Today, huge volumes of information flow at unprecedented speeds, and large organisations like the Dutch National Police face significant challenges. Efficiently sharing, processing, and prioritizing information is essential but has become increasingly difficult to achieve. These chal- lenges often hinder their ability to make sound and timely decisions. This study investigates innovative methods to enhance collaborative decision-making within such complex environ- ments.
The main goal of this study is to research and test the potential of Artificial Swarming Intelli- gence to enable collective ranking of information based on its importance. The resulting Proof of Concept offers a new method for improving the efficiency of information sharing, enhancing collective intelligence, and facilitating the decision-making processes within the police force.
Drawing inspiration from natural swarming behaviour seen in species like bees, the platform allows participants to rank multiple pieces of information during collaborative sessions. This process helps to highlight the most significant topics, enabling quicker access to critical in- sights.
The Proof of Concept is structured as a digital platform designed to improve real-time decision- making. For our case study, we focused on the policy advisors of the Dutch National Police. The Proof of Concept operates as a client-server model, ensuring that user interactions are efficient while providing live updates on rankings. This allows participants to engage actively and see the evolving importance of various pieces of information.
However, despite the progress achieved, several limitations became evident. First, some envisioned functionalities were not fully developed or implemented within the project’s time frame. In addition, current design faces challenges in scalability, particularly when engaging larger groups of users. Issues related to user accessibility and potential biases in decision- making processes also came into view. To guide further work, we have highlighted key areas for future research and provided specific considerations that address these limitations, aiming to support the Proof of Concept’s development into a complete and fully scalable platform.
Ultimately, this Proof of Concept presents a promising approach to enhancing decision-making processes at the Dutch National Police. It lays the foundation for future research and devel- opment, with opportunities to refine its functionality and expand its use within the organisation. By improving how information is shared and prioritised, the Dutch National Police can enhance its collective intelligence and improve its decision-making processes.

Currently, the Dutch National Police are looking to optimize resource allocation and decision-making through the measurement of the concept: “safety.” However, the measurement of safety does not have a universally defined method in the branch of policing and security. Qualitative data collection methods are being increasingly implemented in police departments around the world to improve safety and security. These data collection methods aim to implement evidence-based policing practices in order to form predictive assessments of future crime. Additionally, qualitative reporting methods such as officer interviews and victimization surveys can complement current quantitative data collection by improving police-community engagement and mitigating
"dark numbers" (unreported crimes).

This research aims to address what the implementation of victimization surveys and officer interviews, in coordination with current data collection methods, can add to an optimized police response and resource allocation to future crimes in theft for the Dutch National Police. The research approach of this thesis takes inspiration from commonalities found in a scoping literature review of policing methods around the world and a former joint interdisciplinary project (JIP) with the Dutch National Police. The perspectives of victims and police, which can be partially measured by surveys and interviews, are considered to significantly affect safety and security within society.

The research method was executed through the use of surveys (historical CBS data and theoretical scenario surveys) and officer interviews. These surveys and officer interviews were designed to determine how significantly certain victim factors, such as amount stolen, income, geography, and past experiences of theft affect a victim's perception on the severity of the theft, reporting threshold, and desired outcome in reporting theft. The method designed was flexible, as the ability to implement surveys to people that measures income and geography was dependent on both the Human Research Ethics Committee (HREC) and the data security concerns of the Dutch National Police.

Based on a victimization survey given to 1547 respondents, these victim factors do correlate with the perceived significance of theft. The magnitude and significance of these correlations are displayed in this report, and comparison with police employee interview results leads to significant insights that may determine why these correlations exist. In brief summary, the victim factors of income, geography, previous victimhood, and the amount stolen in theft generally have positive correlations with a respondent's victimization chance, reporting threshold, desired monetary compensation after theft, general satisfaction in successful reporting outcomes, and the perceived severity of theft.

A concluding recommendation from this research is to consider the further implementation of victimization surveys as a complementary data collection method. Specifically, the correlation of victim factors to the perceived significance of theft can assist in predictive policing through victim profiles that more accurately estimate dark numbers. In addition, the concept of adding qualitative measurement methods on victim factors to a universally-defined equation of safety can serve as a complement to current quantitative crime statistics. Through this implementation, current theft prevention and resource allocation strategies may be improved for the Dutch National Police, leading to a safer society.

Currently, there is not a clear and well-defined metric available that can be used to measure safety and in turn justify resource allocation within the police Department. This metric will help decision-makers understand the situation better prior to taking major decisions. The large amount of data available to the police is not yet used to its full potential when making these critical decisions. This project aims to translate and quantify the qualitative concept of safety by relying on measurable values found in the Netherlands. The creation of this metric will successfully allow the police to compare, over time, how police resource allocation and intervention tactics lead to a safer society in the Netherlands. The proposed final equation is put together, combining Crime-Harm Index, Utility, and Effectiveness factor of the police. Each of these individual components of the equation were studied individually and the final equation has been explained and validated with hypothetical values. This leads to a composite safety factor, which is bounded from 0 to 1. The safety factor can be
later visualised, essentially displaying a hot-spot map that updates frequently. This will help the police in determining the effectiveness of their decisions and measure the impact of their interventions to a certain extent. Moving forward, we believe the Dutch National Police should take such a form of measurement into serious consideration, as this equation explores a more holistic representation of safety in Dutch society through various factors