Scenario discovery translates large simulation ensembles into interpretable input regions linked to policy-relevant outcomes. While previous studies have compared scenario discovery algorithms, they were ad hoc and hard to reproduce. We propose a general workflow to evaluate rule induction methods for scenario discovery. The workflow (i) provides synthetic benchmarks that expose axis and directional misalignment, nonlinearity, boundary fuzziness, and dimensional noise; (ii) unifies metrics and diagnostics around coverage–density trade-offs, interpretability, runtime, and scaling; and (iii) prescribes a staged experiment design from low-dimensional screening to stress testing. We illustrate the approach by comparing established algorithms PRIM and CART with an oblique decision tree variant called HHCART(D), finding that the latter does not outperform the former. Our workflow surfaces method-specific trade-offs and supports principled, reproducible algorithm selection for scenario discovery.

People around the world seek climate risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate risk services should provide, many expect noncommercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: Only four percent of the most-cited peer-reviewed climate risk studies in recent years fully share their data and code although this is a widely accepted minimum standard for transparency. We highlight low-cost measures that noncommercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate risk management.

Climate change and geopolitical tensions demand increased adaptability from crisis-response supply chains. Advanced information technologies, such as Artificial Intelligence and Blockchain, have been proposed as tools for increased adaptability. However, recent evidence shows that such technologies can also lead to new vulnerabilities and risks, a phenomenon we refer to as technology-induced maladaptation . This paper analyzes the effects of information technologies on resource allocation in crisis-response supply chains and identifies the conditions under which technology-induced maladaptation emerges. We develop an agent-based discrete-event simulation model in which managers adapt their resource allocations and coordination structures in response to climate shocks and geopolitical emergencies, using different information technologies. Using exploratory modeling and rule induction, we examine a sequential and a convergent supply chain configuration. Both configurations use different information technologies and are exposed to disruption patterns representing climate shocks and geopolitical emergencies. Rule induction reveals technology-disruption combinations under which technology-enabled efforts to reduce information delays and accelerate recovery may inadvertently decrease performance. The findings reveal two technology-induced maladaptation profiles: conditional and broad maladaptation. Conditional maladaptation refers to underperformance restricted to specific disruption duration windows, whereas broad maladaptation is distributed across the full range of disruption durations. The disruption pattern and supply chain configuration jointly moderate which profile a technology exhibits. Identifying these profiles advances understanding of maladaptation in crisis-response supply chains as a condition-specific phenomenon.

Supply chains are increasingly suffering from widespread disruptions, leading to growing interest in supply chain resilience. Adaptation, a central concept of resilience, is conventionally assumed to effectively manage these disruptions. However, actions aimed at adaptation can have adverse consequences, a phenomenon known in the climate change literature as maladaptation. Supply chain studies discuss only a few instances of maladaptation, without providing an overarching logic that explains why and how such maladaptation emerges. This study brings the concept of maladaptation into supply chain research by developing a conceptual framework that combines maladaptation theory from climate change literature with resilience theory from social–ecological systems literature. This framework identifies the dynamic mechanisms of cross-level interaction, delayed feedback, path dependence, and governance, through which maladaptation emerges. The framework explains how specific supply chain characteristics, such as compressed timeframes, coordination, and competition, accelerate these mechanisms. Through the conceptualization of maladaptation in supply chain management, this study advances resilience theory by establishing a framework to analyze when and under what circumstances adaptive actions aimed at enhancing resilience become maladaptive.

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.

Sustainable management of water resources is crucial for humanity. However, traditional methods for achieving this are becoming obsolete. This is because they are underpinned by the assumption that we have a good understanding of how water availability and demand will change in the future. However, based on our current experience with climate change, this is not the case. In fact, rather than having a good understanding of what the future might look like, it is, in fact, deeply uncertain. Consequently, a new paradigm for water resources management is needed; one that accounts for deep uncertainty by embracing scenario thinking. We categorize and summarize different causes of deep uncertainty in water resources management and provide examples of how an emerging paradigm rooted in scenario thinking can deal with these. We hope to stimulate discussion to enable this new paradigm to be developed further and embedded in standard practice.

The robust decision-making framework (RDM) has been extended to consider multiple objective functions and scenarios. However, the practical applications of these extensions are mostly limited to academic case studies. The main reasons are: (i) substantial cognitive load in tracking all the trade-offs across scenarios and the interplay between uncertainties and trade-offs, (ii) lack of decision-makers’ involvement in solution generation and confidence. To address these problems, this study proposes a novel interactive framework involving decision-makers in searching for the most preferred robust solutions utilizing interactive multiobjective optimization methods. The proposed interactive framework provides a learning phase for decision-makers to discover the problem characteristics, the feasibility of their preferences, and how uncertainty may affect the outcomes of a decision. This involvement and learning allow them to control and direct the multiobjective search during the solution generation process, boosting their confidence and assurance in implementing the identified robust solutions in practice.

Resilience is an increasingly popular concept in research and practice, but quantitative resilience analyses are often disconnected from resilience theory. For example, previous studies argue that diversity, a key attribute for building resilience, and agency, essential for understanding local adaptation and transformation, are critical to understanding resilience. Despite significant progress in integrating them into qualitative frameworks, diversity and agency are rarely incorporated into quantitative social-ecological resilience metrics. This omission is concerning, given the critical role of quantitative resilience metrics in informing resilience-oriented decision-making. This study examines how diversity and agency are represented in quantitative resilience metrics across disciplines, with the goals of (a) assessing how research on social-ecological resilience currently integrates these concepts into quantitative metrics and (b) identifying future opportunities to enhance their inclusion using insights from other fields. Using topic modelling to identify different research fields and facilitate the screening process, we performed a multidisciplinary systematic meta-review of resilience metrics. To understand what types of resilience metrics are used across disciplines and where diversity and agency are more commonly included, we identified six categories of resilience metrics, with “performance under disruption” being the most used category (35%). We found that a limited number of quantitative resilience metrics include diversity and agency, with “system structure” and “compound indicators” being the main sources of diversity and agency, respectively. We further reviewed simulation models applying resilience metrics. The prevalence of performance under disruption metrics is stronger than in reviews (67%) and a similar quantity of metrics including diversity (14%) and agency (5%) was found. Drawing on insights from multiple disciplines, we outline five potential pathways to improve the inclusion of diversity and agency in social-ecological resilience metrics: using network-based metrics, using response and pathway diversity, including diversity and agency in compound indicators, integrating quantitative methodologies outside resilience theory, and improving the application of resilience in simulation models.

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.

Decision-making in the context of the mobility transition requires considering complexity, many actors, and uncertainty about the future. So, choosing effective policies to achieve a more sustainable system is challenging. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to investigate the capabilities of decision-makers to interact with an agent-based model to explore various transport policies. This paper reports the results of two workshops with students exploring the mobility transition for a fictional version of a city in the Netherlands The participants made decisions in the role of either government or transport provider and evaluated the systemic impact of those decisions. We found that the participants were well-equipped to deliberate policy options under deep uncertainty using model simulations depicting a range of possible outcomes under different scenarios, embracing uncertainty in some respects and ignoring it in others. This study demonstrates the potential of participatory model-based exploration for mobility transitions to deliberate policy options under uncertainty using an agent-based model.

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.