Policy-makers in Dutch passenger rail face deep uncertainty in long-term demand forecasting as population growth, urbanisation, and climate change undermine the reliability of traditional models. This research applies Exploratory Modelling and Analysis (EMA) and Multi-Objective Robust Optimisation (MORO) to better capture uncertainty and identify fare policies that remain effective across a wide range of future conditions. Using a simplified elasticity-based simulation model and a multi-objective evolutionary algorithm, the study evaluates thousands of scenarios to explore how fare strategies perform against conflicting objectives (ridership, revenue, CO₂ emissions, and capacity). The results show that extreme fare policies are not robust: eliminating fares boosts ridership and lowers emissions but causes unsustainable revenue losses, while high fares secure revenue but suppress demand and climate benefits. Instead, hybrid strategies emerge as more balanced and resilient. For example, an affordable flat-fare travel pass (akin to Germany’s €49 Deutschlandticket) combined with modest peak-hour surcharges can significantly increase ridership and cut emissions while maintaining financial viability. A long-term analysis (2024–2070) further indicates that no single static policy remains optimal; adaptive fare pathways are needed as conditions evolve. Robust policy trajectories generally feature reduced base fares coupled with a rush-hour surcharge to manage capacity and funding. This adaptive, exploratory approach shifts focus from predicting a single future to preparing for many possible futures, supporting more resilient and sustainable transport policy decisions.

The Dutch electricity grid is facing growing congestion due to rapid electrification and decentralization of energy production, which threatens supply reliability and slows progress on the energy transition. While grid expansion is essential, long-term planning is complicated by deep uncertainties related to future electricity demand, renewable energy adoption, infrastructure delivery timelines, and policy developments. Existing planning approaches typically rely on deterministic forecasts or stochastic models, which are ill-suited to address deep uncertainty. This thesis addresses a critical gap in grid planning by introducing a structured method to incorporate deep uncertainty into long-term investment strategies for medium-voltage distribution networks. While Robust Decision Making (RDM) has been applied to transmission expansion and national-level energy planning, this study presents the first academic application of RDM to distribution network expansion planning.
The research focuses on Stedin’s investment plan for an area with significant expected demand growth and decentralized generation. Key uncertainties were identified using the XLRM framework, informed by a literature review and a focus group with Stedin professionals. A simulation model was developed and implemented in the EMA Workbench, incorporating Stedin’s infrastructure baseline, projected demand profiles, and investment levers. Through scenario-based stress-testing across thousands of plausible futures, the study evaluated capacity risk and cost performance. The analysis applied the Patient Rule Induction Method (PRIM) for scenario discovery and used the MoSCoW prioritization method to develop actionable advice for improving the robustness and adaptability of Stedin’s investment plan.

In complex public organizations, job-rotation policies are routinely used to build flexible workforces, yet their impact on adaptive performance remains unclear. This thesis investigates how the frequency and volume of rotation reshape organisational memory and, in turn, influence adaptation. Drawing on literature in organisational learning, complexity theory and contingency theory, it develops a conceptual framework that links memory loss, knowledge diffusion and re-learning to adaptive fit. The framework is operationalised in an agent-based extension of Epstein’s “Growing Adaptive Organizations” model, calibrated for a stylised public bureaucracy. Four task-environment regimes – stable, peaks, evolving and flipping – were simulated across nine combinations of rotation interval and volume, with stochastic hand-over accuracy and memory persistence. Results show that, within the model, any rotation schedule underperforms a no-rotation baseline; the best-performing rotating configurations lag by 1–18 percentage points in reliability, depending on environmental volatility. Rotation volume drives outcomes more strongly than interval: moving larger shares of staff consistently erodes performance, whereas shortening intervals only matters when large peaks coincide with rotations. Stable environments buffer memory loss, while environments with sharp, predictable shocks can benefit when rotations are timed to ‘prime’ units just before demand spikes. Excluded factors such as motivation boosts, informal networks and long-run career gains suggest the model is conservative about rotation’s upside. The study refines the memory-versus-flexibility debate and proposes four evidence-based interventions: align rotations with forecast shocks, cap simultaneous moves, use stable departments as learning incubators, and formalise hand-over protocols. Overall, job rotation is neither intrinsically adaptive nor maladaptive; its value is contingent on environmental cadence and design precision.

In an era shaped by systemic risks and long-term challenges such as climate change, technological disruption and geopolitical instability, policymakers must make consequential decisions without knowing what the future holds. This condition, known as deep uncertainty, arises when the relationships between actions and outcomes are contested and traditional prediction methods no longer apply. To address this, analysts have turned to computational tools such as scenario discovery, which explores thousands of simulated futures to identify combinations of factors that lead to policy success or failure. It provides decision-makers with understandable scenarios: data-grounded narratives of the future that highlight critical vulnerabilities and opportunities. These scenarios help enable the design of strategies that are robust and adaptive...

The Urban Transportation Network Design Problem is an important challenge in urban planning that aims to design effective transportation networks in urban environments. This research addresses three significant gaps in the existing literature. First, much of the research has remained focused on traditional objectives, such as minimizing costs and travel times or maximizing satisfied travel demand. This study, however, shifts the focus to optimizing for accessibility, thus explicitly incorporates equity into the transportation planning process. Second, while most studies concentrate on designing new networks or enhancing uni-modal networks, this research focuses on existing multi-modal networks, offering a more realistic approach to transportation planning. Finally, the study applies a novel approach to the Urban Transportation Network Design Problem through the use of Reinforcement Learning, specifically Tabular Q-learning.

The research methodology is structured in three parts. We begin by operationalizing equity through accessibility to employment opportunities. This metric is transformed using three notions of justice formalized as Social Welfare Functions: utilitarian, Atkinson, and lowest quintile. The second part involves gathering, cleaning, and integrating data to construct and simplify a comprehensive spatial model of the urban area, forming the foundation for subsequent optimization. The final part applies Reinforcement Learning, using the previously defined equity metrics and spatial model. The model is trained across different equity-driven reward functions to generate optimized network expansions.

A case study in Cape Town, South Africa, demonstrates the practical application of this approach, chosen for its its complex multi-modal transportation network and significant socio-economic disparities. The study's results reveal that each notion of justice leads to radically different expansion generations in entirely separate regions of the urban area. The results indicate that a traditional, utilitarian reward function leads to maintaining existing the existing distribution of accessibility, while the Atkinson reward function improves access for disadvantaged central regions by connecting these vulnerable areas to the Central Business District. The lowest quintile approach focuses on improving access for the most disadvantaged regions, particularly in the central southern areas, ensuring that the expansions provide maximum benefit to areas with the poorest access. The study also highlights the limitations of Tabular Q-learning in handling large state-action spaces and sparse rewards, suggesting the need for Deep Reinforcement Learning methods in future research.

Overall, this research contributes to the growing body of literature on equitable transportation planning by providing a versatile framework that can be adapted to other urban contexts. The study concludes with recommendations for future research and practical applications in urban planning, advocating for the continued exploration of equity-focused approaches in the design and expansion of transportation networks.

Tipping points are an active and growing interest in both the scientific and political study of climate change: what are they, how can we identify them, and how can we avoid them (negative tipping points) or encourage them (positive tipping points). As climate change worsens, scientists and policy analysts have turned to computer models of complex, interconnected, human-Earth systems to help understand and address both its physical and social aspects. As this field has matured, so too has the complexity of both the models being developed and the questions being asked with them. Agent-based modeling (ABM) is one framework that has become popular for its ability to observe system-level behaviour without closed-form equations due to its encoding of heterogeneous individual-level behaviour.

Analyzing the data generated by ABMs is not straightforward, as they tend to have many input and output dimensions, most outputs are either temporally or spatially distributed (or both) and can be sensitive to stochastic effects. When applying a exploratory modeling or deep uncertainty lens—a philosophy that seeks to explore the effects of assumptions made in a model’s development and parametrization, understanding more about the modeled system’s behaviour as opposed to attempting to predict it—the complexity of this analysis grows further. However, this complexity should not discourage analysts from bringing existing Decision-Making under Deep Uncertainty (DMDU) methods to ABMs.

This study applies one such method (scenario discovery) to a complex ABM of household and firm climate adaptation in a coastal economy, attempting to uncover the existence of socio-environmental tipping points in the system. Based on a previously developed analogy connecting the output space of an ABM to the traditional notion of a physical phase diagram, Scenario Discovery is used to generate such a phase diagram and infer tipping points at the boundaries between distinct system states. Ultimately, a set of possible population-change tipping points are generated.

While this work demonstrates the fitness of scenario discovery as a tool for exploring the output spaces of ABMs and finding tipping points within them, it is very preliminary. The work should be repeated with several improvements. First, either the uncertain parameters varied in this study should be selected to be more policy-relevant, controllable system factors, or the system states and thus the tipping points should be expressed in terms of endogenous variables instead of input parameters. Second, this study demonstrates that the typical approach to processing stochastic replications in exploratory modeling—simply averaging all outcomes—is not fit for use with complex modeling like ABM. Despite the computational and cognitive load introduced by simultaneously handling both a wide uncertainty space and many stochastic replications, efforts must be made to ensure any dynamically distinct behaviour generated by the original model is not lost to averaging. Studies like this one that do not put in this effort risk enabling the extraction of incorrect political and policy lessons.

This thesis explores the potential of increased recycling rates as a strategy to mitigate emissions, reduce waste accumulation, and alleviate material depletion across diverse regions and socioeconomic contexts. By developing and applying the JUSTICE-MATTER model, this research advances our understanding of how recycling, as a key component of a circular economy, can be integrated into broader climate policy frameworks to achieve sustainability goals.

The research methodology involved the development of a comprehensive matter use module based on the economy-wide material flow analysis (ew-MFA) framework that tracks material flows from extraction to disposal. This MATTER module provides a granular understanding of material usage across 57 regions, considering regional disparities in resources, economic activities, and socio-economic conditions. The JUSTICE-MATTER model integrates these material flows with climate and economic modules, offering a nuanced evaluation of how recycling policies impact emissions reductions, economic output, and material depletion under different Shared Socioeconomic Pathways (SSPs).

The findings highlight that recycling plays a significant role in reducing waste and conserving material resources, especially in regions with underdeveloped waste management systems. The transition to a Circular Economy (CE) in regions like Southeast Asia, Brazil, and parts of Africa resulted in substantial reductions in waste generation and material depletion. However, the impact of recycling on emissions reduction was found to be relatively modest, particularly in high-emission regions like China and Brazil. This suggests that while recycling is essential, it must be complemented by other strategies, such as transitioning to cleaner energy sources and implementing comprehensive emissions control policies, to achieve meaningful emissions reductions.

Economically, the effects of increased recycling rates varied significantly across regions. Regions with established recycling infrastructure, particularly in Europe, experienced minimal economic disruption from higher recycling rates. In contrast, regions like Chile and Brazil faced higher economic costs due to the need for substantial investment in upgrading recycling systems. These findings underscore the importance of tailoring recycling strategies to regional contexts, considering the economic and infrastructural disparities that influence the feasibility and impact of such initiatives.

The research highlights the importance of region-specific policies, emphasising differentiated approaches that address the unique challenges and opportunities of each region. While developed regions may focus on optimizing recycling processes, developing regions must prioritize infrastructure and capacity-building efforts. Despite the advancements in the JUSTICE-MATTER model, limitations such as static emission factors and a narrow focus on certain materials suggest areas for future refinement. Future research should incorporate dynamic emission factors, expand material analysis, and regionalise key parameters to reflect diverse economic and material use patterns more accurately.

In conclusion, this thesis contributes to advancing Integrated Assessment Models by integrating material flow dynamics into the JUSTICE-MATTER framework. Addressing the identified limitations and pursuing suggested future research directions will enhance the model’s capacity to guide global sustainability efforts and offer more robust policy recommendations for the complex challenges of the future.

Background: The introduction of autonomous vehicles (AVs) could fundamentally transform urban transportation, but their system-level effects on cities remain poorly understood. Previous research has focused primarily on individual adoption decisions or specific impacts like congestion, without capturing the complex interactions between adoption patterns, modal shifts, and transportation system performance.

Goal: This study investigates how autonomous vehicles might affect urban mobility problems, considering both modal shifts and induced demand, and examines which policies could effectively mitigate potential negative impacts while preserving benefits.

Method: An agent-based model combined with mesoscopic traffic simulation was developed to simulate travel behavior in Rotterdam, Netherlands. The model integrates empirical data on population distribution, travel patterns, and network characteristics with a mode choice framework accounting for heterogeneous time valuations. A full-factorial analysis explored 144 scenarios varying AV costs, perceived time value, space efficiency, and induced demand. Eight representative scenarios were then tested against nine policy combinations including congestion pricing and speed reductions.

Results: AV adoption patterns appear to depend more strongly on space efficiency than cost or comfort advantages. A critical threshold around a density factor of 0.5 (compared to conventional vehicles) emerged - below this threshold, high AV adoption can maintain system performance, while above it, increased adoption tends to degrade network performance regardless of other characteristics. The model also revealed that AVs compete more directly with sustainable transport modes than with private cars, potentially undermining urban sustainability goals. Traditional policy interventions showed limited effectiveness across different scenarios, with localized restrictions proving particularly inadequate for managing system-level impacts.

Conclusions: Autonomous vehicles may represent neither an inherent solution nor an inevitable problem for urban mobility. Their impact appears likely to depend on the interaction between their operating characteristics, adoption patterns, and policy frameworks. The significant variations between potential futures - ranging from improved mobility to system strain - emphasize the importance of proactive policy consideration in AV development. Results suggest that cities should focus on ensuring space-efficient AV operations rather than just regulating costs or access, while developing more dynamic and comprehensive policy frameworks to manage the transition.

As the impact of climate change becomes increasingly urgent, conflicts over transboundary water resources have become more complex, particularly in light of the role of water in shaping socioeconomic and regional power dynamics. The challenge of optimizing objectives and strategic behavior across multiple stakeholders simultaneously makes it difficult to arrive at policies that can enhance long-term transboundary water management in a harmonious manner. In the Eastern Nile Basin (ENB), tensions have risen between upstream Ethiopia and downstream Egypt and Sudan due to the development of the Grand Ethiopian Renaissance Dam (GERD), making successful negotiations for water allocation critical. Ideally, such negotiations should produce agreements with high-stability policies, meaning that no actor has an appealing unilateral gain in utility that would cause them to defect from an agreement.

Several water allocation simulations have been developed in the ENB using both linear and nonlinear programs to aid decision-making, but only two studies have previously examined measures of stability for these optimization results, and neither has been done since the completion of the GERD. Moreover, as simulation complexity has increased, there is a gap in knowledge regarding the measurement of stability using optimization results from closed-loop, multi-objective adaptive simulations.

To address this gap, this research reexamines the stability of policy candidates for water allocations in the ENB using three different solution concepts from cooperative game theory—the Nash-Harsanyi solution, the Shapley value, and the nucleolus. The stability of each policy candidate is assessed using three different stability metrics—the Euclidean distance, the Loehman Power Index, and the propensity to disrupt—to determine their relative stability. The approach yields similar objective trade-offs and utility behaviors for the Nash-Harsanyi and Shapley Values. The most stable policies, when ranked by Euclidean distance, prioritize Ethiopia’s utility, while policies become more unstable with the rapid growth of Egypt’s utility. Furthermore, propensities to disrupt and power indices between Egypt and Ethiopia or Sudan show converging and diverging behaviors, respectively, which explain the negotiation potentials between the players. Our results indicate that Egypt’s willingness to engage in collaboration is directly related to its level of utility; however, at these levels, Ethiopia and Sudan’s benefits from utility are at levels that prompt higher likelihoods of defections from a potential coalition. The results also showed stable policies characterized with high policy efficiencies in instances of basin-wide cooperation, which increases benefits to all nations.

Given the different assumptions and characteristics of each stability concept, the insights on the stability of different policies provide general guidelines for incorporating stability into the optimization formulation itself. The results have larger implications for policy planners in the ENB and the difficulties they may face when finding acceptable solutions in multi-stakeholder decision arenas. Furthermore, the methodological contribution of this study could allow for easy application of this method to other water allocation conflicts to help guide policy planners detect opportunities for utility optimization or risk mitigation in a cooperative setting.

The Dutch Ministry of Defence fulfils a crucial role in keeping the Dutch kingdom safe. From missions overseas to providing aid in the case of natural disasters, correct and efficient information-flow is critical for their operations. When a situation occurs, everything is dropped to make sure that they can handle it. The Royal Dutch Army, Navy, Airforce and Marechaussee make up the armed forces part of the military. Besides the branches of the armed forces, there are two support branches. Under one of them, Defensie Ondersteuningscommando, the Air Transport Unit of the Dutch military is located. This unit plans and advises on all forms of air transport for the Ministry of Defence, a unit where correct and timely information is flow critical.
This thesis attempts to answer the following research question: What are the effects of formal network structures within public organisations on their information-flow quality? Attempting to fill gaps in literature surrounding the effect of formal network structures on information-flow quality. The study defines information-flow which is used for the KPIs. The effects of formal network structures on information-flow quality are studied using an ABM model developed in this thesis. The model is tested on the Air Transport Unit network and a diverse set of randomly generated networks. In this case, key findings from the model results indicate that in the case hierarchy, a more hierarchical structure positively affects their data correctness. However, this more hierarchical structure negatively affects their information timeliness. Employee business does not seem to be affected by hierarchy. Other findings relating to the number of employees whose function it is to control data for mistakes, have significant effects on improving data correctness while at the same time, negatively affecting the timeliness of information. Indicating that the optimum, hierarchical, or less hierarchical organisation lies with the ambitions and goals of an organisation.

In the Netherlands, a substantial burden of morbidity and mortality persists for cardiovascular diseases in women. Despite this, the reduction of cardiovascular diseases in women has plateaued. This is against the backdrop of an ageing population and the prevalence of sedentary and unhealthy lifestyles.
This research presents a novel model to explore the problem space. A multi-disciplined microsimulation model is presented that incorporates theories and data aggregated from individual health data, population studies, social network studies, and behaviour studies. To our knowledge, it is the first model of its kind. We used the city of The Hague as our case study, as we were able to use a treasure trove of individual health data to inform the model, and thus inform the answer to the research question. The model was developed to answer the research question:
How can behavioural interventions decrease the healthcare burden of cardiovascular diseases among women in The Hague?

Our methodology consisted of multiple phases. First, we conducted a literature study to identify
cardiovascular risk factors and entry points for interventions. Second, we developed health data models to be integrated into the microsimulation model. Third, we designed and implemented a microsimulation model and explored a plausible future cardiovascular health burden. Fourth, we looked at the impact of certain interventions applied to the entry points. The chosen interventions are based on the hypothesis that was derived from the literature read during the first phase. The hypothesis was as follows:

How can recurring interventions targeting diet, exercise or smoking behaviours decrease the healthcare burden of cardiovascular diseases among women in The Hague?

During the literature study we conducted, we made multiple findings. First, current studies seem to omit relevant risk factors for women, such as pregnancy complications. So far, studies primarily focus on men, even though that cardiovascular diseases are the leading cause of morbidity and mortality for women in the world. Second, studies that examine risk factors oversimplify the nature of the problem and neglect cultural, social and even biological context. The problem is complex and multi-faceted. Thus, it warrants a fitting approach, such as the one presented in this research. Third, there is too little evidence on the efficacy of interventions targeting behaviours that lead to an increased risk of cardiovascular diseases.
During the development of the data models, we found that the cardiovascular risk of a young female is significantly higher if she has multiple risk factors – something that is currently not mentioned in the cardiovascular guidelines. We also found that smoking is the most dominant modifiable risk factor. However, since, in the model we developed, exercise and diet behaviour affect a woman’s blood pressure, total cholesterol and blood sugar, indirectly, BMI may be just as, if not more, important. Our data model and our literature study thus confirm that these are important entry points that need to be exploited by interventions.
The simulation runs made the staggering revelation that, unless we do something about it, the future for women with regard to CVD looks bleak. The health issue is obstinate, and much of the prevention potential seems to be lost. The effects of many temperate interventions, such as education in schools, are negated, due to the oversaturation of unhealthy lifestyle behaviours. We also found that the effect of repeating interventions is more sustainable and long-term. However, our experiments implied that true progress can be made if extreme interventions are introduced repeatedly. Due to the intensity, it is unlikely the population of The Hague and additional stakeholders would approve of these interventions. We nuance the findings by the fact that the model is a simplified representation of the real world.
Choices were made during the design, and certain elements of human behaviour and of cardiovascular pathophysiology were omitted from the model. In some aspects, there simply was not enough data, such as on the effect of policies on a woman, but also how a woman is exactly influenced by her network and by external influences. These were some of the unknowns that could be addressed in future research.
This research concludes that there is an urgent need to introduce interventions that realise a sustainable, lasting change in the behaviours of women in The Hague. Three potential entry points are food intake, exercise, and smoking. Promoting healthier lifestyles is however only possible if we also address the social and cultural context. This model shows it is less effective to just change the behaviours of one woman, as social pressures may persuade her to fall back to her previous behaviours. We can set up women for success by involving her social network and as such decrease the barrier for her to permanently adopt a healthier lifestyle.

Communicative diseases have put a burden on societal well-being throughout history. The global advent of the COVID-19 virus made the world painstakingly aware of the impact a pandemic can have on all levels of society. To combat the spread and impact of a pathogen, policymakers turn to specialized tools to craft policy. One of these tools is mathematical modeling and simulation. By using the agent-based modeling paradigm, it is possible to evaluate the impact of policy measures on the viral spread and healthcare burden. An agent-based model always contains a set of agents. Agents are the digital representations of a studied entity, for example, a person, but agents can also be an organization or a vehicle. One of the characteristics of agent-based modeling is the reliance on data to create a valid agent population. However, data on the level of individual people, also known as microdata, can be difficult to acquire and work with due to GDPR regulations. Aggregated population data, also known as open data, may serve as a substitute for microdata. Both open and microdata may be used to synthesize a population of agents, this process is known as synthetic population generation. Although both of these forms of data can be used to synthesize an agent population, it is currently understudied how the results of the agent population synthesis process are affected by the type of used population data. This research project focuses on this gap in knowledge and systematically analyzes the differences between an open-data-based and a microdata-based agent population. The populations that are synthesized are part of the HERoS model. The HERoS model is an epidemiological agent-based model that maps the spread of COVID-19 in the city of The Hague. The agents in the HERoS model are digital individuals, each characterized by a set of attributes. These attributes define the behavior of an agent within the model. Examples of agent attributes in the HERoS model are age, household size, and social role. Two agent populations for the HERoS model are synthesized, one using a sample-free algorithm that uses open data, and one using an algorithm that converts microdata to an agent population. The differences between the agent populations are quantified by introducing two new metrics, the modified Freeman-Tukey statistic, and agent matching. It was found that the agent populations show similar characteristics on a city level, but show significant differences at the neighborhood level. Furthermore, it is shown that the quality of an agent population is both dependent on the number of attributes that are synthesized, as well as the type of attributes. Moreover, the relationship between the quality of an agent population and the outcome a model produces is also understudied. This relationship is investigated by using the open-data-based and microdata-based agent populations as input for the HERoS model. The model outcomes are then compared on a global and neighborhood level, using phase comparison and the quantification of the precision of the model. It is found that the effect of using open data instead of microdata is multifaceted. The model outcome curves show similar characteristics, albeit they differ in a numerical sense. Using open-data increases the number of hospitalizations, ICU occupants, and deaths in the HERoS model. Furthermore, the precision of the model decreases when open data is used. Finally, it observed that neighborhoods that significantly differ in input population also significantly differ in model outcomes, showing the importance of the quality of an agent population for smaller resolutions. Conclusively, this research project shows that open data can serve as a viable alternative to microdata when it comes to synthesizing agent populations for epidemiological agent-based models.

Being able to timely access and share accurate information to all stakeholders that can influence a supply chain is referred to as supply chain visibility. Supply chain visibility is crucial for increasing the resilience of a supply chain as this helps to proactively plan and design interventions to undesirable events. Various methods are available to increase supply chain visibility, often by increasing the accuracy of estimating future events. Many of these methods are relianton (large volumes of ) data, which is problematic because data on supply chains is often not accessible, expensive to acquire, and difficult to process. Therefore, methods to increase supply chain visibility in situations with limited data availability are needed.

 Modeling and simulation can be applied to situations with limited data availability, but has a limited predictive power for complex systems. Data assimilation in discrete event simulation (DES DA) is a method to increase the predictive power of modeling and simulation. This method aims to approximate system states based on real-time measurements to increase the accuracy of simulation models. DES DA has not yet been applied to supply chains and the dependency on data availability still needs to be explored. The goal of this research isto develop a DES DA algorithm that can accurately estimate future events in supply chains and test this algorithm for different levels of data availability. This will give insight in the relation between data availability and the accuracy of the proposed DES DA algorithm. During the development of this algorithm the typical supply chain challenges of high dimensionality and future state estimation are addressed.The proposed DES DA algorithm shows to have a higher predictive power (accuracy) than the results of a simulation exercise without assimilation. To allow for maximal reproducibility and facilitate future work, all adjustments to the simulation algorithm are extensively described in the body of this thesis.

Analysis of the performance of the proposed algorithm under different levels of data availability shows that limited data availability does not necessarily result in less accurate estimations. Less observations can can result in similar or even more accurate estimations. However, this can only be the case if the missing data is not part of the set of crucial data sources. If the missing data points are part of these crucial data sources the  accuracy of the data assimilation algorithm detoriates and the algorithm starts to under fit. Under fitting occurs when a model uses input variables that are not significant enough to determine a meaningful relationship between the input and output variables. 

If limited data availability is the result of omitting data sources that are not part of the crucial data sources, the accuracy of the data assimilation algorithm can increase. Using less datapoints helps combatting the curse of dimensionality which results in more accurate estimations. This research has shown that crucial data points for supply chains are the data sources that serve as arrival sensors of entities at the system boundaries. The arrival sensors reduce the uncertainty of future state estimations by accurately estimating the number of the entities in the system and the moment of arrival of these entities. Especially the accurate estimation of the moment of arrival reduces the variation in the estimations of the particles and thereby increases the accuracy of the estimation of future events.

In the face of environmental challenges of unprecedented scale and urgency, the European Commission enacted in 2019 a new comprehensive growth strategy with the aim of reaching net zero greenhouse gas emissions in 2050. Named as the European Green Deal, this strategy includes energy, biodiversity, pollution, and climate adaptation targets, and envisages that all EU actions and policies will contribute to its objectives. However, to date, specific actions addressing biodiversity, climate adaptation, and health issues from within the energy sector are still missing or lagging behind. This research proposes a new form of Renewable Energy Community (REC), which combines the use of solar photovoltaic panels with green, namely vegetated, roofs to address multiple Green Deal’s objectives. First, this form of REC was grounded in the current European legislation so to ensure its eligibility for the support schemes that Member States are currently required to devise. Next, the costs and benefits of this REC were determined with value transfer for a case study in Esch-sur-Alzette (Luxembourg) and a probabilistic cost-benefit analysis (CBA) was conducted. By applying Scenario Discovery, the CBA was simulated under different combinations of input parameters and rather than only providing multiple net present values (NPVs), the ranges of input values resulting in desirable NPVs were determined. As a result, the conditions under which the photovoltaic-green roof energy community becomes economically convenient were determined, providing guidance to national policymakers designing RECs’ incentive schemes at present.

The climate change around the globe driven by Greenhouse Gas emissions (GHG) is fueling the growth of Sustainable Energy Systems (SES) at a faster pace. However, the lack of acceptance of these systems by society has stifled its successful deployment. Societal values play a key role in evaluating the social acceptance and the broader consequences of SES. However, there exists a complexity of change in the values of people. Alternatively known as value change, although SES may embody values permanently during its design, the values that people hold important may change during the lifetime of SES. Value change may often be driven by various exogenous factors as well as due to the complex, emergent, and dynamic characteristics of SES. Consequently, this has led to high uncertainty in the future acceptance of SES. Exploring the uncertain scenarios of value change is crucial for social acceptance of SES as it can facilitate better consideration of values in evaluating social acceptance of SES, ultimately contributing to the future acceptability of SES by society. However, current approaches to explore value change in ethics of technology literature are scarce. Few have proposed to explore value change, but lack in dealing with value change after it has occurred, or they consider values in a static manner. Alternatively, simulation models show better prospects in exploring complex societal dynamics of which a human mind cannot picture. Simulation models such as Agent-Based Models are seen as a suitable solution to capture the underlying mechanisms that drive the value change considering the complex and dynamic characteristics of SES. The objective of this research is to gain an understanding of the mechanisms that drive the value change in SES, by formulating a modelling approach that integrates agent heterogeneity, common pool resource & dynamic behavior, transformative experience & bounded rationality, and social interaction, to explore value change under various policy and uncertainty scenarios of SES. To this end, this study combined Agent-Based Modelling and Exploratory Modelling approaches to explore the change in importance of values in SES.

The growing import of illegal products in the Netherlands via container ships causes increased violence, addiction, and tax evasion in the Dutch society. With the current criminal detection practices the Dutch National Police and Dutch Customs do not manage to intercept the majority of the illegal shipments. The resources used by the Dutch Police and Customs for criminal detection do not yet entail information from the criminal risk vs gain trade-off point of view. Therefore, this study set out to research the impact of the criminal risk vs gain trade-off on the international criminal supply chains.

The world is currently a rushed environment; things change faster than ever. Technological innovations impress us again every year. Many innovations that enhance society are the result of innovating organisations. These organisations invest in innovative solutions, for their own good, but eventually also for the greater good. Allocating resources to these investments is therefor an important aspect of innovation. This thesis has been conducted to gain more insights in the resource allocation process, the process which determines the investments executed in organisations. The resource allocation process model as defined by Bower and Gilbert helps managers and executives to create awareness about allocating their valuable, finite resources. Bower and Gilbert tried to create a simple model without a lot of predefined mechanisms, but their model lacks detail and therefore the inner mechanisms of each sub-process of the resource allocation process is a gap in the literature, there are some scholars who describe specific cases in detail, but there is no generic interpretation. This research gap has been the foundation of this research and has led to the development of an agent-based model of the resource allocation process. Within the development of the simulation model all core processes had to be defined in detail. And since the objective is to develop a generic simulation model, the core of the sub-processes and inner mechanisms had to be defined. One of the most important mechanisms is the way an organisation can influence their own resource allocation process. This happens through structural and strategic context determination. With these two types of context determination, organisations can influence their allocation process in order to either execute more induced strategic action (action which corresponds with the defined corporate strategy) or more autonomous strategic action (action which lies outside the defined corporate strategy). Which type of strategic action is necessary depends on the specific situation of an organisation. With the knowledge of these two kinds of determination the sub-processes and their mechanisms had to be defined. Definition of these sub-processes happened through literature research. There are two topics within these processes: Definition and Selection. The basic principles of these two topics had to be known in order to be able to formulate a possible generic detailed definition of the sub-processes. This resulted in a conceptual model which, through formalisation could be implemented. Before implementing an agent-based model the detailed definition had to be complete in order for the model to function. The implemented model has been tested on known phenomena derived from literature. The model is able to reproduce the expected strategic action under specific organisational context settings. Within a dynamic environment the model reproduces expectations for the contextual settings. And the mechanism of an increasing opportunity space can be reproduced. The model is unable to fully capture the effect of communication and divergence on the autonomous strategic actions. These results support concluding that the developed agent-based model is able to simulate the resource allocation process of a hypothetical organisation. With the model, the boundaries of the resource allocation process can be explored on proportion of control versus organisational learning. The model is also be able to analyse the effect of organisational changes on the strategic actions. Currently the model as implemented is unable to simulate a real-world case, but the potential for use within organisations and further research is certainly there.

Integrated Assessment Models (IAMs) are widely used tools for estimating climate change's impacts on the socio-economic system. However, IAMs possess a limited ability to represent the impact on vulnerable populations. Influential IAMs use highly aggregated outcomes masking significant relative differences in impacts across heterogeneous populations and time. Therefore IAM studies systematically undervalue the need for faster CO2 emission reductions to prevent substantial climate impacts on low-income groups. Previous literature has proposed alternative distributive principles to use in IAMs to overcome inequity problems in current abatement pathways. Due to the heterogeneous models, scenario assumptions, and principles used, comparison between studies has been difficult. Therefore the literature lacks an overarching comparative study of the performance of various distributive principles. Furthermore, deep uncertainty present within climate change is often not considered because most studies focus on single-scenario optimality. Therefore, current abatement pathways contribute to maintaining current economic inequalities and possess exposure to uncertainty. This thesis applies alternative principles to the RICE model through a MORDM method. Alternative distributive principles are used to generate more equitable abatement strategies using a MOEA. This research analyses four main principles: the Prioritarian, Sufficitarian, Egalitarian, and the Utilitarian principle. Alternative principles generate significantly different abatement trajectories than the Nordhaus optimal policy. Sufficitarian policies have the most stringent abatement targets focusing on reaching net-zero emissions by 2065. Prioritarian strategies aim at 2065-2105. Utilitarian strategies focus on reaching net-zero emissions around 2135. Lastly, Egalitarian policies enforce slow climate abatement. Abatement strategies have been stress-tested through an uncertainty analysis based on the SPP-framework and exposure to long-term climate uncertainty. Sufficitarian strategies possess high robustness to climate extremes and high average performance across all scenarios. Prioritarian strategies possess high average robustness but lower regret-based robustness. This research shows that alternative principles can successfully generate well-performing abatement pathways in the RICE model that outperform Utilitarian-based policies. The Sufficitarian principle's capacity to produce equitable and economically optimal strategies has been undervalued in previous research. Lastly, this thesis emphasizes that stronger climate action is needed to prevent possible disastrous worldwide impacts on low-income groups. Global emission reductions should adhere to a global warming of 1.5 degrees set out by the IPCC, which is consistent with the best-performing pathways found in this research. A first step would be to reduce existing economic inequalities to overcome exposure to climate extremes for lower-income groups.

Good supply strategy that can improve the logistical chain involves taking into account the complexity that a specific good is situated in. This research takes improving the bicycle supply chain within the Netherlands armed Forces as a use case. Aside from the goals for the use case of bicycles, the aim was also to fill the knowledge gap of analyzing an intra-organizational, multi-structural supply chain system, from the perspective of viewing an organization as a collection of business processes. The method used to fill the knowledge gap and gain an answer on the research question is Agent-Based Modelling, combined with an exploration approach using the Exploratory Modelling and Analysis (EMA) workbench. The results show that If the processes are not aligned at every decision made by agents, extra variance and unpredictability in the behavior is seen. Both predictive replacements and willingness to share information were promising policies, but do require a trade-off. The uncertainty in demand and supply also greatly influenced the effort required of the different agents. Choices have to be made in what found important within the supply chain. One must also take into account the long term visions of Defense with regards to the goals of the organization as a whole. The used simulation methodology in this research is a novel approach to investigate complex but practical problems occurring within Defense’s processes. Recommendations to overcome hurdles in practical research are, establishing a clear pragmatic goal and exploring all the processes involved before implementing a simulation model or looking to explain quantified performance indicators. Analyzing the intra-organizational supply chain within Defense showed that in general, intra-organizational supply chain research would benefit from a more process oriented perspective. Generalizing of the results goes as far as goods that have the same classification. Limitations of this research consist of three points: Effort as KPI, run time and extrapolation. Further research might investigate the possibility of extrapolation, extended geographical structure within the model and expansion of uncertainty considerations.