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.

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.

Decision-making under Deep Uncertainty (DMDU) offers approaches to support robust, adaptive strategies for complex decision-making. However, practical uptake of DMDU remains limited, partly due to fragmented access to resources and a lack of an inventory of available tools. This study introduces a comprehensive catalogue of tools and resources. Through a structured survey and expert elicitation, we identify 28 resources and 16 tools that support DMDU research and practice and classify them using an established DMDU taxonomy. Our analysis reveals a focus on introductory guidance regarding theory and methods of DMDU application, with some bias toward water-related applications. Technical, method-specific resources on how to implement existing frameworks remain limited. Our results identify tools supporting all core DMDU components, though they highlight persistent scalability challenges. The resulting online catalogue provides a foundation for expanding the use of DMDU in practice and is intended as a living, community-driven platform.

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.

Non-Technical Summary. We are in a polycrisis – the entanglement of crises across multiple, interconnected global systems such as climate, health, and finance – that interact to produce harms significantly greater than the sum of their parts. We propose that, to mitigate and adapt to this polycrisis, strong systemic risk governance is required, and that just and effective governance requires principles. Principles help us to identify common values, provide a framework for decision-making, and lead the necessary societal change towards a shared vision, taking on increasing importance in an ever more complex and fragile world. Technical Summary. We are facing multiple crises, from risks across systems that are central to the safety and prosperity of humanity and ecosystems. Traditional planning and implementation have been based on command-and-control approaches with narrow objectives formulated within a constrained logic model. However, the polycrisis and addressing systemic risk require multiple objectives beyond narrow ones, which cannot address large-scale initiatives in complex, dynamic environments aimed at systems transformation. This requires a deep consideration of what objectives societies and organizations have and how they should meet them. The notion of utilizing a set of guiding principles is critical. Principles are becoming ever more prominent in considerations around the different ways in which societies, organizations, and individuals operate. Principles take on increasing importance in an ever more complex world where our effectiveness depends on adapting to context, guiding adaptation, and facilitating dialogue on options, trade-offs, and choices. We propose a set of 10 principles to guide the development of the field of systemic risk assessment and response within and across multiple domains. These principles – developed to meet the needs of the field of systemic risk – provide a complete set of operating guidelines to drive towards safety, equity, and security for human and ecological systems. Social Media Summary. This article proposes 10 principles for systemic risk governance to navigate the polycrisis and ensure a safe future.

Securing the availability of enough metals to fulfill demand is a critical societal concern. Models of metal supply systems can help enhance our understanding of these systems and identify strategies to reduce material criticality and improve resilience. In this work, we introduce a novel approach to modeling metal supply systems, using nickel as a case study. Our approach combines system dynamics modeling, in which various feedback loops influence future outcomes, with the higher sectoral and geographical detail of industrial ecology (IE) methods and data on individual mines. We also include extensive uncertainty analyses through exploratory modeling and analysis. Using this combined modeling approach, we explore the development and resilience of the global nickel supply system between 2015 and 2060 under various uncertainties and policy levers. Our results show that incorporating feedback effects leads to more realistic demand behavior and resource depletion patterns compared to traditional dynamic material flow analysis. Market feedback enhances resilience, but cannot fully offset criticality risks. Sectoral disaggregation reveals increased criticality risks due to the energy transition, which can be mitigated by increasing opportunities for substitution, product lifetime extension, recycling, exploration, capacity expansion, and by-product recovery. Geographical disaggregation highlights the resilience benefits of diverse supply sources, as well as the effects of changing regional market shares on sustainability impacts, ore grade variability, and by-product dynamics. Our combined modeling approach is a step toward prospective, dynamic criticality assessment, in which system changes and future risks are accounted for when determining material criticality and policy recommendations.

Humanitarian and military organizations face deeply uncertain, continuously changing environments due to disasters and conflict. Information sharing is vital to adapt to these disruptions effectively and ensure the timely availability of essential equipment and supplies anywhere in the world. However, little is known about the role of information sharing in adapting to a changing environment. We use an agent-based discrete-event simulation to study information-sharing mechanisms, specifically delayed information-sharing behavior, and analyze how they impact the adaptation of decision-making structures over time. Drawing upon adaptation models from literature, we develop a model in which agents share information and endogenously create these structures. We experiment with various levels of information delays in dynamically changing environments and assess how this affects adaptation and performance. Our findings unveil that horizontal delays lead to earlier hierarchical expansion and vertical delays slow decision-making in crisis response environments.

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.

Geopolitical tensions and conflicts can disrupt energy markets, threatening international energy supply security and imposing financial stress on energy-intensive industries reliant on imported fossil fuels. Exploring the challenges and opportunities associated with supply diversification is crucial for understanding the potential for hard-to-abate industry decarbonization under the risk of future energy price shocks. In this context, we investigate the role of green hydrogen as a viable and sustainable alternative to natural gas applications in iron and steel manufacturing. We first quantify how the integration of green hydrogen into the existing infrastructure can complement stringent climate action ambitions in reducing CO2 emissions over the next five decades. We find that green hydrogen acts as a transitional technology, enabling a gradual shift towards electrification of heat supply while bridging the gap until low-carbon steel technologies become commercially feasible. Furthermore, we assess the benefits of timely green hydrogen investments in mitigating the economic repercussions of unforeseen natural gas price surges. Overall, this study underscores the potential of green hydrogen in decarbonizing the iron and steel industry while promoting energy independence, but it also highlights its contingency on sufficiently ambitious climate policies and adequate technological advancements.

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.

We analyse shock and parameter uncertainty in a Dynamic Stochastic General Equilibrium (DSGE) model by exploratory modelling and analysis (EMA). This method evaluates in a novel way the performance of monetary policy under deep uncertainty about the shock and model parameters. Scenarios are designed based on the outcomes of interest for the policymaker. We assess the performance of different policies on their objectives in the scenarios. This maps out the policy trade-offs and supports the central bank in making robust policy decisions. We find that in response to a negative supply shock, policies with low interest rate smoothing and a strong response to inflation most obviously contribute to price stability under deep uncertainty.

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.

Human societies and ecological systems face increasingly severe risks, stemming from crossing planetary boundaries, worsening inequality, rising geo-political tensions, and new technologies. In an interconnected world, these risks can exacerbate each-other, creating systemic risks, which must be thoroughly assessed and responded to. Recent years have seen the emergence of analytical frameworks designed specifically for, or applicable to, systemic risk assessment, adding to the multitude of tools and models for analysing and simulating different systems. By assessing two recent global food and energy systemic crises, we propose a methodological framework applicable to assessing systemic risks in a polycrisis context, drawing from and building on existing approaches. Our framework’s polycrisis-specific features include: exploring system architectures including their objectives and political economy; consideration of transformational responses away from risks; and cross-cutting practices including consideration of non-human life, trans-disciplinarity, and diversity, transparency and communication of uncertainty around data, evidence and methods.

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.

Climate policy faces increasingly complex challenges that span multiple human decision scales in nature-society systems. Contemporary climate policy models, while valuable and increasingly versatile in handling spatial and temporal scales, struggle to capture interacting multiscale decisions on the socioeconomic side. This perspective draws attention to the power of coupling among different modeling families, taking integrated assessment models (IAM), computable general equilibrium models (CGE), and agent-based models (ABM) as examples. Recent computational advances, maturity of models, availability of data, and interdisciplinary expertise make model coupling an increasingly feasible, effective, and useful tool for climate policy analysis. We examine the unique contributions of each modeling approach, highlight synergies from uniting their strengths, and discuss alternatives to and conditions for coupling. In addressing methodological challenges, we present examples of effective coupling of IAM-ABM-CGE, emphasizing the importance of maintaining model integrity while enhancing policy relevance. By bridging human decision scales and leveraging complementary strengths, coupled models can provide nuanced insights into climate-economy interactions, ultimately supporting effective and equitable-not just efficient and optimal-climate policies.

Mesopelagic fishes are a vital component of the biological carbon pump and are, to date, largely unexploited. In recent years, there has been an increased interest in harvesting the mesopelagic zone to produce fish feed for aquaculture. However, great uncertainties exist in how the mesopelagic zone interacts with the climate and food webs, presenting a dilemma for policy. Here, we investigate the consequences of potential policies relating to mesopelagic harvest quotas with a dynamic social-ecological modeling approach, combining system dynamics and global sensitivity analyses informed by participatory modeling. Our analyses reveal that, in simulations of mesopelagic fishing scenarios, uncertainties about mesopelagic fish population dynamics have the most pronounced influence on potential outcomes. The analysis also shows that prioritizing the development of the fishing industry over environmental protection would lead to a significantly higher social cost of climate change to society. Given the large uncertainties and the potential large impacts on oceanic carbon sequestration, a precautionary approach to developing mesopelagic fisheries is warranted.

Many European cities are investigating how to transition to climate-neutral transport systems. Due to the transport system's complexity and uncertainty about the future, identifying drivers and choosing effective policies to make the city more sustainable is challenging. Additionally, the chosen policies need to be supported by relevant actors. This study aims to support the municipality of The Hague in generating robust policies supported by and within the municipality. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to address this goal. In two workshops, the participants formulated goals and objectives, created Causal Loop diagrams, and identified potential interventions. Using a set of possible futures, the interventions were then stress-tested to evaluate their robustness. By explicitly linking, for the first time, participatory modeling and decision-making under deep uncertainty approaches, the participants could understand the system better and deal with uncertainty. Participants gained insight into systemic complexity and methods to deal with it, the inter-relatedness of interventions and their effects, and a shared understanding of the problem and its scope. This study demonstrates the potential of a novel approach to generate supported robust interventions to achieve the goal of a climate-neutral transport system.

Tin is an important metal for society with a high risk of supply disruptions. It is, therefore, classified as a critical material in many parts of the world. An exception is the European Union, for which tin was classified as a non-critical material in 2023. However, there are many discrepancies in the literature regarding the definitions and values of the indicators used to determine tin criticality in general, and recycling indicators in particular. Values for end-of-life recycling rate (EoL RR) range between 20% and 75%, and values for end-of-life recycling input rate (EoL RIR) range between 11% and 32%. In this paper, we critically assess the circularity and criticality indicator values for tin and calculate new values using material flow analysis. The new values for tin recycling indicators are lower than those used in most previous research, with a global EoL RR of 16% and an EoL RIR of 11% in 2017. Based on the updated recycling values, combined with a highly concentrated supply, high import reliance, and difficult substitution, we argue that the European Union should classify tin as a critical material. This reclassification can lead to more policy attention for tin, which can help reduce the impact of future supply disruptions and increase the resilience of the European and global tin supply chains.