Federated learning is a promising method of distributed machine learning that can allow industries such as aviation to utilize their data without actively sharing it, but has issues regarding cooperation if proper incentives are not utilized. This thesis explores the problem and builds from a previously designed FL Framework by designing an IT system that can be used to control FL Tasks and Rewards. Using an institutional analysis to set the playing field and based on previous work, an IT artefact consisting of a set of smart contracts on a private blockchain were designed and implemented as an MVP. These contracts perform the role of organising and controlling the participants within FL tasks and can be used to distribute rewards in the form of two types of Tokens: The Reward Token which also can be utilized for voting in proposals and reward distributing as a representation of ownership, and the Model Access Token, which can be used to represent Model access.

The latest state-of-the-art large language models (LLMs) are implicit computational models of humans due to how they are trained and designed. This implies that LLMs can be used as participants in economic and behavioural experiments. However, their technical and ethical limitations have sparked ongoing debate about the usefulness of LLMs in experimental research. This study contributes to the research area by reproducing the “Flip a Coin or Vote” experiment conducted by Hoffmann & Renes (2021), with GPT-3.5 and GPT-4o as participants.

The findings indicate that GPT-3.5 (1) struggles to fully understand the rules of the experiment, especially in calculating payoffs; (2) has difficulties interpreting negative and positive valuations; (3) fails to match the percentage of rational choices observed in the lab results; and (4) demonstrate results that deviate too much with the lab results to be considered human-like.

In contrast, GPT-4o shows greater promise as a ‘good’ participant. GPT-4o understands the rules of the game correctly and demonstrates a much stronger ability to make rational choices, even matching, and sometimes surpassing, the percentage of rational choices made by human participants. However, in the second part of the experiment, GPT-4o's decision-making becomes "inhumanly" accurate. Moreover the results deviate too much with the lab results to be considered human-like.

Future research should be focused on how to steer the results of LLMs towards more human-like results, possibly through enhanced prompting techniques or further model training and fine-tuning. Furthermore, future research should focus on establishing best practices for developing a more standardised approach to conducting simulation research with LLMs.

In recent years, the introduction of shared micromobility options such as scooters, mopeds, and bikes has transformed transportation in densely populated urban areas. Interestingly, when looking at shared moped availability, it can be observed that they are rarely available in less densely populated areas, such as the suburbs around large cities. This can be explained by the fact that financial viability for private operators is unlikely in these regions because of the lower demand compared to highly densely populated areas. Nevertheless, the introduction of shared mopeds to these suburban regions comes with many external societal effects. These effects can not be internalised by a private operator but could be internalised by a governmental body. However, from a governmental perspective, allowing and supporting shared mopeds in their region is only interesting if these external effects result in a societal welfare increase.

To this end, a societal cost-benefit analysis (SCBA) has been conducted to evaluate and quantify the societal effects of introducing shared mopeds in the suburban region of Schiedam. Using this analysis, an overview of the net societal benefit of shared mopeds and the profit/loss for an operator in a suburban region has been obtained. Seven conceptualised effects were valued quantitatively; operator setup cost, operator operating costs, operator revenue, accessibility, carbon emissions, noise emissions, and traffic safety. Three conceptualised effects were valued qualitatively; parking nuisance, public space freed, and the option value. Furthermore, two shared moped operator business models have been investigated; free-floating and hub-based models. To show the uncertainty of the net societal benefit figure a sensitivity analysis has been performed from the perspective of an operator. This sensitivity analysis has three scenarios: worst, nominal, and best.

Accessibility and safety are the most important positive and negative externalities, being responsible for over 80% of the net societal benefit in all scenarios. The SCBA net societal benefit is positive, for the nominal scenario when an operator uses a free-floating model. The net societal benefit becomes negative when using a hub-based model. The difference in the net societal benefit value between the worst (very negative) and best (very positive) is large. This shows that there is a high amount of uncertainty in the SCBA result, which is caused by not all effects being included and safety and accessibility having a considerable influence on the overall societal benefit figure. Nevertheless, this research provides a framework to investigate shared moped operator viability and the societal impact in a (potential) region. Moreover, it gives insight into that safety and accessibility effects should be prioritised when discussing the introduction of shared mopeds into a suburban region, but also which effects still need to be investigated further.

Traditional financial companies play a central role in ensuring financial stability and reliability. However, these companies face institutional problems due to inefficiencies, lack of transparency and limited innovation. For this reason, the use of distributed ledger technology, or blockchain, has been explored as an alternative and its integration into traditional finance towards a 'hybrid' approach. The Dutch mortgage market was chosen as the focus of this research and is further analyzed due to its high transaction costs, including the high cost of trust. This led to the research objective of designing a Hybrid Mortgage Lending System (HMLS) that reduces the cost of trust.

A design science research methodology was used for this research where two interwoven artefacts were developed. The first artefact was a contextualized trust model based on three trustworthiness factors: integrity, ability and benevolence. The second artefact was a demonstration for the HMLS using a technological model.

The trust model was validated through expert interviews and enriched with additional information. Based on the feedback from the expert interviews, nine meta-requirements for the technological model were developed. The proposed solution for the design of the HMLS is to automatically distribute fractional ownership of mortgages via Non-Fungible Tokens (NFTs) through an ecosystem with the traditional finance company as originator and public investors as lenders. The ownership of the NFTs is transparently tracked via the blockchain to monitor the fractional ownership of a home. These NFTs exist in mixed token pools and are controlled by the traditional finance companies. These pools are supplemented with other digital assets to create different risk profiles to suit different types of investors. A demonstration of these token pools is shown, with an overview of the architecture explaining the mortgage application and transaction processes through the different layers. Finally, a stakeholder map is shown and their roles within the ecosystem are explained. The research provided new insights by making the second model dependent on the first. The findings highlight new challenges for further research, such as prototyping or improving the versatility of the model.

This study addresses the growing demand for expanded train capacity in the Netherlands, particularly during peak hours. The current rail network is constrained by space and high infrastructure costs, prompting the exploration of policy measures aimed at encouraging alternative travel behaviours among students. This research evaluates four policy alternatives—ranging from requiring students to pay full peak fares to shifting lecture times—assessing their impact on student travel behaviour and overall welfare using Cost-Benefit Analysis (CBA). Findings indicate that while three of the alternatives result in a welfare loss, the 'Shift Lecture Hours' alternative yields significant welfare gains. The recommendation from this research is therefore to collaboratively develop the shift in start times in education into a comprehensive set of policy changes that enjoy broad support from students, teaching staff, and educational institutions.

This paper explores the potential of smart contracts in the reinsurance industry to address escalating non-productive costs driven by market dynamics. Reinsurers, aiming to enhance stability amid increasing inflation, claim severity, and frequency, have adopted stricter underwriting criteria and raised premiums. This has led to a more detailed drafting of reinsurance contracts, reminiscent of the formalization trend that began in the 1970s. However, this formalization has inadvertently inflated administrative and dispute resolution costs, counter to the industry's core objective of efficient risk and capital allocation.

The study delves into how smart contracts can offer a solution. Smart contracts are computerized protocols that can automate contract clauses, potentially aligning better with industry goals. The examination focuses on their impact on transaction costs, involving expenses incurred by insurers and reinsurers to execute transactions.

Findings reveal that smart contracts can effectively reduce administrative costs by automating tasks, particularly in high-volume and standardized scenarios. However, their effect on dispute resolution costs is more nuanced, as the reinsurance sector still benefits from human interpretation.

In conclusion, while smart contracts hold promise for reducing transaction costs in reinsurance, the industry's unique complexities and high financial stakes may pose challenges and necessitate post-implementation adjustments. The paper recommends exploring smart contract applications in industries with smaller disputed amounts and lower trust levels than reinsurance.

The Paris Agreement seeks to limit global temperature rise and achieve climate neutrality by 2050. The UN's Sustainable Development Goal 7 emphasizes sustainable energy access, with smart grids playing a crucial role. These grids adapt to user behavior and interconnect various components. While smart grids address challenges in integrating renewable energy, their complex socio-technical nature demands a multidisciplinary approach. Institutions are vital in smart grid development, but their importance often goes unnoticed. The interplay of stakeholders, technology, and institutions complicates smart grid implementation. Although existing research largely focuses on technical aspects, a holistic approach considering social, economic, environmental, and institutional dimensions is essential. This study aims to bridge this gap by employing the Institutional Analysis & Development (IAD) framework to enhance the Netherlands' electricity/energy system design, particularly at the community level, concerning the impacts of institutional rules on smart grid development and implementation.

The chosen research approach is a comparative case study, focusing on smart grid integration in the Netherlands. The Institutional Analysis and Development framework aids in analyzing complex socio-technical systems, particularly energy transitions and smart grids. Three cases were examined: Schoonschip Amsterdam, GridFlex Heeten, and Groene Mient The Hague. Data collection involved literature review and 14 semi-structured interviews with experts and stakeholders. Interview data was deductively coded using the IAD framework, facilitating comparative case analysis and cross-case analysis using the ASI diagram (Actors, System & Institutions).

The transition of the Dutch electricity system into a competitive liberalized market has increased the importance of smart grids for sustainability. Decentralized energy production, facilitated by smart grids, requires technological advancements and institutional changes at various levels. Successful smart grid implementation in the Netherlands involves obtaining exemptions, feasibility studies, and engaging legal entities like homeowners' associations and energy cooperatives. Cases emphasized the significance of pre-exemption feasibility studies, technological requirements, stakeholders like Distribution System Operators, and energy management systems.

The findings highlight the uniqueness of each case and the role of institutional rules. In Schoonschip, boundary, position, choice, and payoff rules were pivotal. GridFlex highlighted position, choice, information, and scope rules. Groene Mient showcased boundary, position, choice, payoff, and scope rules. These case studies underscore complexities in decision-making and governance within smart grid initiatives. Successful implementation requires considering technology, institutions, and stakeholders as indicated by the ASI diagram. The IAD framework analysis reveals the impact of biophysical conditions, community attributes, and rules-in-use on smart grid outcomes.

Recommendations include further research on evolving rules, understanding actor positions, interdisciplinary research, integrated decision-making frameworks, empirical case studies, and policy recommendations such as financial support, clear policy roles, local smart energy systems, positive framing, standardized data exchange, and umbrella organizations for cohesive local smart grid projects.

In conclusion, the study underscores the intricate nature of smart grid implementation, emphasizing the need for comprehensive consideration of technology, institutions, and stakeholders. The IAD framework sheds light on the complex dynamics, guiding effective decision-making and policy formulation for successful local smart grid development.

The modern power grid is becoming more susceptible to cyber-attacks due to an increase in digitalization, leading to a larger attack surface for malicious actors to attack. Such attacks on critical infrastructure could lead to partial power outages, minor societal disruption, or in the worst-case scenario, a rolling black-out in which the entire country has no access to electricity. Electrical utility companies can decrease the likelihood of a successful cyber-attack on the Cyber Physical Power System (CPPS) – consisting of the physical power grid, and vulnerable Information Technology (IT) and Operational Technology (OT)- by implementing cyber security interventions. Investing in these cyber security mechanisms is not cheap, which is why it is expected to have a certain return on investment. However, it is hard to quantify the effects of prospective cyber security investments. The main research question of this study is: “To what extent can cyber security measures decrease the risk of cyber attacks on CPPS substations?” This research question is answered by means of an implicitly mixed research approach that uses computer-assisted attack tree modelling and Monte Carlo simulation. The model is based on the publicly available technical system information of known suppliers of relevant substation components and other documentation acquired by means of multiple literature studies and document analyses. The change in likelihood and subsequent risk has been studied by extensively modelling the possible attack paths of a digital substation. This has been combined with financial analysis in the form of a societal cost-benefit analysis. As a result, potential cyber security investments can be evaluated on their merits in the form of risk reduction and their required costs as expressed in dollars. The contribution of the performed research to science is the elaboration of existing models to more accurately represent reality, and simultaneously provide the cyber security decision-making process with a tool that provides guiding Key Performance Indicators (KPIs). This study has shown that suggested measures from the quantified model are able to increase the TTCavg needed by malicious actors to reach their intended target, and therefore cause a decrease in likelihood and subsequent risk of the studied scenarios. An important finding of this study emphasizes the need for extensive attack path modelling. This finding was the fact that the application of some well-intended countermeasure (such as remote-attestation), might have no significant effect on the likelihood and risk of a certain scenario at all, but only changes the dominant attack path. While the constructed quantified model, as proposed in this study, is able to provide quantified insights into the effects of proposed cyber security investments, it is merely a simplified tool that should be expanded upon to generate more accurate insights. Besides the aforementioned there have been additional findings from this study. Such as a list of weaknesses in the current state of digital substation cyber security. This list has been created by an extensive document analysis of over 40 sources. Also, an overview of 23 different possible cyber security interventions has been compiled by a systemic literature review of over 16 sources. According to the quantified model, a reduction (between 21.8% and 93%) in the total risk of certain attack scenarios against digital substation by malicious actors can be achieved. The costs for these possible risk reductions range between $28 thousand for a honeypot deception system and $413 thousand for a combination of all the simulated countermeasures. These countermeasures could, in comparison to a base case with no protection, potentially reduce the total risk by an amount between $3.7 billion and $15.9 billion. According to the general societal cost-benefit analyses, the best Retun-on- Investment (ROI)/cost-effectiveness of investment is the investment in a honeypot (scenario 5) which has an ROI of 247,390, and the least cost-effective is the investment in remote attestation (scenario 4), which has an ROI of -2,066. Altogether, this study has shown that there is added value in using a simplified quantified model to aid in decision-making for digital substation cyber security investments aimed at risk reduction.

The Architecture, Engineering, and Construction (AEC) industry is undergoing digital transformation to improve project performance and Building Information Modelling (BIM) is helping to achieve this objective. BIM is a comprehensive digital representation of a built facility with great information depth. BIM fosters collaboration across the construction supply chain however the benefits of collaboration are realised only in a relatively small number of BIM-enabled projects. Poor collaboration continues to be one of the major risks affecting BIM-enabled projects with misunderstandings, misinterpretation of data and increased rework. This is especially high in the design phase of construction projects because of the high BIM usage. This research aims to explore the most critical barrier to collaboration in BIM teams in the design phase of infrastructure projects and provide strategies to avoid the barrier in future projects. The most critical barrier in this research is defined as “the main issue that causes the occurrence of problems resulting in undesired outcomes”. For this objective, the case-study method is preferred. Three large infrastructure projects are studied and interviews were conducted with BIM teams to identify the barriers to collaboration in BIM teams. The data collected are analysed by comparing among cases and with literature. Why questions were asked until the main issue that led to the occurrence of problems is found. From the analysis, it is found that lack of guidelines and standards is the most critical barrier to collaboration in BIM teams in the design phase of infrastructure projects. To avoid this problem in future projects, the existing BIM-based design process is improved to work with ISO19650 standards along with strategies to achieve better results. To enable better collaboration in BIM teams, the process is automated using Business Process Management (BPM) software. The proposed strategies are validated with BIM teams for applicability in practice. To conclude, the proposed strategies help in solving the lack of guidelines and standards. It improves the BIM-based design process in infrastructure projects and leads to achieving better collaboration in BIM teams. This research benefits researchers as well as BIM teams. Researchers can validate the proposed strategies in real-life projects and BIM teams can improve their work practices by implementing the proposed strategies.