Celerity in the courts: The application of fuzzy logic to model case complexity of criminal justice systems

Book Chapter: There are many complex phenomena in the criminal justice system that are difficult to understand because they contain features or concepts that are fuzzy in nature; in other words, it is difficult to assign a crisp value or label to them. Fuzzy Logic is a mathematical concept developed to deal with these very problems. Fuzzy Logic techniques are capable of dealing with approximate facts and partial truths, and not just precise values to model complex issues and processes. While Fuzzy Logic has been used in a number of criminology and criminal justice research efforts, it has not been applied to issues in the criminal court system. Case management is critical to ensure court systems run efficiently and understanding case complexity is an important part of that task. In this chapter we propose Fuzzy Logic as a technique that could be used to model the complexity of cases using general characteristics that are known before cases enter the court system. Using the adult criminal court system in British Columbia as an example, we propose a model that could predict case complexity immediately following the laying of charges by Crown prosecutors. By understanding case complexity a priori, courts may be able to enhance early case consideration procedures such as screening and scheduling to create a more effective and efficient justice system. <p>Book: Research into social systems is challenging due to their complex nature. Traditional methods of analysis are often difficult to apply effectively as theories evolve over time. This can be due to a lack of appropriate data, or too much uncertainty. It can also be the result of problems which are not yet understood well enough in the general sense so that they can be classified, and an appropriate solution quickly identified. Simulation is one tool that deals well with these challenges, fits in well with the deductive process, and is useful for testing theory. This field is still relatively new, and much of the work is necessarily innovative, although it builds upon a rich and varied foundation. There are a number of existing modelling paradigms being applied to complex social systems research. Additionally, new methods and measures are being devised through the process of conducting research. We expect that readers will enjoy the collection of high quality research works from new and accomplished researchers. <p>Book Series "Intelligent Systems Reference Library": The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included. The list of topics spans all the areas of modern intelligent systems such as: Ambient intelligence, Computational intelligence, Social intelligence, Computational neuroscience, Artificial life, Virtual society, Cognitive systems, DNA and immunity-based systems, e-Learning and teaching, Human-centred computing and Machine ethics, Intelligent control, Intelligent data analysis, Knowledge-based paradigms, Knowledge management, Intelligent agents, Intelligent decision making, Intelligent network security, Interactive entertainment, Learning paradigms, Recommender systems, Robotics and Mechatronics including human-machine teaming, Self-organizing and adaptive systems, Soft computing including Neural systems, Fuzzy systems, Evolutionary computing and the Fusion of these paradigms, Perception and Vision, Web intelligence and Multimedia.