War is always evolving, becoming more complex over time. With the addition of new technologies and emerging threats the complexity on the battlefield is also growing. Following this trend tools to train new recruits or develop new doctrines also becomes more complex. One of the tools impacted by this is military wargaming. While wargaming is an ancient practice it has developed to follow the growing complexities of war. In the context of military training wargames portray multi-domain battlefield containing artillery, helicopters, tanks, infantry, planes, terrain and much more. To keep the wargames significant and executable the implementation of AI into military wargames was explored. This proved to have an impact on aspects such as data processing, decision making and strategies. Further research shows that while possible and useful the implementation of AI into military wargaming is of an ad hoc nature and lacks any guidance or standardization. That is why this thesis set out to develop a practical and guiding framework for wargame developers for the implementation of AI into military wargaming.

After an exploratory literature review the knowledge gap is identified in the crossing between AI, wargaming and their integration. This lead to the main research question being “Which framework can guide wargame developers in the field of AI and military wargaming through the implementation process ?”. To answer this main research question several sub questions are developed and answered using several research methods.

The first sub question is: “What are the current challenges and constraints in the implementation of AI into military wargaming and how can they be addressed? This question is answered in chapter one with the use of a literature review. It results in more depth into the problem and more insight into the knowledge gap.

The second sub question is: “What are the requirements, that the framework aims to achieve?” This question is answered in chapter two with the use of another literature review and expert interviews. This resulted in three design principles and several requirements. The principles, requirements and their sources can be found in table 4. Furthermore, the result of the expert interviews highlighted the value of a supplementary method, when academic literature is not abundant.

The third sub question is: “How can the design principles and requirements be translated into a framework?” Based on the requirements a preliminary framework is developed. This framework is than tested with a use case to find areas of improvement. The use case was a validation implementation of AI into the military wargame called “Take That Hill”. This resulted in a framework consisting of several phases and activities. The result can be seen in figure 7. Furthermore, the outcome of the use case illustrated the wargame is not properly balanced as it can be won by simply always moving forward. In addition to the direct relation between the requirements and the translation into the framework another finding took place. The use case illustrated the importance of testing the preliminary framework. As the use case uncovered a new dimension in the framework with the core activities, which were not apparent from the theory alone.

The last sub question is: “What is validity of the designed framework?”. This was answered by assessing whether the requirements were met and expert interviews. The requirements assessment can be found in table 6 and the final version of the framework can be found in figure 8. Furthermore, while the validation expert interviews directly improved the framework they also showed the importance of the diversity of experts interviewed. The experts for the requirements focused on time and security, while the experts for the validation focused on the ethical and moral aspects of AI in military wargaming.

In conclusion, this thesis presents a multidisciplinary approach to solving a problem in military training and doctrine development. The development of an practical and guiding framework for AI implementation in military wargaming represents a significant advancement in the field, offering both academic contributions and practical applications. By aligning with the goals of the Master’s program in Complex Systems Engineering and Management, this research exemplifies the integration of complex and complicated domains, ultimately contributing to the enhancement of military preparedness and operational effectiveness.

As this thesis was written as part of a graduation internship at TNO the research recommends the wargame developers of TNO to use this framework as a guide during future implementations of AI into military wargaming and expend on the framework when necessary.  

This thesis investigates the performance of the DeepCASE[13] alert-filtering system when incorporated into a multi-system security detection environment and compares its efficacy to other state-of-the-art alert-filtering tools, such as the Integration Layer in[30]. The main research question addressed is: How does the DeepCASE system perform in a setup with multiple security detection systems, and how does its performance relate to other state-of-the-art alert-filtering systems? This research is of critical importance given the growing complexity of cybersecurity environments, where various detection systems frequently create overwhelming amounts of alerts. Key Findings: • Metrics for Evaluating Alert-Filtering Systems: SQ1 looked into relevant metrics for determining the efficiency and efficacy of alert-filtering systems. It was shown that general performance metrics can be misleading in complex environments. Granular metrics, such as false negative and true positive rates, yield more meaningful evaluations. In this study, the false negative rate was ranked first, followed by workload reduction, as key indicators for evaluating DeepCASE. • DeepCASE Performance with Multiple Detection Systems: SQ2 evaluated DeepCASE’s per- formance in a multi-system setting. DeepCASE achieved promising results, with a 0% false negative rate and an 87% reduction in workload. These findings demonstrate its usefulness in managing alerts across several security detection systems while maintaining accuracy. • Improvements for Multi-System Integration: SQ3 looked at potential improvements to Deep- CASE’s performance in a multi-system environment. We tested with a variety of tactics, including alert aggregation and incorporating security analyst expertise. It was discovered that incorporating security analysts’ expertise via the ”IL Preprocessing approach” considerably increased performance, resulting in a 0% false negative rate and an impressive 96% workload reduction. This emphasizes the significance of expert-driven preparation in improving alert filtering. It should also be noted that out of all of the attempted alert aggregation approach, the 1-Minute window alert aggregation also managed to provide some improvement, achieving 0% false negative rate with a 90% workload reduction. • Comparison with State-of-the-Art Systems: SQ4 compared DeepCASE’s performance to a state- of-the-art alert-filtering system, namely the Integration Layer. DeepCASE performed remarkably well, outperforming the Integration Layer when not all of the latter’s preprocessing approaches were used. However, the paper notes that a properly optimized integration layer may have different outcomes. DeepCASE is highly effective in situations involving multiple security detection systems, significantly reduc- ing workload while retaining zero false negatives. However, expert-driven preprocessing techniques signifi- cantly improve its performance. While it compares favorably to other cutting-edge systems, further research is needed to fully understand the potential of these systems when completely tuned. This thesis advances our understanding of how alert-filtering systems can be evaluated and adjusted in complex, multi-system situations, giving useful insights for improving cybersecurity operations.

This thesis explores the phenomenon of greenwashing, particularly its systemic origins and contributory factors within corporate and regulatory frameworks. Through the analysis of three prominent case studies—Volkswagen, DWS, and Shell—it demonstrates that greenwashing is not merely the result of isolated corporate actions, but a complex, multi-layered symptom of wider sociotechnical influences. The research adopts Rasmussen’s risk management framework alongside Salmon’s systems-thinking approach to identify and map out systemic failures leading to greenwashing. Findings indicate that the most influential factors originate from management policies and regulatory oversight, showcasing the critical role of internal and external governance structures. The study’s conclusions emphasize the need for stricter regulatory audits and the cultivation of transparent corporate cultures. The analysis also provides insights into the ethical implications for sustainability-focused business practices, offering a taxonomy of systemic influences that contribute to greenwashing. This work aims to inform future strategies for regulatory bodies and businesses to mitigate greenwashing and promote genuine environmental responsibility.

The scale and complexity of projects in the construction sector have grown significantly in recent years. Larger projects typically result in worse performance, including overspending and delays in the calendar.
This research focuses on identifying and analyzing Early Warning Signals (EWS) to mitigate schedule delays during the design phase of infrastructure projects managed by Sweco, a consultancy company in the Netherlands. The primary aim is to explore how effectively EWS can be used to identify potential issues early and prevent project delays.
The research begins by assessing the current knowledge and application of EWS among project managers, controllers and directors at Sweco. Initial findings indicate that while most participants have a basic understanding of the concept of EWS, they do not actively use or recognize it in their daily workflows. Their awareness of these signals primarily stems from gut feelings and extensive experience rather than systematic identification and application.
To identify the most relevant EWS, the study combines insights from existing literature with practical experiences shared by Sweco experts. This process involves creating a comprehensive list of potential EWS, which is then narrowed down and prioritised based on their perceived impact and likelihood. The prioritisation is similar to a risk assessment method, considering both the probability and potential impact of each signal.
The study examines how to measure data related to the signals and proposes measures on how to handle those EWS better. Some of the suggestions include establishing clear working agreements, balancing formal and informal communication, enhancing collaboration through workshops and visual tools, and leveraging advanced technologies like AI and BIM. The study further emphasises the importance of continuous client communication, internal quality checks, and ensuring appropriate expertise for project tasks.
The research provides a comprehensive framework for identifying, measuring, and managing EWS to improve project outcomes and reduce schedule delays in infrastructure projects. The findings emphasise the need for systematic approaches and technological integration to enhance early warning capabilities and project management practices. By implementing the recommended strategies, Sweco can better anticipate potential issues, make informed decisions, and ultimately deliver projects more efficiently.

Keywords: Early Warning Signals (EWS), schedule delay, design phase, infrastructure projects, project management, measurability, prioritisation

This research integrates a socio-technical perspective into the safety risk management of ammonia-powered ships, with a particular focus on the engine room. The objective is to develop strategies that enhance crew safety by examining the complex interactions between humans, organizations, and machines within this high-risk environment.

The international shipping industry, responsible for a significant portion of global greenhouse gas emissions, faces an urgent need to adopt more environmentally friendly technologies. Anhydrous ammonia (NH3) has emerged as a promising alternative fuel due to its carbon-free nature and higher volumetric energy density compared to other fuels. However, ammonia’s toxicity presents substantial challenges, especially in enclosed spaces like the engine room, where crew members are at increased risk of exposure.
Current research predominantly addresses the engineering aspects of ship design to minimize ammonia leakage risk but lacks a focus on safety risk management through a human-centered approach that enhances crew safety. This research addresses this gap by adopting a socio-technical system (STS) approach, emphasizing the intricate dynamics and implications of human interaction with new technology. The study demonstrates that incorporating a socio-technical perspective through the Functional Resonance Analysis Method (FRAM) provides a comprehensive understanding of human-machine interactions, enabling the development of human-oriented modifications.

Employing a multidisciplinary approach, this research combines qualitative and quantitative methods. FRAM is used for qualitative analysis, offering detailed insights into interactions and performance variability within the socio-technical system. This research developed the complex STS relationships through Work-as-Done FRAM, identifying actor roles, time spent, location, and procedures related to engine room activities. Complementing this, Fault Tree Analysis (FTA) and Quantitative Risk Assessment (QRA) provide a deeper understanding of risk factors and their implications.

The findings indicate that adopting a socio-technical approach to safety risk management can significantly enhance the safety of ammonia-powered ships. This research has developed modification frame works that integrate human-machine interaction activities derived from FRAM with necessary system modifications. The study identifies on-board maintenance activities as having the highest probability of crew presence in the engine room (PER). By modifying the engine room layout and work procedures with the on-board maintenance as considerations, the Individual Risk Per Annum (IRPA) can be substantially reduced from the current value of 1.33E − 05 to 2.00E − 06. Normatively, these modifications result in an IRPA level that is lower than that of the currently operating LNG (CH4) powered ship engine room, which has an IRPA value of 3.16E −06. Thus, these modifications meet the risk acceptance criteria as outlined by the IGF Code guidelines.

The research provides several recommendations for stakeholders within the maritime industry. These include modifications to engine room layout and task locations to minimize human exposure to ammonia leaks, advancements in on-board maintenance procedures specifically for ammonia, enhancement of gas sensor response times, and digitization of the engine room logbook. These strategies aim to ensure safe operations and support the adoption of ammonia as a sustainable maritime fuel.

This study introduces HydroWizard, an innovative framework addressing critical challenges in water resource modeling through enhanced transparency, efficiency, reproducibility, and extensibility. Integrating a YAML-based Model Specification Language with a sophisticated Execution Engine, HydroWizard enables accessible modeling of complex water systems.

Applied to the Lower Omo-Gibe River Basin in Ethiopia, the study employs Evolutionary Multi-Objective Direct Policy Search to identify 283 Pareto-optimal policies. Findings reveal nuanced trade-offs: irrigation-optimized policies eliminate demand deficits but reduce environmental flows by up to 48%, while environmentally-focused policies show opposite effects. Notably, mean power generation remains relatively consistent across policies, challenging assumptions about water resource allocation trade-offs.

HydroWizard introduces novel visualization techniques, including animated rule curves and system state graphs, enhancing strategy interpretability. Its versatility is demonstrated through application to diverse water systems, including the Zambezi River Basin.

This research marks a significant advancement in water resource modeling, offering an open-source, accessible tool for complex water system analysis. It contributes valuable insights for sustainable water management and sets a new standard for global water resource management studies, emerging as an innovative solution to intensifying water management challenges worldwide.

Construction accidents are known to occur often; in 2022, there were 129 accidents in the Netherlands for every 100,000 workers . Beyond the tendency to place the responsibility on employees, this study investigates the impact of client involvement in enhancing safety performance. The majority of safety research focusses on the building phase, indicating that client influence decreases as projects progress. The study highlights 43 crucial client responsibilities with a focus on pre-construction phases, specifically in the Planning, Inception, and Feasibility; Design, and Procurement and Tendering phases. Moreover, Semi-structured interviews reveal differing practices between infrastructure and oil & gas projects, providing the opportunity to learn from these differences and highlighting areas for improvement, such as establishing stricter safety objectives, early contractor involvement, and enhanced knowledge sharing. The findings suggest that optimizing client roles in pre-construction phases can significantly enhance safety performance in construction projects.

The expansion of offshore wind farms in the North Sea has raised serious concerns about maritime safety, in particular the increased risk of collisions—or 'allisions'—where a ship adrift hits a stationary structure, such as a wind turbine. For Rijkswaterstaat, the executive agency of the Ministry of Infrastructure and Water Management, managing this expansion requires developing a comprehensive understanding of the integral image of allision risks of drifting ships in the Dutch North Sea region. This understanding must be valid, reliable, and insightful, and include factors such as dynamic weather conditions and shipping activity. Unfortunately, current state-of-the-art methods for estimating the probability of allision rely heavily on assumptions, leading to significant variations in risk estimates, as highlighted by Ellis et al. (2008). These methods usually lack a North Sea-wide perspective and instead focus on risks specific to individual turbines.
This study proposes an approach based on trajectory prediction using the OpenDrift model, which is simple and accessible for simulating ship drift trajectories based on wind, wave, and current data. A method is developed to estimate the probability of allision by combining the probability of a ship drifting into a wind farm, analysed through a ship's potential trajectories, with ship density data for specific locations. This method facilitates the calculation of allision risks under the influence of different environmental conditions and can be applied to multiple wind farms, providing a detailed assessment of the origins of potential threats and available response times.
Overall, the assumptions in state-of-the-art methods might not sufficiently capture the risks associated with drifting ships. In addition, the established method broadens the ability to improve monitoring accuracy and optimise the positioning of ERTVs, even for specific environmental conditions. As a next step, this method should be extended to estimate actual risk levels by considering the potential consequences of an allision. With this approach, decision-making regarding allision threats can be contextualised alongside other maritime safety risks, facilitating the development of a robust risk mitigation strategy that enables Rijkswaterstaat to responsibly exploit the abundant potential the North Sea has to offer.

This thesis investigates the integration of Large Language Models (LLMs) within large organizations operating in low resource language (LRL) regions, with a particular focus on Íslandsbanki, one of the prominent banks in Iceland currently grappling with the adoption of LLM technology. The research aims to understand the frameworks governing LLM integration within the bank, explore stakeholder perspectives on the integration process, and identify blind spots and gaps in their understanding of associated risks in order to understand what a comprehensive approach would look like.

The methodology had mixed methods. It consisted of a comprehensive literature review followed by interviews with stakeholders involved in the LLM integration at Íslandsbanki. Subsequently, an Impact-Probability Assessment survey is conducted, where stakeholders are asked to evaluate the identified risks in terms of their potential impact and probability. This approach facilitates the identification and prioritization of risks, thereby enabling future integrators to navigate the integration process more effectively. Furthermore, data analysis techniques were applied to the survey results to provide quantitative insights into the identified gaps and perspectives.

Findings reveal that perspectives on risks associated with LLM integration vary significantly, particularly between individuals with technical backgrounds and decision-makers within the bank. Moreover, the concept of text inflation emerges as a novel risk factor, highlighting the importance of considering diverse perspectives in risk assessment and mitigation strategies. Emphasis is placed on adopting a sociotechnical perspective to comprehensively assess risks at an organizational level.

This research contributes to the understanding of the organizational impact of LLM integration, offers insights into diverse risk perspectives, and underscores the importance of considering sociotechnical factors in the integration process. By shedding light on the challenges and opportunities associated with LLM integration in the financial sector, this study aims to inform future research and practice in this evolving domain.

The abstract outlines a study focusing on improving the design approach for flexible dolphins, vital marine structures used for vessel berthing and mooring. Current design methodologies, particularly those outlined in the CROW C1005 handbook (2018), are questioned due to potential conservatism stemming from insufficiently calibrated partial factors. The study advocates for reliability-based assessments to address these concerns, which consider uncertainties inherent in dolphin design and quantify failure probabilities over their lifespan. The investigation identifies critical failure modes and determines main pile dimensions based on these modes, utilizing the API PY-curves for rapid computation in the design process. Probabilistic assessments, employing Directional Sampling, reveal the significance of berthing load in structural safety and identify soil parameters as dominant variables affecting fixity failure mode. Navigation conditions, ship arrival rates, and variation in ship sizes also influence partial factors, with recommendations provided for adjustments based on different conditions. The study suggests load testing to reduce uncertainties and increase reliability, exemplified by a Bayesian update from Calandkanaal full-scale load tests. Overall, reliability-based assessments yield insights into managing uncertainties in dolphin design, potentially reducing material usage by up to 20% while meeting safety requirements, and offering the potential to decrease failure probabilities tenfold when combined with test loading.

Phishing attacks are a growing cause of cybersecurity incidents such as data breaches. With these attacks, malicious actors try to gain access to systems by exploiting the vulnerability of employees. Particularly, intruders use different tricks to create convincing phishing emails to force people to behave less securely. Unfortunately, technological applications are insufficient in detecting all possible phishing attempts. As the consequences of a successful breach can be disastrous for a company, especially in the banking sector, pressure is put on employees to recognise and report potential phishing emails. To aid them in this task, phishing training is provided by employers. Employees are taught what phishing emails look like and which actions they should take if an email appears malicious. However, the effectiveness of these training sessions is difficult to evaluate.

To understand the interaction of employees with phishing emails without influencing their behaviour, we study the emails employees report as suspicious to characterise the security culture at a bank. A better understanding of the behaviour provides grounds for recommendations to improve anti-phishing training and create a safer environment. The newfound metrics can provide an alternative to current methods.

This research uses Exploratory Data Analysis (EDA) to evaluate the email reporting behaviour of employees at a bank to answer the Research Question How can email reporting patterns in a large organisation measure the relationship between phishing training, reported emails and employee behaviour? With this case study, we apply EDA to a large dataset containing bank employees' reported emails over 16 months. We analysed the reported emails and related them to the provided phishing training events. Moreover, we did a text analysis of the emails' content using the Term Frequency - Inverse Document Frequency (TF-IDF) method. Additionally, we extract the dominant topics of the emails using topic modelling. Lastly, with the help of interviews, the results are tied to employees' experiences to understand their behaviour.

The major findings of the research are, firstly, the new metrics we identified to measure the security culture of a company. These metrics were found from both the analysis of employee behaviour over time, as well as the analysis of the email content. From the analysis of the reporting behaviour over time, new metrics include the unique reporters in relation to the total reported emails over time. Besides the unique reporters, unique reported emails can uncover the presence of campaigns. For example, a single email can explain the increase from 50 to 350 daily benign reports. The difference between the total reports and unique reports uncovered this. Secondly, topic analysis and content comparison show similarities between benign and malicious reported emails, indicating an increased vigilance of employees on these attributes.

A second finding originates from the analysis of the email components. One of the components was used in all simulation emails, while it was not present in all the benign and malicious reported emails. This shows that the simulation emails can be extended to include different scenarios. Therefore, we recommend the company to extend the phishing simulation emails to contain varied phishing tactics to expose employees to other types of attacks and incorporate all aspects taught during the E-learning.

Lastly, the analysis shows no concrete relation between the number of reported emails and the timing of the simulation wave. Although the reported p-value of benign emails after the simulation is 0.03, this significance can also be explained by external factors.

With these results, we can measure employees' security culture and awareness in real-world circumstances without influencing the employees' behaviour, providing a new approach to investigating phishing behaviour. Adding to the research of Steves et al. (2020),, the click rates can be explained by more than solely the employees' awareness levels, and new explanations come forward to handle phishing threats. Moreover, the absence of a required test environment for the analysis created a solution for existing gaps. For example, as seen in (Hillman et al., 2023).

A limitation of exploratory data analysis is that results are often ambiguous and mainly provide possible directions for future research. Furthermore, external factors influencing the behaviour could provide alternative reasons for the discussed interactions.

To conclude, by using the reported emails to measure security behaviour related to phishing, we found new metrics which do not influence the employees in their daily behaviour while still providing insights to improve the tactics of a company in combating phishing attacks. Reporting behaviour can be used to analyse the current anti-phishing tactics of a company and provide suggestions for improvements.

Future research should explore the differences in applying the method in other companies and across sectors. Overlap and differences can create an understanding of the diversity in security culture and the effect of external factors. Combining these results with a comprehensive understanding of a company's operations can expose directions for improvement in the security approach. Additionally, the effect of the recommendations can be analysed using the metrics we proposed. This can be done with a follow-up analysis of the behaviour to see whether the desired effect can be observed.

The Sahel region has become a focal point of concern due to the alarming escalation of terrorism, culminating in devastating attacks that have claimed numerous lives and disrupted regional stability. Mali, in particular, has experienced a significant surge in terrorist activity. This thesis presents a novel approach of using criticality measures based on network science as pay-offs for a game theoretical model in order to protect connectivity of road networks in conflict areas. Through two comprehensive literature reviews the network scan approach, based on shortest paths, population sizes, travel distances, and travel times and the two-player zero-sum game structure were selected as the basis for the methodology. In order to enhance understanding of dynamics on the road network, semi-structured expert interviews were conducted. The model is applied to the region of Segou in Mali and optimal strategies were compared to empirical terrorist attack data of the ACLED database. It is concluded that road network protection can be strengthened to minimize the impact of terrorist attacks through the allocation of protective resources. Firstly, experts highlight the positive impact of combining military patrols and convoys with explosive ordnance disposal on road safety. Secondly, results revealed the network scan approach's potential as a valuable tool for criticality assessment. Thirdly, contexts lacking empirical traffic data, directing network protection efforts towards roads identified as optimal strategies by the model becomes prudent. Lastly, the comparison of optimal strategies against emperical data provides the means enhance insurgent behaviour's understanding. While the developed model can be considered rudimentary, it is believed to have laid down essential foundational principles. Further research is recommended the extend the model further, including validate the model's effectiveness and determine its applicability to different types of attacks and different insurgency groups. This thesis hopes to have made a contribution to the academic fields encompassing counter-terrorism, network science, and game theory within conflict settings.

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.

Digital challenger banks in the UK retail banking industry have caused unprecedented Personal Current Account (PCA) market share changes at the cost of traditional banks. This loss in competitive position prompted traditional bank managers to reconsider the way they operate, resulting in the commoditization of digital services till date. Now, the industry is faced with another potentially disruptive strategic innovation: sustainable business models. Adopting such a business model is, however, a complex decision for bank managers, as they need to ensure that the resources and capabilities of their bank align in an optimal manner to achieve competitive advantage. On the one hand, there is evidence in favor of sustainable business models as they have been associated with lower bank risk, better (financial) performance, and higher stakeholder value. In addition, consumers and policymakers increasingly expect UK retail banks to adopt a sustainable business model. On the other hand, there are also many uncertainties regarding the disruptive potential of sustainable business models - in part because there is no insight on how consumers might respond. Bank managers are thus faced with a dilemma on whether to change their business model at the cost of existing revenue for potential future profit.

In this study, we aimed to address these latter risks by providing insights into the mechanisms that underlie changes in the PCA market shares of UK retail banks and investigating what the future composition of the UK retail banking industry might entail when banks adopt sustainable business models. The UK retail banking industry is, however, a complex, open, uncertain, and dynamic multi-actor system in which the competitive position of banks depends on the strategic decisions of themselves and their competitors. As a consequence, there are many plausible future states of the system that can only be explored by the quantitative simulation of banks’ interactions. Previous literature advising bank managers on the adoption of sustainable business models fail to do so.

Due to the lack of detailed data on the consumer response to sustainable business models, however, we could not employ existing market share models that generally rely on the statistical analysis of this detailed (time-series) data. As such, we developed a novel simulation model that used the language of game theory in the context of agent-based modeling (ABM) to understand the complexity of customer market share dynamics. Specifically, the model simulated the consumer choice of bank behavior in response to the strategical decision-making of 20 banks on a variety of topics (e.g., sustainability, digital capabilities) during a seven-year time period. Consequent to being the first model of its kind that allows the study of the disruptive potential of a strategic innovation, we note that the model is unique in its ability to (i) simulate the endogenous decision-making by the banks under study in response to the evolving state of the system, (ii) simultaneously include (potentially) competitive effects (i.e., customer switching volumes) and (potentially) market-expansive effects (i.e., PCA market growth), and (iii) systematically address system uncertainties.

Using exploratory modeling of 12,000 alternative future scenarios, we show that PCA market shares are quite robust (i.e., between 0.5-2% decrease) to different factors such as the customer switching volume, PCA market growth, consumer choice of bank behavior, and different types of sustainable banking implementation strategies (e.g. being a leader or follower). As to the mechanisms that do underlie the marginal changes in the market shares of UK retail banks, we identify a role for improved digital capabilities of mid-tier banks resulting in an improved competitive position of these banks. In addition, smaller specialized banks in the UK may capture some market share, but this growth is limited as serving more customer segments is associated with less overall satisfaction and a consequent potential decrease in market share. This trade-off means that the organic growth of smaller banks in the UK retail banking industry is limited. We furthermore show that low PCA market growth, low consumer preference towards banks’ sustainable operations, and moderate to high consumer preference towards banks’ service quality and digital services significantly contribute to limiting the market share decrease of big 4 banks to max. 0.7%. Finally, we also show that PCA market pressure is not sufficient for all UK retail banks to adopt a sustainable business model. As such, we conclude that sustainable business models are not directly associated with PCA market share dynamics and that they have a minimal disruptive potential.

While numerical inaccuracy limits confidence in the simulation model outputs, the findings of this study can be used to draw general policy directions for UK regulators and bank managers.
To regulators:
• Focus on increasing competition through unique new entrants, as increased switching volumes currently have little effect on dynamics in customer market shares due to too little competitive differentiation between retail banks;
• Introduce additional incentives for retail banks to adopt sustainable business models, as PCA market pressure alone is insufficient to introduce a sustainability transition in the UK retail banking industry within the next seven years;
• Facilitate consistent data collection on a number of bank KPIs and make this data publicly available to aid future research onto the competitive positions of banks by both regulators and the private sector.
To bank managers:
• Do not focus on building customer market share, as one will likely capture customers that open a secondary (or even tertiary) account which is often operated with a very low margin (if not at a loss);
• Redefine how the customer value proposition is designed, as sustainable business models might have non-tangible side effects such as more loyal and less price-sensitive customers;
• Set up specialized PCA products to focus on an underserved small customer segment to avoid competing with the bulk of PCA providers;
• Use monetary incentives to compensate for a lacking performance on sustainability, digital capabilities, or service quality, as these incentives effectively dampen the dynamics in customer market shares;
• To mid-tier managers, investigate the potential to capture market share, as this study indicates that a potential upswing in mid-tier market share is possible.

Finally, we urge bank managers to consider the ecological consequences of sustainable business models when designing their policies. Precisely because there is no direct link between sustainable business models and customer market share, one can adopt such a business model without fearing the competitive position. The plausibility of irreversible climate change does mean that investment in measures for dealing with these circumstances, and not just greenwashing, should be taken sooner rather than later. Future work should focus on facilitating such multi-objective decision-making for bank managers by extending the model KPIs (e.g., consumer deposits, sustainability metrics) and by improving the model structure to increase numerical accuracy.

The rapid development of technology gives many opportunities but brings threats as well. The digitization of the financial sector has made the threat for cyber attacks significant. Cyber attacks such as the Petya virus or the Wannacry attack have exposed the vulnerability of the critical infrastructure. The financial sector is especially prone to cyber attacks within the critical infrastructure. The financial sector must be available at all times. Even a minor disruption could cause wrongful or unexecuted transactions leading to cascading effects on careers, organizations, or entire industries. The way to cope with disruptive cyber events is through digital operational resilience. In order to ensure this the European Commission has proposed the Digital Operational Resilience Act. A regulation that harmonizes existing guidelines aimed at ensuring resilience against cyber attacks for the financial sector in the EU. To estimate the regulatory performance of the DORA, insight is to be created into the perceptions of the stakeholders. Therefore, the research question of this thesis is: What is the perception of the financial sector towards the expectation of the Digital Operational Resilience Act? In order to answer this research question, interviews were conducted with high-level security managers of financial service organizations (FSOs) in the Netherlands, ICT providers and supervisory authorities. From insights gathered through the interviews, it can be concluded that the participants share a predominantly positive perception of the DORA. They were confident about there ability to be compliant within the given 24 months. The DORA is also seen as a step in the right direction towards a digital operational resilient financial sector. Recommendations are made for supervisory authorities regarding the implementation and supervision of the DORA and for the FSOs on the implementation and compliance with the DORA.

With the advent of a smart society and an era of connectivity, there remain a numerous amount of challenges yet to be solved. One of the key IoT innovations analysed in this research is known as Unmanned Aerial Systems (UAS). UAS, or drone technologies, allow carrying out repetitive and dangerous tasks with almost no human intervention or supervision (Fernández-Caramés, Blanco-Novoa, Froiz-Míguez & Fraga-Lamas, 2019). The advent of drone utilisation in both public and civil domains has led to application areas such as realtimemonitoring and surveillance, parcel delivery, search and rescue missions (e.g. ER), remote sensing in agriculture and multiple other application domains (Valavanis & Vachtsevanos, 2015). However, the fast pacing development of interconnected devices and systems has outrun the human understanding and experience of usage, imposing challenges with regards to technical security, trust and of course privacy (Coetzee & Eksteen, 2011). Subsequently, most drones are limited to computing, storing and sharing data, making them vulnerable to adversarialattacks. A need for a more responsible adoption of drone-use exists and must be fulfilled with care. To this, a blockchain-based solution is proposed.The focus of this research is on a Dutch independent agency part of the Ministry of Agriculture, Nature and Food Quality, called the Netherlands Food and Consumer Product Safety Authority (NVWA). They have been actively using drones since 2017 and their main tasks consist of supervision, risk assessment and risk communication with the goal to protect the human and animal welfare (NVWA,n.d.). An explorative research approach was taken whereby the current bottlenecks experienced by the NVWA were identified and visualised, by means of desk research and semi-structured interviews. Subsequently, the sociotechnical effects of the use of drone detecting services by the NVWA were identified to obtain the key values for a responsible redesign. In this way, the role of the values, an understanding of the roles of institutions realizing these values, and the stakeholder engagement is understood. After identifying the open issues and conflicting values, a redesign phase began with the trade off between three mitigating security techniques.Blockchain technology is found to be the most suitable security technique in this research and fills in the ’lack of transparency’ gap which is crucial for a responsible redesign. Although it hasn’t reached full maturity and could induce undesired delays, it increases the security level of the organisation significantlyand takes into account the key values security and privacy. Moreover, the data managing method allows users to validate, maintain and synchronize the content of a transaction ledger which is replicated across the other users in the network (Tapscott & Tapscott, 2017).The solution has proven to work in existing literature and automotive contexts and is therefore generalisable in multiple on-going projects of the NVWA. Subsequently, the novel data managing method could be used as for revenue models such as digitised inspections. This indicates future growth options andpotential competitive advantages. All in all, the benefits outweigh the uncertainties of this technologyand it is believed that a blockchain based solution will be the next big step to achieve more responsible implementation of drones.

Offshore floating photovoltaic (OFPV) gain increasing attention in research and from project developers, due to the need for more renewable energy production. OFPV must be proven to be both technically and economically feasible to be a viable option in the offshore environment. Several incidents of floating PV systems have been observed for mainly inland applications of floating PV. These incidents, which occurred due to malfunction of the system, may induce several risks. Hence, the risks associated with OFPV technology should be assessed, as these risks may affect the feasibility and overall support of the developments. However, the current early stage of industrial development present a gap in the standards and guidelines, as well as the availability of data on all levels, to perform a risk assessment. The aim of this research is to outline the process of assessing technological risks resulting from loss of function (failure) of operating offshore floating photovoltaic systems. The general steps of risk assessment procedure are performed on an OFPV system in this thesis. In the initial step, a generalized OFPV system was modeled and analyzed, which includes the functional decomposition of the system to element-level and a quasi-static load analysis. Then, a preliminary hazard analysis (PHA) is performed to identify a broad scale of hazards and failure mechanisms of the OFPV system in its environment, followed by a qualitative failure mode and effect analysis (FMEA), to relate the identified hazards to the components of the OFPV system. Based on the FMEA and observed incidents, it was found that failures of the mooring lines are a major threat to floating PV systems. To determine the quantitative likelihood (probability) of OFPV system failure in relation to its components, a reliability analysis was performed with a fault tree analysis (FTA). The fault tree analysis includes sensitivity analysis with Birnbaum and Fussell-Vesely importance measures, to allocate the most important elements that contribute to failure of the system. Due to lack of probabilistic data for OFPV, element failure rates were estimated based on substitute data sources, which made the results are prone to data uncertainty. Therefore, structural reliability analysis is included, since this systematically includes uncertainties and correlations, by providing a probabilistic method to determine the failure probability of an element of the FTA. As mooring failures were identified as a major threat to OFPV, this element was analyzed in more detail. Initially, a limit state function was defined to describe the condition under which a mooring chain failure occurs, in terms of random load and resistance variables. The first order reliability method (FORM), including the Rackwitz-Fiessler transformation and the Rosenblatt transformation, was used to iteratively determine the probability of failure of the failure event. Metrics to assess the contribution of the random variables to the probability of failure are included with sensitivity factors. To determine the consequences of mooring failure, hypothetical accident scenarios were modeled with an event tree analysis (ETA). Finally, the results obtained from the previous steps of the risk assessment process are summarized and evaluated. The risk matrix and ALARP principle were introduced, to evaluate the acceptability of the risks. In addition, the contributors to uncertainty of the results are qualitatively assessed per step. This research presented the tasks and potential difficulties that an analyst may encounter when performing a risk assessment for an OFPV system. Guidelines and standards for risk assessment of OFPV are desirable to evaluate the technical and economic feasibility of several concepts, towards further industrial development of the technology. Moreover, this research did not intend to obtain actual quantitative results for a specific OFPV design or concept, mainly due to the lack of descriptive and probabilistic data. With further development of OFPV technology, and as more data becomes available, this research can serve as a foundation for future risk assessments of offshore floating photovoltaic systems.

The use of Global Navigation Satellites Systems is increasing rapidly. More and more applications use positioning and/or timing information form a Global Navigation Satellite System (GNSS). Also more and more people and applications rely on high-precision positioning based on GNSS. The high-precision solution of GNSS is achieved with the use of example augmentation data. For example real-time kinematic (RTK)-GNSS enables centimetre-level positioning. Commonly the augmentation data is sent with the use of internet. At the moment an unsecure internet link is used to sent this augmentation data from the reference station to the user. The aim of this study was to find out if it is possible to manipulate the augmentation data for DGNSS using a cyber attack without being detected, and what the consequences could be for the final estimated parameters of interest. The parameters of interest can be the position and/or the timing. The augmentation data is sent using the Networked Transport of RTCM via Internet Protocol (NTRIP). What is found is that this is an unsecure connection. For an attacker it is possible to use a man-in-the middle attack, where the augmentation data is sent from the reference station, via the hacker, to the user. The data is not encrypted and therefore it is possible for the hacker to see and alter the data. Based on a man-in-the-middle attack this study found that it is possible to manipulate the DGNSS augmentation data, without detection. The model that is used to manipulate the augmentation data is based on a Single Point Positioning model. As long as the manipulation is in the range of the design matrix of the used model, it is not detectable. This means that the manipulation only contributes to the so called influential bias and not, or minimal, to the testable bias. As the name suggest, the result of this manipulation is that the final solution is manipulated due to the effect in the influential bias, and without detection since the testable bias is not changed. GNSS processing is based on non-linear observation equations. This means that those models are linearised before the final solution is estimated based on the least squares estimation. The effect of this non-linearity is minimal, but it means that a (very) small part of the manipulation contributes to the testable bias. This study points out that this small increase of the testable bias is insignificant when the observations are tested based on an overall model test and the w-test. The conclusion of this study is that it is possible to spoof the augmentation data when NTRIP is used to sent the augmentation data. Furthermore, the consequence of augmentation data spoofing is that it can be exactly manipulated by the hacker, based on a certain direction and distance, as long as the magnitude of the manipulation is in the order of 2 to 3 meter.

Building failure incidents are occurring within the Netherlands and these incidents can be a valuable source of technical data regarding improvements in structural safety. This research has attempted to utilize this information gained to develop a tool to aid the building construction sector in identifying hazards during building construction projects. For that purpose, this research has investigated risk assessments and structural failure incidents. To be able to develop a tool, deeper knowledge of risk assessments is required and therefore risk assessments were studied to understand how they should be performed and what characteristics makes them effective. The research of Terwel (2012) on the Cobouw database formed the basis for the analysis of structural failure incidents. This thesis investigated how other research used structural failure databases to improve structural safety and studied the data available from the Cobouw database for developing a tool. The analysis of risk assessments has shown that identifying hazards is a crucial step in the assessment process. It was concluded that it would be beneficial to a project team if it had information on which hazards related to structural failure can occur, the probability of occurrence of hazards and the possible impact they can have. An investigation into structural failure databases showed that no other research could provide a tool that fulfilled the above requirements and in addition it was concluded that the database available to this research was only sufficient to provide information on which hazards can occur. There was not enough data available to make a justified estimate regarding the consequences of a hazard or probability of occurrence. Therefore it was chosen to use a different approach to analyze the database. The Cobouw database has been restructured into a fault tree format, to relate hazards to specific components of a building structure. Afterwards an attempt was made to discover why many hazards have occurred or which building components were prone to hazards. There was not enough data available to draw conclusions on that subject either. Finally this research concluded that the most efficient way of developing a tool, from the data available to this research, is to create a guide which can be used during the design phase to caution the project team about frequently occurring hazards. The method that was used to create the tool can also be used as an inspiration for future research, because this research was limited not only by the amount of available incidents, but also by the source of the incidents, which was from a news site that would most likely not focus on low-profile failure incidents and the reporting of precise technical details. Therefore the most important recommendation this research has, is to introduce a large scale collaboration in the construction industry focused on gathering accurate data on structural failure incidents.