To prevent the worst dangers of climate change, the EU has committed to net zero by fully decarbonizing the European energy sector by 2050. While most energy sectors can be decarbonized by electrification, some “hard-to-abate” sectors need other solutions to lower their emission output. The EU has chosen green hydrogen to replace fossil fuels in such sectors. However, despite many advantages, it is expensive to produce and thus not competitive in the energy market. Therefore, the EU has created the Hydrogen Strategy as a central policy package to optimally implement green hydrogen within the energy sector. The finding of naturally occurring hydrogen, known as white hydrogen, could revolutionize the EU transition towards hydrogen as its production cost could be very low. However, as much is unknown about what effects white hydrogen could have on energy markets, it creates uncertainty for the EU and the Hydrogen Strategy. Modeling future scenarios can reduce the uncertainty for policymakers. While green hydrogen has been studied with a modeling approach, no such literature exists on white hydrogen. Thus, this study aimed to fill this knowledge gap by modeling white hydrogen to increase insights into the white hydrogen system and how it might affect the future energy system. During this study, a Decision Making under Deep Uncertainty approach was used to analyze the effects and uncertainties of white hydrogen on the global energy system and EU hydrogen policymaking. First, a system dynamics model was made to model global energy markets, including all relevant hydrogen. This model was tested with various experiments under uncertainty. Then, multiple scenarios were developed to match the possible trajectories of a white hydrogen market. The system behavior within the model was analyzed using the Exploratory Modeling Analyses method to determine the effects of white hydrogen on the energy system. Finally, the EU hydrogen policies were analyzed in the context of the scenarios, and recommendations were made to the EU on handling the uncertainty surrounding white hydrogen. The system analysis showed that the global energy system is heavily impacted by white hydrogen in scenarios where the white hydrogen market takes off. In specific scenarios, the price of white hydrogen may directly compete with gas prices, leading to a surge in demand. As the prices of other types of hydrogen decrease, they will also see an increase in demand, resulting in a net positive effect for all hydrogen types. However, despite rising demand, the production of white hydrogen may not keep up, leading to a lower overall usage of hydrogen. This supply-demand gap could cause significant shortages of hydrogen, which will exacerbate the existing energy shortages caused by carbon taxing and result in an overall decrease in energy usage. The results showed that the EU can only achieve its renewable hydrogen goals by 2030 and 2050 if the white hydrogen market grows substantially. An increase in the share of white and green hydrogen will lead to greater use of renewable energy sources. This, combined with a decrease in energy demand, will achieve net zero emissions more frequently and earlier in cases where the white hydrogen market grows. While EU hydrogen policies contribute to these goals, their impact alone is not enough to achieve them. However, the white hydrogen market works well with these policies, increasing total hydrogen demand. Furthermore, results showed that the policies aimed at reducing hydrogen prices significantly impact all scenarios, with the hydrogen bank policy being particularly effective. Yet, if hydrogen becomes too expensive, the sudden increase in green hydrogen prices could harm the green hydrogen market. The results suggest that developing the white hydrogen market is crucial for achieving renewable hydrogen goals, and policies that drive down hydrogen prices are also important in reaching these objectives. ii iii The main scientific contribution of this study lies in the modeling of white hydrogen within a dynamic system, which allows asserting the impact of white hydrogen on the energy system under multiple scenarios. The insights gained from it serve as a first indication of how the white hydrogen market may develop and how it can contribute to the adaptation of hydrogen. This study could function as a wake-up call for the EU and other governmental bodies to take white hydrogen seriously and implement timely policies to maximize its potential, enabling a more optimal transition towards renewable hydrogen and benefiting society. Overall, this study showed that in most scenarios, white hydrogen has a significant impact on the energy system. In general, white hydrogen market growth will stimulate the hydrogen market, resulting in a higher share of renewables in the EU energy mix, increasing the chances of the EU reaching net zero. The timely adoption of white hydrogen policies could ease EU efforts to transition towards clean hydrogen. However, the EU should not bet on one horse and should continue its efforts to

The ongoing war between Russia and Ukraine has shown the importance of a well-organized military logistic system. The Dutch Ministry of Defence has concluded that their military logistic system is not used most efficiently. The need to build greater confidence in the ability of a defence organization as a whole to meet and sustain logistic support for future tasks has been acknowledged in the past. One such driver to build greater confidence is that of ‘trust’

Previous research on trust building between supply chain partners has been done manifold. However, the Dutch Ministry of Defence is a unique organization where the roles of distributor and client are within the same organization. Thus we are dealing with an intra-organizational supply chain, rather than a common inter-organizational one. Additionally, as a military organization the main priority of the supply chain is the responsiveness of the supply chain, rather than the efficiency. The uniqueness of the organization is the reason that additional research is needed on the role of trust within the supply chains with these special characteristics.

Multiple variables and their underlying relations, which create complexity and uncertainty, need to be addressed to implement policies that stimulate tryst within the organization. An System Dynamics (SD) Modelling approach will be used to access the dynamic complexity and uncertainty. The Exploratory Modelling Analysis (EMA) workbench enables the use and development of SD models, characterized by dynamic complexity, and deep uncertainty.
The results of the several analysis have given plentiful insights to consider regarding the future of trust dynamics between military supply chain partners. The initial values in a system such as this one have a huge impact on the progress of the model runs. Since the system consists of two major reinforcing feedback loops, Thus the system either strengthens or weakens itself, depending on the initial state of the model. Thus it is important to get the system to such a positive state, since it is likely to remain in a positive state.

As for the policy levers present in this system, the correction capacity lever seems the policy lever to have the most impact on the model outcomes. The correction capacity is the speed of which the incorrect orders get corrected. The results of the analyses conclude that regarding the state of trust in the model, it is crucial to keep this factor as efficient as possible. Since too low performance regarding correcting orders results in such delays that the trust level in the system completely disappears and does not come back within the time frame of the model run.

To summarize, I applied SD modelling and scenario discovery to explore the trust dynamics between a distributor and their client in a military intra-organizational supply chain context. Hereby I proved the importance of the initial state of the system, which due to its structure of reinforcing feedback loops is a big factor for its outcome. Additionally, it shows that the policy lever regarding performance by the distributor has the most impact on the trust level in the system. This insight tells us that performance is the biggest levers to adjust by policymakers to improve trust dynamics within their organization. I recommend policymakers to formulate strategies that give this insight to employees functioning as the distributor as well as the client. The interdependencies between one another should be emphasized. This should give the employees more incentive to improve their performance and thereby build trust between their collaboration partners.

Different leader-follower behavior may be observed in models, such as group gathering, backtracking, and flexibility of the group. However, a comparison of these behaviors resulting in possible substantially different estimates of optimal evacuation procedures is lacking. Hence, we developed an Agent-based model in combination with exploratory modeling to compare backtracking, group gathering, and the possibility to change to another leader and investigate their influence on the evacuation and response time to receive a robust result. The results showed that backtracking and flexibility of the group increased the evacuation time. Whereby group gathering impacts the response time. In addition, the combination of behaviors increases the influence on evacuation and response time. Further research needs to test these results with empirical studies. Furthermore, the impact of other leader-follower behavior needs to be investigated.

Crowd management is a crucial element in keeping situations safe. Models can help understand in-event crowd management system more thoroughly, and illustrate potential effects of measures before they have to be implemented in real life. However, applications of crowd models for operational support on in-event crowd management are sparse. Two main reasons are the cause of this: (1) inherent uncertainty regarding crowd modelling; and (2) computational requirements regarding large-scale applications. This research proposed three methodological steps for model-based experimentation—exploration, selection, and evaluation—to overcome current challenges in the application of crowd models. These steps were then applied on a case study of operational in-event crowd management at the Grote Markt, in the city of Breda. The research question thereby was: “What effect do the in-event crowd management measures—traffic regulators, directional guidance, and object placement—have on the density and walking speed of pedestrians in the Grote Markt, Breda?”. To answers this question, this study: (1) constructed a detailed microscopic crowd model of the Grote Markt, utilizing open-source crowd simulation framework Vadere for rapid, yet sophisticated, development of an agent-based model; and (2) applied this model according to the proposed steps for model-based experimentation, using techniques from the field of exploratory modelling and analysis. A connector between Vadere and the Python based Exploratory Modelling and Analysis (EMA) Workbench was constructed to synergize these two steps. Main findings highlight the potential of the proposed traffic regulator measure, and its effectiveness compared to object placement and directional guidance. With the proposal of the methodological steps, this work provides the needed stepping stone for operational support on crowd management. One that utilizes crowd models to understand in-event crowd management systems, and thereby enables the comparison between different in-event measures before they have to be implemented in real life.

In 2020, the world was hit by a global pandemic through COVID-19, causing millions of deaths worldwide. The last two years proved that a health-related crisis like COVID-19 strains us as a society and the capacities of health systems worldwide. Severe infections of COVID-19 require higher capacities of hospital beds, ICUs with ventilators and Personal Protective Equipment. For health systems, the question arises of how to secure enough PPE and ventilators before and during a health-related crisis like COVID-19.
This research explores how health systems can create responsive and prepared supply strategies to meet the rising demand for critical medical supplies during health-related crises. Concerning the availability of critical medical supplies, preparedness entails stockpiling as a supply strategy. Furthermore, preparedness entails actions decision-makers in health systems can take to increase the effectiveness of responsive supply strategies. Responsiveness refers to supply strategies used at the beginning of a health-related crisis in the short term. Responsive supply strategies include ramping up domestic production, supporting innovations, loaning medical supplies, and purchasing medical supplies from the world market or through direct tender.
In this research, a System Dynamics model was developed to explore how health systems can create resilient and responsive supply strategies, a System Dynamics model was developed, focusing on the English health system as an example for the Global North. Given the deep uncertainties surrounding the supply chain of critical medical supplies, Exploratory Modelling and Analysis was conducted to identify candidate policies and explore their performance given the uncertainties. Specifically, uncertainties affecting the success of purchasing supplies through direct tender and from the world market had the most impact on the availability of PPE and ventilators.
The results of the analysis and the findings in the literature provide the following recommendations to decision-makers in health systems to create prepared and responsive supply strategies that ensure the availability of critical medical supplies during health-related crises. Decision-makers can implement (i) stockpiles exhibiting higher inventory levels than recommended, and no delays concerning the operation within the warehouse or the delivery of the medical supplies. Furthermore, it is suggested to (ii) implement diverse supplier frameworks and crisis frameworks, (iii) diversify supplier, (iv) implement stocks of raw material for domestic production. It is useful for decision-makers to apply several responsive supply strategies simultaneously. Furthermore, health systems can further investigate additional supply strategies.
In this research, it is assumed that collaboration is not relevant nor of interest for the English health system. Hence, it would be interesting to explore the effect that sharing critical medical supplies between national health systems could have on the shortage of PPE and ventilators in the future.

The SARS-CoV-2 virus, more commonly known as the coronavirus, is arguably responsible for the biggest global crisis in recent history. In an attempt to effectively deal with this crisis, politicians around the globe have been using simulation models in their policy development. There are multiple types of modelling techniques used for epidemiological transmission modelling (Alsharhan, 2021; Anastassopoulou, Russo, Tsakris, & Siettos, 2020). Each different method used has different advantages and disadvantages related to them. One of the used simulation methods for transmission modelling is the Agent-Based Modelling (ABM) technique. The technique is a bottom-up approach, meaning it focusses on the behaviour of individuals to gather knowledge about the resulting emergent overall system behaviour. This technique specifically excels at developing early epidemic growth profiles, however, to gain this feature it needs to process a large amount of data. This data is not always readily available. Even if it is available, the amount of data that needs to be processed combined with the focus on individual behaviour, requires a lot of computational power for simulations making a robust uncertainty analysis very time consuming. An alternative technique is equation-based modelling, with System Dynamics (SD) being an instance of it with additional benefits regarding communication. This is more of a top-down approach, focussing on the overall mechanics of the systems instead of the behaviour of the individuals in the system. Working with aggregate values for most if not all variables to create increased understanding in the system behaviour under different circumstances. Because this technique works with these aggregate values, there is a less of a computational strain when simulating the model. However, this comes at the costs of being able to generate accurate early epidemic growth profiles, as this technique is not capable of fully incorporating key concepts for transmission models, such as heterogeneity of agents, spatial effects, and stochasticity. These two aforementioned modelling techniques, ABM and SD, have characteristic that lean themselves well to cover for each other’s weaknesses. Utilising a technique that dynamically switches between the two modelling methods depending on the state of the model, could incorporate the strengths both models have, this is called dynamic coarse-graining. These strengths are the accuracy and incorporation of key concepts for the ABM side of the model, combined with the simulation speed of the SD side of the model. This dynamic coarse-graining is still in its infancy, as research related to it is very limited. The goal of this research is to examine what dynamically coarse-graining an ABM model to a SD model would mean for the simulation speed of the model, and whether the results will stay consistent with the more accurate ABM method. The current research that has been performed on this topic has been on behaviourally stable models (Bobashev et al., 2007; Gray and Wotherspoon, 2012), meaning there are no changes to the behavioural mode of the model during the dynamic coarse-graining process. In this research two epidemiological transmission models are analysed. The first model will be a relatively simple model that is similarly incapable of exhibiting behavioural changes during the switching process. This Simple model is used as a test-case, for a more extensive SARS-CoV-2 specific model. This Extensive model will include the option of behavioural change during the switching process itself, meaning both the dynamic switching condition and agents’ behaviour is dependent on disease state. As an added benefit, by comparing the results of the two different models, the gained insights are more generalisable...

Over the last decades, the world population has grown vastly and the percentage of people living in cities has increased. This process of urbanization has had a lot of implications for both safety management and research on evacuations. Due to this growth in urbanization, more high density gatherings are taking place, potentially resulting in more casualties during emergencies. A common method used to conduct research on evacuations is simulation models. Simulation models have already improved our understanding of human behaviour during evacuations, but researchers argue that most current models are still not able to accurately describe human behaviour during evacuations since they do not take into account the effect of groups. Empirical research has shown that social groups in crowds influence the dynamics of evacuations. For instance, when an emergency happens and groups have to decide where to go, groups sometimes just follow other groups. However, accurately modelling such notions is often complex and contains uncertainty. One of the ways to deal with uncertainty systematically is by applying exploratory modelling. This, however, has yet to be applied to evacuations. This study aims to address the need for models which include the notion of groups by analysing the effect of two decision-making schemes on the evacuation time; leader-follower decision-making and consensus decision-making. Furthermore, this study aims to provide a stepping stone for exploratory modelling in the realm of evacuation modelling. In order to do so, this study uses three methods; a literature study, agent-based modelling, and exploratory modelling. The literature study was conducted to lay the foundation of the agent based model, while exploratory modelling was used to explore the uncertainty space of the agent-based model. After the development of the model, it was verified and validated using multiple tests, and data from a previous study. Through extensive validation and verification, it was concluded that this model is fit for purpose. It is both able to generate behaviour in the same magnitude as previous empirical research, and show valid behaviour on the lower abstraction levels of the model. Results show that groups have a significant impact on evacuation time. The more groups are present, and the bigger they are, the higher the evacuation time will be. Furthermore, results show that there is almost no difference in leader-follower decision-making and consensus decision-making. These two only differ when no one is familiar in a building. When there is 0% familiarity, leader-follower behaviour will lead to lower evacuations times compared to consensus decision-making. Lastly, results show that the combination of groups being present and all people being familiar with a building may actually have adverse effects on the evacuation time in crowd densities between 0.07 and 0.36. In case of crowd densities up to 0.36, a high percentage of familiarity may actually lead to higher evacuation times. All in all, this study provides a stepping stone for modelling group behaviour using an exploratory agent-based modelling approach. This study was the first to lay focus on the effect of group decision-making schemes by incorporating it in an agent-based model and exploring its behaviour by running it numerous times under different parameter settings. Furthermore, this research has important implications. First, evacuations inside buildings should not only be evaluated by crowd density or familiarity, but also by exit capacity. Secondly, different crowd compositions have different effects on the evacuations time. Policymakers should, therefore, take into account what types of groups will most likely be present. As regards future research, future research should mainly focus on the effect of modelling leader-follower behaviour in different ways, modelling consensus groups in general, analysing the effect of groups on different segments of evacuation time, and adding more (social) factors to the model which influence evacuation behaviour.

Evacuation strategies are critical in preventing casualties during emergency evacuations in buildings. As large-scale gatherings and high crowd densities in buildings occur more often, the need of relevant and effective evacuation strategies emerges. However, the domain of research that tries to identify possible ways to improve evacuation, i.e. prescriptive domain, is underlooked. Several studies successfully improve evacuation by optimizing existing evacuation scenarios in buildings. A shortcoming of these studies is that they often focus on one strategy and scenario in particular. Therefore, one should opt for a more generic approach to evaluate the effectiveness of evacuation strategies under different circumstances. A way to mitigate uncertainties in evacuation is by using data. Recent studies use a data-driven approach, in which data is used as an input to calibrate and enhance the evacuation strategy. A promising source of data is WiFi data. WiFi data captures movement patterns of building occupants and can be translated to population and building characteristics. Therefore, WiFi data offers the creation of evacuation scenarios in which evacuation strategies can be practically tested. This study aims to (1) evaluate the efficiency of evacuation strategies in buildings under different circumstances, and (2) determine effective evacuation strategies given WiFi data as an input. Therefore, this study presents a new exploratory agent-based approach to evaluate evacuation strategies, and moreover, presents an approach to incorporate input data to practically test evacuation strategies in a given building. To do so, this study used three methodological approaches, namely ExploratoryModeling and Analysis (EMA), Agent- Based Modeling (ABM) and Data Mining. EMA is used to experiment with the created agent-based evacuation model. EMA addresses the effect of uncertainties on the evacuation time, and if evacuation strategies are effective and robust in different circumstances. This study showed that in the created model of the TU Delft TPM faculty building, guiding evacuation strategies, such as dynamic signs and using evacuee staff members turned out to be an effective option if the familiarity in the building is low. However, as the familiarity increases the relative effectiveness of these strategies becomes negligible. In case of increasing familiarity, bottleneck improvement strategies, such as wider exits or stairs and obstacle placement, decrease the total evacuation time consequently. Moreover, this study concluded that an exploratory approach for evacuation models is promising, as the effectiveness of evacuation strategies is very dependent on the evacuation scenario. As a result, this study is able to evaluate these scenarios beforehand and to determine the effect on the total evacuation time. In this study the uncertainties crowd density, familiarity with the building, compliance with given instructions, and the exit capacity are leading in influencing the total evacuation time. The latter was found as a newly modelled uncertainty for evacuation scenarios.

Acceleration of the energy transition requires increased mining of materials. An important material for the energy transition is nickel, used in the stainless steel required for all energy infrastructure and an important component for both stationary batteries and batteries used in electric vehicles. Previous research has been done on the global nickel requirements for the energy transition at a high level of aggregation. In this thesis, exploratory system dynamics modelling and analysis was used to assess the resilience of the nickel supply chain and its nexus with the energy system at the level of individual mines. The development of the global nickel supply chain, and its energy requirements and greenhouse gas emissions, was modelled and explored between 2015 and 2060 under different disruption scenarios, sustainability policies and uncertainties. A nickel demand of 6 - 18 million tonnes per year is projected by 2060 for the BAU scenario, with projections up to 38 million tonnes per year in the scenarios that aim to limit global temperature increase to 1.5 °C. The main contributors to this large demand are electric vehicle batteries. The nickel system is conditionally resilient to the energy transition, given sufficient exploration and annual capacity increase. To increase the resilience of the nickel system, policies that support innovation in battery material composition and lifetime and good end of life waste management of batteries can play an important role. Modelling the nickel supply chain at mine level leads to different behaviour compared to previous research where mines were aggregated. Insights obtained from the detailed modelling in this thesis include a higher demand than previously projected; the possibility of the average ore grade increasing over time as mines with lower ore grades are decommissioned; average final energy requirements that can decrease, increase or increase rapidly depending on a varying average ore grade, a varying composition of processing methods and a varying composition of by-products; average energy costs that differ depending on the projected electricity mix in the countries containing deposits; and a reduction in end of life nickel recycling rate for most scenarios due to an increasing share of batteries. The most important contribution of this thesis is not in the data and assumptions, but in the model itself, which can be adapted and refined in further research, where more stakeholder input is included, to make the outcomes more robust and useful for decision making. Other important avenues for further research include determining how much exploration is possible and how quickly mining capacity can be increased.

For centuries humanity has been engineering the second largest river system in the Netherlands, the river Meuse system. This resulted in improvements for the shipping and water safety functions, but simultaneously has had a negative impact on other functions of the river Meuse system. Nature and water quality, for example, degraded due to the construction of dikes, weirs, locks, bend cut-offs, summer bed mining and floodplain reduction. Additionally, climate change is affecting the fresh water availability due to low river discharges in combination with little precipitation in dry periods. On top of that, weir leakage and levelling of ships lead to water loss, which amplifies the fresh water availability problems even more. What the future might bring is deeply uncertain, however, the current problems are expected to grow larger due to influence of climate change and socio-economic developments.With the upcoming task to replace the hydraulic structures (weirs and locks) in the Meuse river, a unique opportunity arises to improve the problems that the river Meuse system experiences and prepare it for future developments. Replacement strategies need to be developed and tested on the system. Traditionally separate models will be constructed to simulate shipping, nature, water quality and hydraulics. They tend to be complex and computationally heavy and more often than not, these models make predictions based on a small set of the most probable scenarios. However, in the rapidly changing times of today, these scenarios are often not sufficient enough to account for the highly uncertain future. Therefore, in this thesis, one model is constructed containing the multi-functionality of the river Meuse system which is tested on a broad range of future scenarios to test replacement strategies for the hydraulic structures.
MethodologyRobust Decision Making (RDM) is used to explore replacement strategies from a multifunctional perspective and evaluate them based on their robustness. RDM is a method which helps making decisions that lead to the construction of a robust system without making predictions and a system is robust if the applied strategy performs satisfactory under a wide variety of future conditions. RDM works with four steps, first the river Meuse system is modelled in the system dynamics software Vensim. This Meuse Model consists of submodels that simulate: the flow of water, the flow of shipping and the performance of the functions water quality, fresh water availability and the shipping load on the locks. All functions are therefore assessed in one model. The second step explores the behaviour of the current system by analysing the performance of the functions under future scenarios (i.e., combinations of future uncertainties). To cope with the deep uncertainty of the future, the probability of occurrence for each scenario is assumed to be uniformly distributed. Thirdly, design options are developed based on the vulnerabilities of the current system and combinations of design options form replacement strategies. In the fourth and last step, the replacement strategies are applied to the system in a policy analysis, after which the design options are evaluated based on their robustness. Exploration of the current systemExploring the current system behaviour over a variability of plausible futures demonstrates that in some scenarios problems arise for the functioning of the system. The performance of the fresh water availability is in general robust. The river Meuse system is designed to maintain a specific water level to provide for shipping. However, especially in weir section Grave, which contains outflow to the river Waal and a relatively large leakage of the weir at Grave, fresh water availability problems occurred during extreme drought events. In approximately 18% of the runs, the fresh water availability was smaller than full capacity. A scenario discovery revealed that several uncertainties have crucial impact on the undesired performance of the fresh water availability. It is found that leakage of the weirs and certain dry discharge years are influential uncertain input variables that cause problems for the fresh water availability.The water quality in the weir sections of the river Meuse reaches for most of the runs risky or undesired behaviour. The weir sections Linne, Grave and Lith are the most vulnerable because the weirs in these weir sections maintain relatively large water depths. This results in low flow velocities and therefore high risk for algal and cyanobacterial blooms and low diffusivity of toxic substances. For the intensity of shipping, two future scenarios are composed under socio-economic developments. The first leads to a slight increase of ship intensity and the second one leads to a slight decrease of ship intensity in the coming decades. Most of the locks seem to be able to cope with this change. Only the lock at Grave might experience issues as it contains the smallest and least amount of lock chambers in the corridor. For approximately 30% of the runs, the intensity to capacity ratio of the lock at Grave took either risky or undesired levels (>0.5).
Formulating replacement strategies
Based on these findings, a number of design options are formulated with the main goal to improve the robustness of the fresh water availability. Robustness performance of other functions, however, should not deteriorate, and preferably improve by applying the design options. Weir section Grave is used as main focus for improvement as this weir section is most vulnerable for future scenarios, and improvements will therefore have the largest impact. The design options are: •Installation of pumps (all weir sections): limit the outflow of water in dry periods•Heightening crest levels of the weirs at Grave or Lith: increase buffer capacity•Relocating the weir at Grave in upstream direction: increase the average water depth •Dynamic crest level (all weir sections): temporarily increase buffer capacity in drought periods•Efficient locking (all locks): limit the daily outflow of water in dry periods by imposing a minimum amount of ships that need to enter the lock chamber before starting the locking cycle
Analysing the replacement strategies
Combinations of design options are made to form replacement strategies, which are now applied to the system. All design options lead to improvement of the fresh water availability for weir section Grave. By introducing efficient locking, however, the intensity/capacity value of the lock at Grave increases compared to the current system for more than 80% of the runs. Raising the crest level of the weir at Grave leads to a decrease of flow velocity, and therefore water quality, for approximately 80% of the runs. Changing this to a dynamic crest level results in a decrease in flow velocity for approximately 25% of the runs, which is significantly less. By relocating the weir at Grave, the robustness of the fresh water availability in weir section Lith is slightly reduced and the ship draught downstream of the weir at Grave is at risk. To increase the water depth and remove the risk of insufficient ship draught, the crest level of the weir at Lith is raised. However, an increase in water depth leads to a decrease in flow velocity and therefore water quality. Installing pumps creates the largest robustness for freshwater availability, does not induce any deterioration for the other functions and is therefore seen as best performing replacement strategy. ConclusionThe research implemented a general robust decision making method to analyse complex systems and applied it to the replacement of hydraulic structures in rivers. Thereby it has shown how various design options can be evaluated for multiple functions and under uncertain future scenarios. This approach can be used to select design strategies that perform well under a broad scope of future uncertainties and thus make the system more robust. However, the model and tools used in this thesis also know many limitations. Costs, for example, are left outside of the scope of this thesis but is expected to have a large impact on the decision making process. Furthermore, potentially better fitting methods such as Many Objective Robust Decision Making or Dynamic Pathways are available to analyse replacement strategies. Because of these limitations, the results from this study can be seen as a first interpretation and setup for further research.

In the military field, detailed simulation models are often used to get insight in the consequences of choices regarding the composition of a force and the extent to which it can be kept deployable. With detailed models, the main idea is to capture all real-world complexity relevant for decision-making processes. However, detailed models tend to be ‘bad’ in the evaluation of uncertainty in a system. This can be problematic, as the outcomes of detailed models usually depend on numerous dubious assumptions that are too buried to be understood by hand. As simplified models can better cope with uncertainty, this study examined the combined use of a detailed model and a simplified model (this approach is also known as ‘multi-resolution modelling’) to improve the robustness of a naval force support system. In order to work with models with different levels of detail, an existing detailed model that reflects certain elements of the force support system of the Dutch Royal Navy is used to develop a new simpler model.

The key takeaway of this study is that multi-resolution modelling can be considered useful as the detailed model and the simplified model were found to be mutually dependent in their design, validity, and use to improve the robustness of a naval force support system. As the detailed model contains a more accurate depiction of the system of interest, it formed together with a set of aggregation functions and cross-validation tests the basis for the design and validity of the aggregated model. The detailed model also remains important for the evaluation of the real-world effectiveness of promising ‘high-level’ actions discovered by the simplified model. Vice versa, by means of exploratory analysis, the simplified model revealed some potentially problematic assumptions in the detailed model which has implications for its design and validity. Also, the simplified model gave insight into (a) problematic scenarios that lead to a low sailing availability, (b) patterns in the robustness of policies across different usage profiles, and (c) potential leverage points to increase the robustness of the naval force support system. This study concludes with a number of scientific research recommendations involving challenges in strong consistency and the composability of models at different levels of detail.

Accidents in buildings happen frequently and if people are not evacuated in time, this can have major consequences. The behaviour of building occupants is one of the most critical determinants herein.
Evacuation behaviour consists of two phases: the response phase and the evacuation movement phase. During the response phase, a building occupant is notified of an incident and performs a series of information and action tasks. When the response phase is finished, a building occupant will initiate movement towards an exit or safe place during the evacuation movement phase.
In this thesis, the focus is on response-phase behaviour. There are many factors influencing response-phase behaviour, four of these are: culture, cues, affiliation and setting.
Culture is defined as "the collective programming of the mind distinguishing the members of one group or category of people from others". For this research, national cultures have been considered.
Cues are any kind of changes in the environment, which indicate that something is not normal. Affiliation encompasses the tendency for people to seek friends or colleagues. Lastly, setting limits the knowledge obtained and the type of actions which can be performed based on the location of the building occupant.

The following research question has been answered: " How does culture, in combination with cues, settings and affiliation, influence response-phase behaviour and time and total evacuation time ? ”. To answer the research question, a case study was introduced. In this case study library evacuations have been considered in Czech Republic, Poland, Turkey and the UK. Within the context of this case study a questionnaire and an agent-based model have been developed.

The results show that that there are significant differences in the number of response tasks being performed. Turkey performs the highest number of response tasks, followed in a decreasing order by Poland, Czech Republic and the UK. Furthermore, it has been found that response behaviour in all countries is influenced by cues, setting and affiliation, which results in significant differences between the countries for their response and evacuation time. It has been found that, as with the number of response tasks, Turkey has the highest evacuation and response times, followed in a decreasing order by Poland, Czech Republic and the UK. Lastly, it has been found that affiliation and being informed by a staff member highly affect response and evacuation times, while the setting and seeing fire do not. The degrees to which these factors influence response and evacuation times differ per country.

Overall, this research acknowledges the importance of performing cross-cultural research for evacuation behaviour. It has shown the need for policy makers and emergency planners to discuss effects of culture on evacuations. Additionally, it provides a new approach to study the effect of cultures, in combination with cues, setting and affiliation, on response-phase behaviour and response and evacuation times.

Greece was amongst the first countries to be hit after the outburst of the European financial crisis in 2008. The crisis revealed the underlying intra-Eurozone macroeconomic imbalances and turned the attention of the global community and investors towards the very high level of Greek public debt. Private investments in the Greek economy also suffered a heavy downfall since the beginning of the crisis. The level of private gross capital formation kept decreasing since 2008 and forecasts estimate an almost null growth for 2020. This research tries to answer the question of what the main factors behind the decline of private investments are and which policies can re-activate them. To investigate further and answer the question about the link of public and private investments a System Dynamics model of the Greek economy is built based on a traditional macroeconomic approach. With the use of the System Dynamics model for the Greek economy. Next, the Exploratory Modelling & Analysis (EMA) Workbench tool is used to perform the analysis. Two different policies are introduced. The first one concerns a mix of lower taxes and lower government investments and the second one is based on a policy of higher taxes and higher government investment. Sensitivity analysis with the use of SOBOL technique and scenario discovery analysis with the use of PRIM are used to explore the most sensitive factors with regard to private investment.

Vehicle automation has the potential to disrupt the status quo of urban transportation, because it adds a new mode of transportation by taking away the task of the driver. The projections of estimations of the impacts of this new technology are based on many uncertainties and are thus largely unknown. A literature review showed that a wide number of effects is possible with automated driving and that no straightforward method to assess future impacts of this new technology exists yet. This thesis developed a method that provides insights into the impacts of vehicle automation in urban areas, without preconceived ideas about the impacts of the different scenarios. Existing knowledge from literature, transportation and land use data, and sociodemographic information were combined in a geographically disaggregated
System Dynamic model. This model explored the effects of vehicle automation on the performance of the transportation and spatial system of the case-study city of Copenhagen, Denmark. Different model runs provided insight in the possible range of outcomes. Considerable problems may arise in the transportation network with the introduction of automated driving because, using the car might become very attractive. A city’s land use does, however, not change as much as many could expect. The causes of (un)desirable outcomes
were identified with the Patient Rule Induction Method. The ranges of uncertainties in the value of time in an automated vehicle and in the level of adoption of car-sharing were found to influence desirable versus undesirable futures the most. Mitigating measures should focus on these scenarios to prepare for a future with automated driving.