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.

This paper investigates the viability of using scope graphs to implement type checkers for programming languages, specifically for a Scala subset. The primary objective is to determine if scope graphs can offer a declarative and extensible approach to type checking. To achieve this, we used a phased Haskell library to implement such a type checker. The declarativity and feature extensibility of the approach were evaluated by means of comparation with Rouvoet et al.'s approach in mini-Statix. The results demonstrate that using scope graphs as a basis for type checking yields a modular and extensible solution compared to traditional methods. However, it is noted that this approach may sacrifice a certain degree of declarativity. These findings suggest that scope graphs are a promising tool for type checking, particularly in the context of name binding. Further research is recommended to explore the possibility of implementing similar type checkers for other programming languages. Additionally, the paper suggests incorporating additional features into the targeted Scala subset, thereby enhancing its extensibility.