The Dutch electricity grid is currently facing unprecedented net congestion, with a backlog of industrial connections and estimated social costs ranging from €10 to €40 billion per year. Distribution System Operators (DSOs) like Stedin must make substantial infrastructure investments to mitigate net congestion. However, traditional Electricity Distribution Expansion Planning (EDEP) methods are often too deterministic and static, failing to account for deep uncertainty regarding changing energy policies, technological adaptations and different stakeholder perspectives. This thesis develops and applies a Robust Decision Making (RDM) based simulation model to stress-test a candidate investment plan at a masterplan scale, using Latin Hypercube Sampling to generate 10,000 plausible future scenarios. Through the Patient Rule Induction Method (PRIM), five key storylines driving capacity risks are identified: medium-to-fast residential heating transition, medium-to-fast electric vehicle growth, emergence of large datacenters, residential housing expansion combined with residential heating electrification, and high industrial electrification combined with electric vehicle growth. Based on these vulnerabilities, an adaptive investment plan is developed incorporating scenario-dependent mitigation measures, which substantially reduces capacity risks across all substations. The adaptive pathways are visualised using Dynamic Adaptive Policy Pathways (DAPP) through a MetroMap, supporting stakeholder dialogue. The results demonstrate that RDM based methods provide the analytical foundation necessary to develop robust and adaptive investment strategies for electricity distribution networks under deep uncertainty. ... Read more

This thesis investigates the comparative performance of three Multi-Objective Evolutionary Algorithms, Epsilon-NSGA-II, Borg, and Generational Borg, within the Evolutionary Multi-Objective Direct Policy Search framework, applied to the optimisation of the JUSTICE Integrated Assessment Model. By combining benchmark problems (DTLZ2 and DTLZ3) with the high-dimensional, multi-modal JUSTICE model, the study evaluates convergence dynamics, computational efficiency, and solution quality across varying levels of parallelisation. Results show that Borg consistently outperforms the other algorithms, particularly under high problem complexity, due to its asynchronous, steady-state architecture and adaptive operator features. Generational Borg performs better than Epsilon-NSGA-II on complex problems, but both suffer from scalability limitations due to synchronous execution. These findings underscore the importance of algorithm selection in climate-economy policy modelling, offering guidance on how MOEA design and computational resources interact to affect optimisation outcomes. ... Read more

Transitioning to a more sustainable mobility system is a challenging task due to the complexity of the system, the presence of multiple actors with diverging views on the problem, the uncertainty about the system's current state, how the future might unfold and affect the system, and what values to consider. This research contributes a novel approach to address the wicked problem of transitioning to more sustainable mobility. We build on transition modeling, stakeholder engagement, and decision-making under deep uncertainty (DMDU) to enable decision-makers to frame the problem, model the system, generate policies, and deliberate these policies while taking uncertainty into account. By designing, executing, and evaluating DMDU processes involving stakeholders, we make the first steps to establish a practice of participatory DMDU. ... Read more

Even in this digital era, the data for improving supply chain visibility is often sparse due to the actors’ reluctance to share information or because of illicit activities. This dissertation demonstrates a simulation approach for reconstructing illicit supply chains with sparse data, that could help robust decision-making on effective interventions. We use a simulation calibration approach in combination with a ground truth simulation model of a stylized counterfeit Personal Protective Equipment (PPE) supply chain as case study. ... Read more

Fugitive interception is a challenging task, requiring the police control room to decide in a matter of minutes on the optimal positions of police units to intercept a fleeing suspect. Models can support the police by suggesting interception positions. This dissertation addresses four barriers to using models for decision support in fugitive interception: timely simulation-optimization, effective representation of the search space, simulation of fugitive behavior, and adaptation to information updates. ... Read more

Poor and coastal regions are increasingly at risk from the effects of climate change. These risks are accompanied by a high level of uncertainty, which is also called deep uncertainty. Current planning approaches lose efficacy under deep uncertainty, necessitating new approaches that function better under these conditions. Decision making under deep uncertainty (DMDU) is the name for the family of approaches that attempt to deal with this level of uncertainty. Bangladesh, the Netherlands, and New Zealand have all already adopted the use of DMDU techniques. Research into local implementation of these techniques is also being done.

Two DMDU techniques often deemed as complementary are robust decision making (RDM) and dynamic adaptive policy pathways (DAPP). RDM can be seen as a computational extension of scenario planning, where proposed plans are tested against every potential combination of uncertainties. DAPP is a flexible policy framework that allows decision-makers to keep long-term plans in mind while making short-term decisions. DAPP especially has seen increased adoption in national delta protection plans such as in the Netherlands, Bangladesh, and New Zealand.

To design DAPP, currently a combination of many-objective robust optimization (MORO) and participatory processes are used. These methods both have their own issues. MORO requires the upfront specification of rules and policies and is computationally expensive, while the participatory approach is qualitative and can be insufficient when dealing with complex systems. RDM is seen as a potential improvement in supporting the DAPP policy structure in two main ways. First, RDM can be used to iteratively develop and/or stress-test potential actions and pathways. Second, the vulnerabilities identified through RDM can be used to lay the base for a monitoring system by identifying promising signposts and signals.

While RDM is seen as a potentially helpful tool to support DAPP, there is a lack of studies that have established a systematic and analytical approach which uses the robust decision making process to support the development and monitoring of DAPP. This research proposes a novel approach based on literature to achieve this. The approach uses the vulnerabilities identified through RDM to iteratively inform and develop more robust actions and to lay the basis for the technical side of a monitoring system. This approach is then illustrated by way of the adaptation case of a wastewater treatment plant in Helensville, New Zealand. This wastewater treatment plant serves a small community and will have to retreat at some point in the future due to increasing risks from compound flooding, which are exacerbated by rising sea levels.

The results of the case illustration show the benefits of using RDM to better understand vulnerabilities in the system in two main ways. First, the vulnerability analysis (which included a sensitivity analysis) helped to identify factors most important to the outcomes to inform potentially effective actions. Second, RDM helped in the development of the monitoring system. Those factors making up the identified vulnerabilities formed the basis of the technical signposts selected. Using the coverage-density tradeoff from the scenario discovery results, promising signals could be selected, although timing was not taken into account. This could potentially partially solve a common problem for monitoring DAPP: the selection of trustworthy signals.

There were three main recommendations. The first is to further work through a case such as this, since due to time constraints only the first iteration of the process was followed in this research. This could help identify more potential benefits or issues. A main issue here is also how to identify when an action is fully developed, as the process could continue indefinitely. Second, it is recommended to do further research into determining adaptation tipping points using other scenario discovery methods, and to use the coverage-density tradeoff from the scenario discovery results to modify adaptation tipping points based on policy regret. Third, is to further the monitoring system by posing open questions to support the deliberation on signpost and signal selection, taking timing into account to identify triggers, and by adding a signpost map next to the signal map to visualize signpost interaction, hierarchy, and quality.
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Advances in supply chains together with more turbulence in today's business environments, increase the vulnerability of supply chains to disruption. Various cases of supply chain disruptions in recent decades have revealed that besides focusing on efficiency, improving resilience is also crucial for a supply chain to be competitive in the marketplace. The first step in improving supply chain resilience, or in general, improving supply chain risk management, is to identify and assess risks. The approaches toward assessing risk mostly depend on the availability of information about the disruptive risks. These approaches assume a known probability of occurrence of a disruptive risk and its magnitude, or the methods provide ways to estimate these risk parameters. Although probability‐based approaches toward risk modeling work fine for common supply chain disturbances or operational risks, they cannot deal with less frequent or unknown disruptions. In this research, we suggest a consequence-based approach toward the supply chain risk analysis. Instead of focusing on identifying the root cause of risk and determining its characteristics, such as the probability of occurrence and intensity, one concentrates on analyzing the negative impact of the disruption on supply chain performance. A big challenge for performing an efficient consequence-based risk analysis of a supply chain is analyzing a comprehensive set of disruption scenarios that the supply chain may encounter in the future. Also, resilience practices for overcoming supply chain disruptions must be effective under as many disruption scenarios in the future as possible. To address these challenges, we draw on the paradigm of decision making under deep uncertainty (DMDU). This paradigm emerged in the context of climate change adaptation in response to limits to predictability. A foundational idea in this paradigm is to apply models for exploring rather than for predicting the future. Robust decision making (RDM) is one of the approaches in DMDU that focuses on identifying planning strategies that result in satisfactory outcomes across a large set of scenarios regarding the future. We investigate the potential of using RDM for supporting consequence-based risk analysis of a supply chain.
Network-level stress testing of a supply chain plays an important role in resilience practices. However, the common graphical perspective on the structure of a supply chain network, where different network structures are represented by a configuration of nodes (facilities) and links (transport), may ignore key operational aspects of the supply chain that can affect resilience. We proposed an approach in which we compared the resilience of several supply chain structures that differ in three operational aspects including product structure, sourcing strategy, and production strategy.
A successful supply chain resilience practice depends on reliable stress-testing of the supply chain. A reliable stress test of a supply chain depends on the availability of information about the supply chain structure, where relations among different actors of the supply chain are clear. However, the complexity and globalization of today's supply chains make it difficult for decision makers to access data and information, especially from actors more than a few tiers upstream or downstream. Also, as a dynamic system, a supply chain is confronted with changes in structure and configuration over time. Here, there is a need for an approach for stress testing a supply chain that can deal with a lack of information about the structure of the supply chain. We proposed ensemble modeling as a solution. Rather than focusing on a single supply chain structure, this approach involves generating and analyzing an ensemble of structures to identify vulnerability sources requiring resilience practices. ... Read more

Cardiovascular diseases (CVDs) are a leading cause of death globally, causing 38% of all premature deaths. Providing care for the patients who survive is considered a priority for healthcare managers. In the Netherlands, 1.7 million people currently are diagnosed with cardiovascular diseases. After experiencing and surviving a CVD event, patients require support to get their lives back on track. However, over the next years, the number of patients is expected to increase to 2.6 million people, creating a burden on the healthcare system. Moreover, the costs of healthcare have been increasing over the last few years, decreasing the accessible and affordable for the patients to receive care. Intervention is needed to keep the quality of life of patients high. However, most interventions focus on the prevention of CVDs, with no attention to patients that already diagnosed with cardiovascular diseases. This research presents a model for the support of patients with cardiovascular diseases within the Netherlands. It is done in conjunction with Harteraad, a non-profit Dutch patient organisation that represents the interests of the 1.7 million patients. The model aims to explore the effect of interventions on the lives of patients while keeping the investment cost-efficient. To successfully answer the research question, four steps were taken. First, a literature study was conducted to find potential candidate interventions that Harteraad can implement. The results from the literature study suggest that the priority lies in interventions on physical activity, mental health and medication adherence. In the second step, a causal loop diagram (CLD) was built that maps the interventions to outcomes. For this research, the main outcome variable is the social returns on investment (SROI). SROI measures value creation on a social, environmental and economic level. Secondary outcomes include deaths, hospitalisations, and quality of life. All three of which are used to calculate the SROI. The CLD shows that the interventions affect the outcomes by improving patient behaviour. Whether that is in more exercise, seeking mental support or medical adherence. In the third step, a System Dynamics model was developed to simulate the economic effect of different interventions. Population data were retrieved from the Hartstichting, whereas data sources on input values were found in academic literature. The latter being a source of uncertainty, as healthcare systems across countries cannot be directly compared. So in the fourth step, uncertainties were incorporated into the SD model and tested systematically. The results for the simulation show that the SROI is positive for six interventions. As such, the recommendation is to invest in either of those interventions. More specifically, for physical activity, the efficient interventions are “Active-at-Home”, “Video Gaming”, and “Group-Based Training”. For mental health, the efficient interventions are “Care Coordination”, “Cognitive Behavioural Therapy”, and “Telemedicine”. Lastly, for “CombiConsult”, the recommendation is to offer additional mental support as well to create social return. Although the model is able to give insights into potential interventions, more research is required. The model is only a simplified representation of the real world. Data was either lacking or outdated, creating nuance in the model results. As such, the model cannot be used to give the optimal solution. These nuances thus need to be addressed in future research. Despite the limitations of the model, the research provides societal and scientific contributions. The research shows that system dynamics modelling is a suitable method to model the support of patients. Moreover, the model is able to determine if interventions are cost-efficient while increasing social benefits to the patient. This research furthermore adds a contribution by performing a systematic uncertainty analysis and so on, offering aid in robust decision-making. Lastly, the model is suitable to be used in different countries under different healthcare systems. As such, this research contributes to the research on patient support. ... Read more

Water, an essential resource for diverse purposes like environmental protection, urban water supply, energy generation, and agriculture, faces intensifying demand amid depleting supplies. Multi-Objective Optimization (MOO)-models are vital for addressing complex water system challenges with limited resources. However, varying approaches to distributive justice in these models introduce normative bias, leading to uncertainty in the derived implications from the model. This thesis is the first approach to understanding how the operationalization of distributive justice shapes the implications drawn from the 'optimal' outcomes of MOO-models. This thesis studied: 'How do different operationalization formulations for inequality in existing multi-objective optimization models shift the Pareto front?'. A rival framings approach acknowledges diversity in perspectives, for which it is suitable to contrast the operationalization formulation. The rival framing focused on the inequality metric and the aggregation method over time for this metric, both used for the formulation of inequality in the objective formulation. The case study revolves around the Conowingo Reservoir in the Lower Susquehanna River Basin. Utilizing an Evolutionary Multi-Object Direct Policy Search (EMODPS)-model, the research optimizes water allocation by incorporating an equality objective alongside baseline efficiency goals. Results underscore that the trade-off between equality and efficiency is highly dependent on the chosen operationalization for equality. Moreover, for higher levels of equality, the Pareto front will shift drastically in terms of strength and direction of trade-offs. By unravelling the complexities of justice's integration into MOO-models, advances are made in the comprehension of how distributive justice can inform decision-making. Through the elaboration of justice formulations, a future is reachable where justice is not only considered but reached.



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Celsius or 1085 GtC. This thesis explores this significance and points toward prioritizing ecosystem conservation as a highly effective strategy for mitigating climate change. Initiatives such as carbon offsetting, performed by organizations like ‘8 billion trees’ and ‘cool earth’, could provide feasible solutions to enhance carbon sequestration. The EU's Nature Restoration Law is a step in the right direction, advocating for the conservation of existing ecosystems and restoring deteriorated systems, specifically increasing organic stocks in forests and restoring and rewetting drained peatlands of agricultural use and peat extraction sites seem to be compelling aspects in terms of improving the carbon system proposed in this law. I encourage the exploration of similar policy changes globally, underscoring the importance of taking immediate action and ensuring no further deterioration of existing terrestrial carbon systems.

Following the importance of terrestrial carbon storage, the debate remains about who should sacrifice their land. This research has displayed a high sensitivity in land allocation for terrestrial carbon storage when comparing ethical perspectives of justness, also defined as climate justice principles. This sensitivity can be seen as normative uncertainties and represent potential tensions in developing a coherent global policy. In my research, certain countries are more sensitive to these normative uncertainties than others. The Netherlands, Pakistan, China, and Yemen are highly sensitive to terrestrial carbon storage obligations if islands and smaller states are not considered. In the case of the Netherlands, this sensitivity is caused by a high GDP and, therefore, ability-to-pay. Still, when following efficiency prioritized, Dutch agricultural land should be kept for its high agricultural productivity. China is expected to reach a high level of domestic food security. Therefore, they would have to sacrifice their land for terrestrial carbon storage according to a different interpretation of ability-to-pay. Also, the efficiency prioritized principle for China would be ideal because, with their economic growth and innovation, they are expected to reach a high level of agricultural productivity. In the case of Yemen, it is different, Yemen has low agricultural productivity, and efficiency-prioritized will lead to them converting agricultural land to forests. The best interpretation of ability-to-pay for Yemen is the available land that could be converted for terrestrial carbon storage.

In my analysis, I showed proof of principle to include these normative uncertainties in policy analysis to explore areas of consensus. Through this approach, I found countries that should convert or conserve their land for terrestrial carbon storage and countries that arguably could still replace their forests and wetlands for other land covers such as urban- and agricultural land. I propose that climate justice principles should be included as uncertainties in policy-analysis, thus arguing for – additional – simplified models to allow for easy implementation of these uncertainties in the analysis.

To come to these findings, I performed an extensive analysis of the global food-, land cover-, and carbon system, simulating a wide range of scenarios up to the year 2100. The model I have developed operates at a national-detail level and includes 173 countries worldwide. The model considers several factors for each country, such as socioeconomic development, current land cover, agricultural productivity, and food demand. The national systems interact globally to represent global systems such as food change and the carbon cycle.

I tested a wide range of distribution methods on this system for terrestrial carbon storage burden, represented by five policy levers. Additionally, I considered 21 uncertainties in the model and performed 10.000 experiments to ensure the robustness of the proposed policies. In the last phase of the analysis, I created world maps that display the land countries should convert for terrestrial carbon storage according to climate justice principles and the sensitivity per country. These maps allow for an easy understanding and interpretation of the climate justice principles.

I started the research by exploring frequently used climate change policy evaluation in ethics. Ethics literature discusses two main social justice types: distributive and procedural justice. In this study, I focus on distributive justice for its applicability in modelling solutions. I utilize pre-defined climate justice principles and introduce a new principle – efficiency prioritized, based on the utilitarian principle. The ‘principle efficiency prioritized’ allocates agricultural land to countries with the highest agricultural productivity and terrestrial carbon storage burden to the less-productive countries. Selected principles are Ability to Pay (based on available land for terrestrial carbon storage, domestic food supply, and GDP per capita), You-Broke-It-You-Fix-It (based on historical emissions), and Efficiency Prioritized, with different interpretation methods considered for Ability to Pay.

I modeled the system using Vensim, a System Dynamics modeling software. System Dynamics is a quantitative modeling formalism suitable for dealing with complex systems, allowing for exploring relations between system structure and behavior. It helps to understand the complex land use, land-use change, and forestry, food, and carbon system. It also enables the incorporation of parametric and structural uncertainties, testing a wide range of scenarios in systems with deep uncertainty. The model consists of several sub-systems, leveraging existing models from prior research by Auping (2018), and takes data from sources such as the World Bank, FAOSTAT, OECD, and IPCC as input.

To find robust policies in the face of deep uncertainty, I adopted an Exploratory Modelling and Analysis (EMA) approach using the open-source EMA workbench for Python. This approach involves performing a broad range of computational experiments, analyzing the results, and identifying robust policies based on these findings. EMA workbench has a special connection built-in to run experiments with Vensim efficiently. It includes built-in functions for sampling, such as Latin Hypercube Sampling, and scenario discovery, such as PRIM, used in my analysis to find the scenarios of interest.

I visualized the results with Basemap, NumPy, Pandas, and the EMA workbench, among other tools. Creating maps allows for a simple data representation, enhancing understanding and communicating the findings. This approach enables a thorough exploration of climate justice principles, modeling, and experimentation, providing valuable insights for decision-makers facing complex and uncertain climate change challenges.

To summarize, I applied System Dynamics modeling and scenario discovery to explore the global food and carbon system. I proved the importance of terrestrial carbon storage, especially conserving existing forests and wetlands. Terrestrial carbon storage policy also faces policy challenges in implementation, mainly the distribution of the terrestrial carbon storage burden. Because land for terrestrial carbon storage goes at the cost of agricultural and urban land that could provide economic prosperity and food security, there is a trade-off between different Sustainable Development Goals. Several distribution conventions have been evaluated following climate justice principles to find overlapping policies between the principles. I found a significant difference in the distribution of terrestrial carbon storage obligations between the principles, displaying potential complexities and tensions in policy-making. I also found consensus between the principles, defined as non-discriminatory policies. Policy-makers should start with non-discriminatory policies to ensure swift implementation. I recommend that other policy modelers also include ethical perspectives as uncertainties in their models to find non-discriminatory policies and, if necessary, develop simplified models to enable this.
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Addressing the global challenge of water scarcity, particularly in the context of Sustainable Development Goal 6, underscores the critical need for effective water resource management. Central to this management is the control of dams in river basins, which poses complex decision-making challenges often labeled as wicked problems. Many-Objective Optimization, a powerful tool in modeling water systems, grapples with conflicting objectives, and the complexity heightens when integrating Distributive Justice principles. Distributive Justice, aiming to equitably distribute resources, is crucial in the context of water distribution for reservoir management. While current modeling approaches predominantly apply Distributive Justice principles A Posteriori, this study focuses on their A Priori incorporation, evaluating their impact on trade-offs and decision-making processes within the framework of Many-Objective Optimization.The investigation delves into Utilitarianism, Egalitarianism, and Prioritarianism, representing varied Distributive Justice perspectives. These frameworks are translated into mathematical models to provide insights into how societal values influence water resource allocation. The research centers on managing contentious shared water resources within the Eastern Nile River Basin. A comparative analysis between A Priori and A Posteriori integration of Distributive justice principles reveals distinct Pareto-optimal trade-offs. The findings underscore significant differences between a priori and a posteriori approaches, with Utilitarian integration enhancing overall system performance, egalitarian integration diversifying solutions, and prioritarian integration yielding sharper trade-offs. This study contributes valuable insights to the ethical considerations in many-objective optimization. It also offers guidance for policymakers and water resource managers by providing clear terminology to discuss and evaluate different approaches that are fitting within the policymaker's scope. ... Read more

The central bank plays a crucial role in maintaining price stability, as high inflation or deflation can lead to economic instability. One of its main tools is the adjustment of interest rates to keep inflation within a desired range, particularly in response to economic shocks. To determine optimal interest rate policies, central banks often use Dynamic Stochastic General Equilibrium (DSGE) models, which simulate the behavior of consumers, firms, and the central bank while simplifying the economy to two key variables: inflation and the output gap. The output gap represents the difference between actual and potential output, while inflation measures the change in prices over time. These models incorporate economic features such as price stickiness, expectations, consumer substitution between goods, and the effects of specific shocks.

Interest rate decisions are influenced by three components: the previous period’s interest rate, current inflation, and the current output gap. While DSGE models help identify effective policies, uncertainties regarding model parameters and shock characteristics can limit their accuracy. To address this, this research applies Exploratory Modeling and Analysis (EMA), which involves running large numbers of experiments across a range of parameter values to observe how different assumptions affect economic outcomes. EMA enables robust policy design by examining the effects of parametric and shock uncertainties on model predictions.

Experiments revealed that the most influential parameters in response to a positive demand shock are the share of firms able to adjust prices, the central bank’s conservativeness in adjusting interest rates, and the persistence of the shock. Large deviations and oscillations in inflation and output gap occur when a high share of firms can adjust prices while the central bank reacts conservatively. In contrast, when price stickiness is high, inflation remains moderate but the output gap is positive, indicating stronger demand relative to supply. Negative supply shocks primarily affect the magnitude of deviations rather than creating distinct behavioral patterns.

EMA also allows for behavior-based scenario discovery using clustering techniques to identify parameter combinations associated with extreme or undesirable economic behavior. This is complemented by multi-objective optimization, which seeks policies that minimize deviations and fluctuations in inflation and output gap while keeping inflation within acceptable limits. Pareto optimal policies are evaluated for robustness across a wide range of uncertain economic states. The results indicate that policies which prioritize responsiveness to current inflation and reduce the influence of past interest rates perform best in controlling inflation, although they may lead to larger deviations in output gap.

Overall, EMA provides significant added value to DSGE modeling by revealing which parameters are most critical to economic behavior, highlighting trade-offs between policy objectives, and offering methods to design robust policies under uncertainty. While DSGE models simplify the economy and cannot capture all real-world dynamics, combining them with EMA enables central banks to better understand risks, assess alternative policies, and design strategies that are resilient to both parametric and shock uncertainties. Future research could apply EMA to more detailed DSGE models or real-world data, while ensuring validity and robustness in practical policy contexts. ... Read more

In the pursuit of low-carbon technologies and sustainable alternatives to fossil fuels essential to hand on to future generations a safe and livable planet, hydrogen has attracted engineering interest as a promising vector for storing and carrying clean energy that would help decarbonize society. Scientific research and technological trends indicate that heavy industries, especially steelmaking, might represent an excellent opportunity to consistently integrate the consumption of hydrogen into the manufacturing processes. This is likely to deliver substantial emissions savings as the steel industry, widely recognized to be a hard-to-abate sector, accounts for a large portion of the global pollution every year.
Besides the heated debate on the technical and financial challenges related to deployment constraints and cost increases, the geopolitical risk of energy crises impacting the economic output of industries in energy-importing countries is neglected in the decision-making considerations, overlooking the critical implications of energy diversification on long-term planning.

The aim of this study is thus to investigate the competition between hydrogen and natural gas for high-temperature heat generation in steel manufacturing while considering the potential impact of energy shocks on fuel expenditures. Addressing these dynamics would have significant consequences for the interplay between policymaking and industrial energy transition as it uncovers the relevance of sustainable energy resiliency to energy crises induced by geopolitical disruptions. \\
To achieve the objectives of the project, an Integrated Assessment Model-based study has been conducted using the WITCH model, which required several improvements in the framework.
The steel industry module was conceptualized and developed to describe the technology sets, the financial and technical constraints, the future projections of the steel market and the energy supply structure. Moreover, even though a prior version of the hydrogen supply was already in the model, the equations of the related module were modified to account for the consumption of hydrogen as a fuel in steel mills and to ensure compatibility with the expansion to the industrial sector. To allow for accurate integration of energy shocks in the model algorithm, the existing dynamics that describe the trajectories of fuel costs were then expanded and used to account for different shocks' intensities, time periods and the degree of energy dependency of the affected region. Finally, a scenario architecture suitable to capture the main variables of the analysis was designed to prepare a sensitivity analysis focused on the magnitude of the shock, the year of occurrence and the level of environmental commitment implemented.

The outcome of the simulations shows that most of the production of steel will be located in energy-dependent countries, where energy shocks impact fuel expenditures on a national scale. The financial damages perceived by steelmakers are exacerbated by large magnitudes of the increase in price and by early shocks, which would strike the industry before the development of alternative sourcing of fuels. The regulatory push to support sustainable technologies has the potential to effectively dampen the impact of shocks and decarbonize the energy mix in steelmaking by accelerating investment cycles and promoting the deployment of low-carbon hydrogen. Further explorations of the correlation between preventive investments in hydrogen and perceived disruptions in industrial production have proven how large-scale investments for alternative and secure supply of hydrogen yield long-lasting resiliency to energy crises, while lagging intervention exposes the industry to the risk of wide costs of inaction.

The results of the research have practical significance for both industrial and political decision-making. Risk-averse managers of steelmaking facilities might decide to allocate financial resources for early conversion from natural gas to hydrogen to guard against the possibility of energy shock backlash. Policymakers can produce long-term plans to stimulate the transition to green hydrogen with tailored carbon pricing, which would result in an expense transfer from the potential costs of the backlash of energy shock to the proactive development of secure and resilient hydrogen production. Besides the contribution to national environmental goals, this transformation would yield stabilization and permanent immunization of the industrial energy supply against the reoccurrence of shocks.
This can safeguard not only the manufacturing sector but also the national economy overall, as the increased expenditures endured by steelmakers would translate into rising costs for infrastructural development in the country. ... Read more

Humanity faces the unprecedented global challenge of climate change. The sheer complexity and uncertainty of this problem renders mere intuitive reasoning insufficient. To aid global climate negotiations, Integrated Assessment Models (IAMs) are used to analyze the interplay between climate and the economy. More specifically, IAMs account for how greenhouse gas emissions affect climate change, how climate change affects economic production, and how economic production affects GHG emissions. We can use IAMs to project trends in emissions and gross domestic product, assess the costs and benefits of climate policies, and estimate the social carbon cost required to achieve stated emissions reduction targets. Although IAMs are central to informing decision-making to avoid catastrophic consequences, policy recommendations resulting from IAMs commonly prompt a very heterogeneous distribution of risks and benefits across the globe. During the recent 2021 United Nations Climate Change Conference (COP26), it became clear that equity is a central issue in the climate action debate. Emerging economies consider currently suggested abatement policies unjust in light of the historical CO2 generation of high-income countries and the strongly increasing need for energy in low-income countries. The term double inequality has been coined to describe the inverse relationship between the distributions of risks and responsibilities. In fact, the regions that are the least responsible for historical and mostly current CO2 emissions around the world, exhibit the highest degree of vulnerability to climate damages. In order to enable international cooperation and have a shot at meeting the Paris Agreement target, we require policies that promote more equitable mitigation pathways. Equity is therefore an eminently pressing topic, yet most IAM studies largely neglect it due to the implicit use of a utilitarian social welfare function that aggregates risks and benefits over space and time, thus losing sight of distributional consequences.

In order to account for distributional justice, we transform the RICE model into a simulation model and embed it in a many-objective simulation-optimization setup such that we can find Pareto-optimal climate mitigation pathways for different problem formulations. Next to using four ethical premises (rooted in utilitarianism, sufficientarianism, egalitarianism, and prioritarianism), we also direct particular attention to the disaggregation of utility and disutility within each of these ethical premises. The reason for this disaggregation is based on the incommensurability of these two. Usually, IAMs maximize aggregate variables such as welfare. If we also consider the minimization of welfare loss, which is based on economic damages as one of the objectives, we can enable a potentially fairer distribution of not only consumption but economic damages. We argue that we can find climate justice behind the veil of aggregation. What we mean by this is that more equitable policy recommendations are obscured and lie hidden behind a bulwark of highly aggregated variables. If we look beyond this obstruction by the means of disaggregation, we are better equipped to find climate justice. In order to get to the bottom of this, we ask the following question:

How are Pareto-optimal climate abatement pathways affected by the disaggregation of utility and disutility in alternative ethical problem formulations when using an integrated assessment model under deep uncertainty?

To answer this question, we use a framework that is called multi-scenario multi-objective robust decision-making. For each of the eight problem formulations (4 ethical premises x 2 levels of aggregation), we use a multi-objective evolutionary algorithm to find Pareto-optimal policies. We reevaluate their performances under uncertainty by comparing their climate abatement pathways across the problem formulations. On a high-level, we can summarize our key findings as:

- dominance of aggregation levels over ethical premises
- correlation between low welfare and high welfare loss
- general dominance of egalitarian aggregated Pareto-optimal policies
- shared misery via egalitarian disaggregated Pareto-optimal policies

The effect of disaggregating utility and disutility is stronger than originally expected. Using disaggregated problem formulations yields substantially different pathways even within the same ethical premise. These results are promising as we could transfer these insights to other more complex IAMs such as IMAGE and MESSAGE. Overall, this could be also good news for the equity debate. Using alternative ethical premises and disaggregating incommensurate objectives such as utility and disutility can offer alternative policy recommendations and resulting climate abatement pathways which could in turn enable more equity. What we likely need now, is a stronger dialogue between the modelers and policy analysts on the one side and the stakeholders and decision-makers on the other side. The latter ones should not just blindly trust in the magical outputs of a model but they need to be involved to decide what problem formulations we need to use as there is no correct way to frame a complex real-world problem. As unmitigated climate damages exhibit an independent impact on a region's well-being, we could render IAMs more useful for climate policy if we a) acknowledge that the classical notion of welfare is obsolete, b) use a multi-objective approach, and c) let the decision-makers decide how they want to trade-off the various objectives in post. In this manner, we could use IAMs to advance into the direction of enabling a transition of more climate justice.
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Illicit supply chain networks are not well mapped. Regulators do not know how goods flow from supplier to retailer. There is uncertainty about whom is involved, where goods originate from, and what quantities are being shipped. Simulation models are effective tools to find measures against the distribution of illicit goods such as personal protective equipment. However, simulation models often solely handle uncertainty through the variation of parameters. Structural uncertainty, which is uncertainty in the structure of the model, is often neglected. This study focuses on accounting efficaciously for structural
uncertainty in supply chain simulation models using model-driven exploratory modelling.

Model composability, which is a specific form of model driven exploratory modelling, is used in this study. The methodology is applied to a supply chain of illicit personal protective equipment. Using a model composer, many plausible models are generated of this supply chain. A model composer works by coupling model components in different configurations, while complying to preset constraints. Model components are submodels of a supply chain actors, for example, a retailer. Constraints help to restrict the way the model components can be coupled, making sure that every model generated by the model composer is plausible.

A ground truth is established to test the model composer on its efficacy to account for structural uncertainty. A ground truth is a simulation model of an illicit supply chain that functions as a benchmark. Five sets of 100 models are generated by the model composer to estimate the ground truth. Each set of models is generated with a different set of constraints. A constraint set consists of elements such as the maximum number of suppliers, the locations of supply chain actors, and the maximum number of customers of a supplier. These sets reflect different perspectives on an illicit supply chain.

Results show that structural uncertainty can result in significantly different simulation outcomes. The time in system, the production time, and the international transport time depend the most on changes in the constraints of the model composer. The time in system, the production time, and the international transport time of the models generated by the model composer are significantly different from the ground truth. The distributions of these outcomes have a different shape and have a wider range of possible values. Therefore, this study shows that model composability, a specific form of model-driven exploratory modelling, is efficacious in accounting for structural uncertainty in supply chain simulation models.

In the future, the methodology shown in this study can be used to model structural uncertainty in other fields such as water pipes networks, gas pipes networks, and telecom networks. Furthermore, the methodology can be used to identify robust measures to tackle the problem of illicit supply chains. Another recommendation is to use model composability for the individual components of the model. For example, a component such as a retailer can be build from several components: a cash register, a shelf, and a distribution area. ... Read more

Misinformation on social media is an urgent grand challenge. Misinformation has caused excess deaths because people abstain from getting vaccinated and other evidence-based prevention behaviors. Misinformation also influences various other important topics such as climate change. And it has the potential to influence countless other areas. In order to exemplify the broader point, this project focuses on simple example of beliefs around the safety of COVID-19 vaccinations. To find ways to successfully tackle this grand challenge, it is crucial to have thorough understanding of the system. Modeling and simulation can be a powerful tool to support our reasoning about big and complex systems such as misinformation on social media. Therefore, I choose to look at how modeling and simulation can be useful for the study of misinformation on social media and of potential counter-measures.

Agent-based models (ABMs) are one of the useful modeling paradigms for this grand challenge. And while there is a body of literature on ABMs in the field of misinformation research, there is structural uncertainty about how to represent the way that people change their minds on social media. Different types of representations of this updating process are used. It is unclear which of them is the most suitable representation of the real-world process and also to what extent it makes a difference for the choice of counter-measures. Moreover, the choice between these different belief update functions is usually not discussed. And to the best of my knowledge, nobody has explored the issue of whether the choice between belief update functions makes a substantial difference in the conclusions from the studies.

Because of the significance of this grand challenge and the lack of exploring a key structural uncertainty, I choose to apply a method for exploring uncertainty in the context of ABMs. More specifically, because the structural uncertainty about the belief update function is a central component of models in this field, I explore a method for handling this structural uncertainty. This project is a show case of the value that methods from the field of Decision-Making Under Deep Uncertainty (DMDU) have for the field of misinformation focused ABMs.

Yet, applying a DMDU approach is not only useful for enabling exploration of uncertainties. With many DMDU methods, it is possible to evaluate policies based on not only a single, but on multiple objectives. As far as I know, also the evaluation of multiple objectives has not previously been done in the field of opinion dynamic models such as ABMs which focus on misinformation on social media. However, policies that aim at tackling the misinformation challenge do not only impact one single stakeholder, but a multitude of diverse stakeholders who care about various aspects of the system. If we pick policies by only optimizing for one objective, we run the risk of merely shifting the problem. To find solutions that are sustainable and work for the whole system, it is helpful to consider multiple metrics that stakeholders care about. The ranking and filtering by multiple objectives is not trivial. But there is a method called non-dominated ranking which can be applied to do exactly that. This results in so-called Pareto-optimal policies. It is in this specific niche that I pursue the following methodological question within the field of agent-based misinformation modeling:

Main Research Question

How does the consideration of structural uncertainty with respect to the choice between different belief update functions influence the resulting Pareto-optimal policies and their performance?

I look at three alternative belief update functions, where each belief update function is represented by one model. I show that the choice of the belief update function makes a significant difference for what kind of policies are Pareto-optimal and for the outcomes that stem from these policies. To investigate how the choice of the belief update function influences which policies are Pareto-optimal and what kind of outcomes result, I apply the DMDU-method of Many-Objective Robust Decision-Making (MORDM) approach. With DMDU methods, modellers can acknowledge the uncomfortable situation in which we know that we have uncertainties, ruining the possibility of using models as reliable prediction machines. These uncertainties can be about the real world's states (i.e., parametric uncertainties) or its processes (i.e., structural uncertainty). When applying DMDU methods, modellers can aim to find policies that perform robustly over a large number of possible instantiations of parametric or structural uncertainties. In this project, I first evaluate more than 26'000 candidate policies with each of the three belief update functions. Then, I select a set of Pareto-optimal policies for each belief update function. Additionally, I select a set of policies that seem optimal when only considering a single metric. Subsequently, I re-evaluate Pareto-optimal policies of each belief update function under deep uncertainty to gain a better impression of their performance. Finally, I compare the commonalities and differences between the selected policies and their performances. This, I do for either method of selection and for all three belief update functions.

To explore the structural uncertainty, I use a model which can be instantiated with either of the alternative belief update functions. I refer to these three possible instantiations as the three different models. The first model uses the commonly used function based on the research by Deffuant (hereafter 'DEFFUANT model'). In it, beliefs are always updated by a fix percentage towards the newly incoming information. In this project, this newly incoming information is the belief that is represented in a seen post. The second model samples whether a belief update happens or not. If an update happens, the new belief is the average between the previous belief and the newly incoming information. We call this the 'SAMPLE model'. Unfortunately, neither of these two models includes well-established phenomena from social psychology. Examples of such phenomena include for instance that we are more willing to update towards beliefs that are more similar to ours, that we have limited attention capacity, and that it takes more to change someone's mind when they are very convinced of their current belief than when they are uncertain. The third model was chosen to fill this void by basing its belief update function on Social Impact Theory (SIT) and adjusting this theory to the context of social media. This model is referred to as the 'SIT model'.

Main Findings

- There is a clear distinction between the models' optimal policies as well as their outcomes.
- Differences in parameters do make a difference.
- The models' optimal policies exhibit an order in how optimistic their outcomes are. This order (in descending direction) is DEFFUANT, SAMPLE, and SIT.
- The outcomes of the DEFFUANT and the SAMPLE model are more similar to each other than to the SIT model.

The main methodological take-away is that the DMDU approach can bring substantial value to the field of ABM-based studies on the grand challenge of misinformation on social media platforms. While this is shown by a simple exploration of the structural uncertainty with respect to the belief update, many more insights could be gathered by utilizing the DMDU approach. For instance, the DMDU approach offers state-of-the-art methods to identify vulnerable scenarios, i.e., scenarios which would be particularly bleak. Another example could be to explore different problem formulations with different sets of objectives or other structural uncertainties such as the posting behavior.

Furthermore, by utilizing the tools of DMDU, also society as a whole can benefit. By including multiple objectives and a wide range of considered uncertainties, the many different world-views and values of the diverse stakeholders can be taken into account in order to avoid potential policy gridlock situations. This could contribute to tackling the misinformation grand challenge more successfully and thus for instance lead to more people embracing evidence-based medical interventions. ... Read more

Trust has been identified in previous research as an important factor in criminals their decision-making when smuggling illegal goods. This research utilizes a case study of the criminal supply chain from South America to the port of Rotterdam, for simulating the relevance of trust in the criminal supply chain using an agent-based model. The agent-based model is able to simulate criminal decisions according to two decision-making models: (1) the risk vs. gain trade-off, and (2) transaction-cost theory. Interpersonal trust affects these trade-offs by minimizing risks and costs. The results show an insignificance of trust in the criminal supply chain for certain parameterizations of the model, when gains are relatively high compared to risks and/or costs. Different levels of trust have no effect on overal criminal supply chain performance. Furthermore, the results highlight the possibility of indirectly undermining trust within criminal networks when applying supply chain interventions, that hinder the smuggle of illegal goods. This implicates a possible reinforcing effect of supply chain interventions through the trust channel, on disrupting criminal supply chains when gains, risks, and costs are more in balance. ... Read more

The effect of using decision support systems in decision-making processes characterized by time pressure, uncertainty, and dynamism is still very limited and debated among researchers. By utilizing a case study that concerns the employment of a DSS to assist the procedures of intercepting fugitives undertaken by the Dutch National Police, this thesis investigates the difference in the personal efficiency and productivity of the practitioners (the decision time) between the current practices and the model-supported ones. An agent-based model is thus implemented which mirrors the procedures of intercepting fugitives in the two situations. The results highlight that, in contexts of time pressure, uncertainty, and dynamism, the use of the DSS does not always result in faster decision-making processes. This highly depends on how the DSS is implemented, especially in relation to the level of experience of the practitioners with the activities at stake and the level of dynamism and complexity of the decision-making process. Moreover, it is important to guarantee flexibility in the design of the system, and provide the users with the most relevant information to make the decision, so that the time needed to gather and process the information is minimized, while still guaranteeing an appropriate level of accuracy of the information displayed. Finally, the results show that involving the practitioners in the design of the system can improve the decision-making time, since it reduces the discussion between the actors, but is less relevant than the time needed to gather and process the information (which is a very critical aspect in contexts of time pressure), especially for those who work in contact with the system. ... Read more

Water storage and diversion capacity of reservoirs in the Nile Basin are benefited for various purposes including agricultural irrigation and hydro-energy production. Filling and operation of the recently constructed Grand Ethiopian Renaissance Dam (GERD) have been subject to heated debate for its implications on downstream countries Sudan and Egypt. In this study, we investigated trade-offs between the objectives of the three countries by formulating the problem as many objective optimisation. To this end, we built a simulation model conforming to the evolutionary multi-objective direct policy search (EMODPS) framework. Based on the optimisation results, we discovered two scenarios under which trade-offs exhibit diverging patterns. Our findings suggest that 1) aggressive filling strategies create evident trade-offs between the hydro-energy generation objective of Ethiopia and consumptive uses of Sudan and Egypt, 2) growing demand in the long-term brings the dilemma
of maintaining dams operational versus satisfying the demand and 3) increased demand pressure reinforces trade-offs between Egypt’s aggregate deficit minimisation and Ethiopia’s hydropower maximisation objectives. Our results highlight the potential of compromise policies in managing the objectives of all stakeholders without imposing heavy sacrifices. These policies represent an opportunity for cooperative operation of the dams through which multiple challenges facing the basin can be addressed. ... Read more

Amidst the discourse regarding the decentralization of urban energy systems, energy community has emerged as a solution for optimizing the electricity demand and distributed generation. Community energy projects also facilitate collaboration amongst local prosumers. An energy community is a collective of residential electricity consumers (or prosumers) and non-energy small and mediumsized enterprises (SMEs) formulating a social network involved in decentralized energy production. This study is focused on exploring demand response opportunities in community energy projects located in the Netherlands to reduce their dependence on the grid. Existing studies on community energy projects are primarily focused on residential members, and have little to no inclusion of nonresidential community members. However, recent studies regarding demand response in the energy community highlight the benefits of having a mixed configuration of residential and non-residential members. Introducing non-residential community members such as SMEs, offices, and schools with a complementary demand profile can help the community in attaining self-sufficiency through demand response. Formulating energy communities with a mixed configuration (i.e. including residential and non-residential community members) optimizes local electricity generation and consumption thus avoiding congestion in the distribution network.... ... Read more