly different from 2019. Based on our observation, we notice that the network is shifting from a "main hub-and-spoke connection," which dominantly involves western Indonesia hub-ports structure, towards a "multi hub-and-spoke connection," which increases the ports centrality position in eastern Indonesia. We also analyzed the change of cargo shipping capacity in each link to reflect how shipping liner companies respond to the pandemics. The insights generated in this study are hoping to contribute toward more rapid, effective, and comprehensive responses to this unprecedented disruption.

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.

Drones performing an autonomous mission need to adapt to frequent changes in their environment. In other words, they have to be context-aware. Most current context-aware systems are designed to distinguish between situations that have been pre-defined in terms of anticipated situation types and corresponding desired behavior types. This only partially benefits drone technology because many types of drone missions can be characterized by situations that are hard to predict at design time. We suggest combining context-awareness and data analytics for a better situation coverage. This could be achieved by using performance data (generated at real-time) as training data for supervised machine learning – it would allow relating situations to appropriate behaviors that a drone could follow. The conceptual ideas are presented in this position paper while validation is left for future work.