Climate-induced migration modelling is a popular way of assessing changes in population flows due to climate shocks. Although many models prove that modelling gives compelling insights into migration systems, there is a lack of models that capture micro-behavioural aspects while maintaining holistic realism. This thesis fills this gap by combining Computable General Equilibrium (CGE) data with an Agent-Based Model (ABM) for micro-behaviour modelling. The migration decisions are bound by three migration theories: the New Economics of Labour Migration (NELM), pushpull theory and Foresight’s main factors of migration. Results show that the model can capture behavioural micro-decision making. Holistically, the model can capture migration flows, although accuracy is limited, mainly due to a lack of longitudinal household-survey data and datasets on intertwined social connections. Finally, the model shows that micro-macro coupled climate-induced migration methodologies can provide new and valuable insights within academics, policy design and policy validation.

The energy transition is creating congestion on the low-voltage electricity grid, threatening reliability and slowing sustainable development. To address this, Distribution System Operators (DSOs) are turning to household flexibility measures. However, most current approaches assume households behave uniformly, overlooking important behavioral differences.

This thesis uses an Agent-Based Model to simulate five household profiles (Conscientious Individuals, Structure Seekers, Status-Driven, Responsibles, and Self-Developers) and their participation in three flexibility measures: smart EV charging, solar PV curtailment, and flexible heat pump control. Awareness campaigns are included as a soft intervention. Profiles and parameters were informed by literature and interviews with Dutch DSOs. Scenarios reflect current conditions and two future policy paths, including the planned abolition of net metering.

Results show large differences in participation across profiles. Conscientious Individuals and Responsibles adopt early, while Status-Driven households are least likely to engage. Summer feed-in peaks can be fully mitigated in ideal conditions, but reducing winter peaks proves more difficult due to limited uptake of heat pump flexibility.

The study shows that behavioral segmentation adds critical value to energy modeling. Targeted engagement strategies based on user profiles can help DSOs design more effective, inclusive flexibility programs, aligning grid stability efforts with real-world household behavior.

This research investigates the influence of socio-technical factors on the evacuation performance of university buildings. While prior studies have examined individual factors affecting evacuation, this thesis adopts a comprehensive socio-technical systems perspective, by considering the interactions of social, structural, and technical components within the complex context of university building evacuation systems.

To explore this, an agent-based simulation model was developed using NetLogo. The model simulates evacuation scenarios in two structurally different campus buildings at TU Delft: the Applied Sciences building and the Civil Engineering & Geosciences building. The key variables studied were familiarity with the building layout and exits, social influence behaviour, egress width, and signage. Evacuation performance was assessed using three metrics: 75% evacuation time, mean density, and exit choice.

Following a full factorial simulation experiment of 54 different scenarios per building totalling 10800 individual runs, a standardised ranking was created to equitably rank the scenarios based on their relative evacuation time, density, and exit choice, to determine the performance of the factors. A general linear model was created to determine the effect size of both the individual factors and all possible interactions.

The simulation results demonstrate that familiarity and egress width had the most significant impact on evacuation efficiency. In buildings with limited exits, egress width outweighed the effect of familiarity. While signage and social influence showed modest or statistically non-significant impacts overall, signage became more effective in low-familiarity contexts. The effect of social influence appeared to be sensitive to its level of formalisation in the model, underscoring its context-dependent nature.

Strengths of this study lie in the multi-metric perspective on evacuation performance, the use of multiple buildings to test effects in different structural environments, the possibility to include all possible interactions through a full factorial design, and the adaptability of the simulation model. Also, this study has several limitations, namely the balance between model correctness and performance, the difficulty to detect behavioural patterns, and software-based limitations.

From a policy perspective, the findings suggest that improving occupant familiarity with building layouts, through orientation, drills, or signage, can substantially improve evacuation performance. Furthermore, structural adjustments such as widening exits can mitigate congestion in critical zones, although its effect is dependent on hallway width. Although advanced signage technologies offer some improvements, their individual effectiveness is limited without complementary strategies.

The study highlights the importance of addressing evacuation preparedness as a multi-factorial challenge, especially in complex university settings where population heterogeneity and architectural diversity intersect. The model and methodology offer a flexible tool for future research and scenario testing.

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.

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

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.

The IPCC report of August 2021 calls for an immediately reduction in greenhouse gas emissions. Without changing the world’s diets, even if we stop all fossil fuel emissions today, we would still not be able to maintain global warming below 1.5°C. The EU has set out their strategy for reducing agricultural emissions in the Farm to Fork Strategy, as part of the European Green Deal. This strategy, however, falls short of addressing meat consumption. Individual meat consumption is shaped by a range of factors, including social norms. The influence of these norms is investigated in this thesis, which addresses the research question: “how do social norms influence meat consumption and to what extent can European policy influence these to reduce meat consumption?”. This thesis takes the Netherlands as case study, and constructs an explorative agent-based model, based on the Theory of Planned Behaviour, where social norms spread through social networks. Individual meat consumption is broken into beef, pork, poultry, processed and substitute meat consumption. The factors of influence are analysed in this research through a correlation analysis of various surveys, and the likelihood to eat specific meat types is calculated through a least-squares multiple regression analysis. This research finds that social norms play a tangible role in shaping consumption, and targeting these can emissions from meat consumption by 0.4-4%. Fiscal policies are a more effective policy measure for the EU to follow, as a 20% tax on all meat can reduce emissions by 10%. A tax on beef was found to redistribute consumption in such a way that overall emissions do not decrease. The findings from this research are discussed in the political, policy and socio-economic contexts regarding meat consumption.

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.

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.