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.

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.

This research tries to quantitatively identify the relation between team composition, organization structure, communication and team efficiency. The study takes a model-based approach to reach its objective. This research stresses the utility of agent-based modeling as a tool to first replicate the environment of an on-going project in India and then systematically introducing organizational changes into the project team to quantify various impacts.

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.

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.

The negatives effects of criminals are a threat to the Dutch society. In 2019, 15 percent of the Dutch citizens are victims of High Impact Crimes (HIC). Policy-makers have been mainly focused on disseminating information to businesses and individuals on how to protect themselves and their properties against crime and violence. However, an increased arrest rate is likely to have the largest impact on reducing the negative effects of HIC. Therefore, this research aims to explore the criminal escape route decision-making, to understand the choices made by criminals during their escape, resulting in a higher arrest rate. Since there is a lack data on escape routes, the modelling approach is used for this research. Therefore, a simulation model is developed to predict the prospective fugitive escape routes. The developed discrete-event model for criminal escape route decision-making within Rotterdam is developed based on both expert knowledge from the Dutch Police and the dual-process theory for criminal’s decision-making developed by van Gelder. Analysis of the interviews held with the Dutch Police shows that, type of crime, location of crime, and time of crime are varied to explore the escape route decision-making of criminals. From the analysis of the model outcomes, we conclude that organized crime criminals prefer the bigger (S and N) roads to escape the spatial area. Whereas local criminals prefer smaller (S and collector) roads to escape the spatial area. Traffic density influences the destinations and road use of organized crime criminals in a more distributed use of smaller roads instead of the bigger S and N roads. The location of crime results in different behavior in terms of destination, road use, and choices. The acquired knowledge of this research can result in catching the criminals faster after the crime, due to improved knowledge on potential decisions a criminal could take. Further research should focus on low-level decision-making to give researchers more detailed insights in criminals escape route behaviour. However, additional research is necessary on how personal and micro-level spatial factors influences the criminal escape route decision-making. Also, further research should focus on developing a real-time simulation model for the Dutch Police to predict possible locations of the criminals during the fugitive escape. Therefore, implementing additional features (e.g., live traffic, traffic lights, and micro-level spatial characteristics) to increase the feasibility of the simulation model is recommended. However, additional research is needed on how the additional features influences the criminal escape route decision-making.

Background. Dutch universities struggle to find effective evidence-based intervention tools to reduce the high prevalence of sexual violence among their students. Reducing sexual violence means a safer student climate and thus better inclusion of women and more diversity in higher education. Interesting approaches to consider for cultural change are bystander intervention and an already existing co-designed serious game. This game aims to encourage a dialogue among students but has thus far not been evaluated on its effectiveness. Therefore, the goal of this study was to evaluate how a serious game can promote an intergroup dialogue between female and male students in Dutch universities to encourage bystander intervention in situations of sexually transgressive behaviour. Methods. Literature research and brainstorm sessions with female and male students from Delft University of Technology (TU Delft) were conducted to gain a better understanding of sexual violence among students. These methods were used as part of participatory game design research. Moreover, a quasi-controlled experimental trial was conducted with 64 TU Delft students from different nationalities and study backgrounds to evaluate the serious game on bystander attitudes, sexual violence myth acceptance, the willingness to intervene, and the effectiveness of the intergroup dialogue. Results. The results showed that the serious game session has a significant positive effect on bystander attitudes (i.e. bystander awareness and responsibility) and the willingness to intervene. No significant difference was found in sexual violence myth acceptance. Additionally, the game proved to promote an effective intergroup dialogue between the participants. Conclusion. Serious gaming proved to be effective in encouraging ethical bystander behaviour by promoting an intergroup dialogue. Therefore, universities are recommended to use serious games as an intervention tool to contribute to cultural change. Future studies should include students from other universities and focus on minimising selection bias to research whether serious games can also influence sexual violence myth acceptance.

Throughout history, complex societal problems have plagued societies with their ever-changing dynamic natures and sheer societal consequences. In efforts to address such complex problems, many actors have turned to participatory methods as a means to incorporate community level knowledge and produce community-oriented solutions while addressing societal concerns. Simultaneously, in the field of serious game design, a large body of research has studied the positive effects of using serious game play for societal intervention. However, fewer efforts have been allocated to exploring the effectiveness of participatory serious game design as a means for societal intervention. In other words, can participatory serious game design act as an effective method for societal intervention? Such inquiry is the basis of this research, where the complex societal problem of biased US policing serves as the application of this study. A mixed methods approach to this research was implemented to explore how to develop interventions where serious game design concepts are to be created between US communities and police. Through a mixed methods approach involving quantitative elements of inferential and descriptive statistical analysis and qualitative elements of content analysis, sentiment analysis, and micro-interlocutor analysis, group brainstorming data, workshop observation data, and semi-structured group interview data was explored in an effort to understand if and how participatory serious game design can be leveraged effectively as a means to societal intervention in the context of US policing and beyond. The outcomes of this research are two-fold. First, with regard to improving police-community relations through societal intervention, results from this research indicate that current relations between US police and BIPOC communities, specifically black communities, are in a dire state of mistrust. As such, any attempt at intervention between these two groups will likely be received with caution and scepticism. However, in demonstrating that intervention efforts are long-term oriented and not just “throw away” events, more willing engagement could be facilitated. In addition, this research has also demonstrated that police accountability in intervention efforts can exhibit to communities that such efforts to improve police-community relations are serious. In maintaining police accountability throughout interventions, communities may be more willing to welcome police efforts in engagement and relation building. Second, in considering participatory game design as an intervention study, results from this study indicate that participatory game design has the potential to be an effective intervention method if it is implemented in a way that appropriately caters to the desired audience. In using participatory game design as a means to societal intervention, participant “buy-in” must be facilitated early on to ensure effective engagement. This study also alludes to the fact that participatory game design could be particularly effective as an intervention method when the topic of intervention is taboo, discomforting, or difficult to talk about, as the game-like nature of such intervention can provide an adequate amount of abstraction from reality that helps facilitate less anxiety-inducing dialogue. In a similar vein, the reality-abstracted game-like nature of participatory game design interventions could also be effective at fostering safe and inclusive spaces where all participants can feel able to engage and contribute to discussion, regardless of their backgrounds. Finally, with regard to using participatory game design as an intervention study, this research has exhibited that in ensuring a diversity of participant backgrounds and perspectives, echo chambers, polarity, and groupthink can be avoided in participatory game design-based interventions. Similarly, more meaningful intervention outcomes and creative problem solving has the potential to arise if a healthy amount of group conflict and pressure is managed appropriately within participatory game design interventions. To the best of our knowledge, this research represents the first mixed methods study aimed at investigating the effectiveness of participative serious game design as a societal intervention method for biased US policing. Therefore, this study provides several potential scientific contributions to the fields of intervention science and serious game design, and it also has several potential implications in the context of society and public policy.

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.

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.

Enterprise Modelling (EM) is the process of producing models, which in turn can be used to support understanding, analysis, (re)design, reasoning, control and learning about various aspects of an enterprise. Various EM techniques and languages exist, and are often supported by computational tools, in particular simulation. The goal of this thesis is to study the effects and advantages of applying constraint programming (CP) to EM. To the best of my knowledge, no previous study has explicitly combined EM and CP. On the topic of applying CP to EM, this thesis explains where it can be applied, as well as its requirements and advantages. Furthermore, it explains a possible approach where a neural network, trained on a simulation model that represents an enterprise model, is embedded into a constraint program. This approach is supported with experiments, that show typical business objectives can be embedded in a constraint program and find solutions to it in a multi-objective context. The main conclusion is that due to CP being a declarative programming technique, business constraints and goals can be effectively modelled into a constraint program, making the approach understandable and intuitive for business analysts to use. This thesis argues alternative approaches to apply CP to EM can also be realised. Some of these, as well as improvements over the proposed method, are also discussed.