When a crime is committed, catching the offender in the act has several benefits. Among others, it provides evidence of the offender's involvement in the crime, which simplifies the conviction process. However, to capture an offender in the act, the collaboration of multiple police officers is required. When a crime is being reported through the emergency line, it is the responsibility of the police officers in the control room to answer the call. They gather information on the type of crime, the location, a description of the offender, etc. Subsequently, they dispatch police units to the crime scene. The first unit prioritizes reaching the crime scene to ensure the safety of citizens, while the other units are tasked with locating and apprehending the offender. To adequately respond to reported crimes, police officers in the control room and units on the street rely on intuition, experience, and habit. This leads to the development of individual-specific strategies for handling interception scenarios. Reducing the dependency on these individualized approaches by identifying proven and robust strategies could increase the likelihood of successfully capturing a suspect. However, how the police currently save data on fugitive interception scenarios does not allow for such identification. Hence, alternative approaches to overcome this limitation must be found. Therefore, this paper explores whether simulation and game theoretic analysis are suitable methods for determining robust interception strategies for the police, aiming to increase the catch rate in fugitive interception scenarios. Classical game theory is the mathematical theory of interactions among rational decision-makers with opposing interests. It offers valuable insights into the decision-making processes, compromises, and strategies the police and offenders may employ in real-world situations. To analyze the fugitive interception scenario with game theory, it is first simulated with an agent-based model. In this simulation model, the police and offender are individual agents with opposing interests and individual decision-making processes. Before modeling, research is conducted to find the current strategies that the police and offenders can potentially adopt. Given the limited availability of data on fugitive interception scenarios, literature and expert interviews serve as sources for data collection. They provide insights into the behavior and strategies of both agents. Both sources emphasize the nature of the crime as a primary indicator of the offender's escape behavior. Large crimes, such as assassinations or armed robberies, are typically well-planned and characterized by predefined escape routes and rational behavior. During their escape, offenders of large crimes are found to be less susceptible to external factors such as crowd flows or police sightings. On the other hand, smaller crimes are more frequently committed spontaneously and associated with bounded rational behavior. This is depicted by their chaotic and unpredictable escape routes while taking many turns. For the game theoretic analysis, the results of the simulation model are analyzed. The fugitive interception project is regarded as a non-cooperative zero-sum game. The results are presented in a payoff table, in which Nash equilibria are calculated. Nash equilibria are the points at which no player can single-handedly improve their outcome when the other player does not change strategy. The pure-strategy Nash equilibrium resulted from the offender strategy where they started at a central metro station and aimed to transfer to a train network. These routes were frequently identified as the shortest compared to other end goals. Conversely, strategies that focused on getting as far away as possible, as quickly as possible, were found to be the least successful. In determining the success of the police strategy, two factors were found to be crucial. Firstly, strategies where the police conducted surveillance on the metro platforms, as opposed to the station exits, proved significantly more effective. This highlights the importance for the police to strategically position themselves where the offender is most likely to pass, irrespective of assuming it to be the offender's final destination. Secondly, the police's response time served as an indicator for capture success. The quicker the crime is reported, the faster the police can take action to capture the offender, which increases capture chances. Additionally to the game theoretic analysis, the relationship between the model’s output and its sensitivity to changes in input variables is tested. Results showed that variations in input did not lead to significant changes in output. This can be attributed to the deep uncertainty of this model. To address this challenge, the model must be refined, and done with more iterations. In conclusion, by combining simulation and game theory new insights can be found beyond what either method can provide individually. By modeling the dynamic nature of a fugitive interception scenario, the success of the offender and police behaviour can be found. This can help the police during decision-making to adopt more robust strategies while considering the dynamic nature of the environment and strategic interactions with the offender. The study addresses the knowledge gap by simulating offender and police behavior, and analyzing the result with classical game theory. This study has created a simulation model with an intuitively driven agent in a complex dynamic problem. Potential improvements in offender capture chances, with findings informing effective and unbiased police interception strategies. The study aims to contribute to crime reduction and foster increased trust in the Dutch national police. However, before generalizing the results future research must be done to overcome limitations resulting from the simplifications of this simulation model.