From the article: Abstract The Information Axiom in axiomatic design states that minimising information is always desirable. Information in design may be considered to be a form of chaos and therefore is unwanted. Chaos leads to a lack of regularities in the design and unregulated issues tend to behave stochastically. Obviously, it is hard to satisfy the FRs of a design when it behaves stochastically. Following a recently presented and somewhat broader categorization of information, it appears to cause the most complication when information moves from the unrecognised to the recognised. The paper investigates how unrecognised information may be found and if it is found, how it can be addressed. Best practices for these investigations are derived from the Cynefin methodology. The Axiomatic Maturity Diagram is applied to address unrecognised information and to investigate how order can be restored. Two cases are applied as examples to explain the vexatious behaviour of unrecognised information.
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Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.
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Currently, promising new tools are under development that will enable crime scene investigators to analyze fingerprints or DNA-traces at the crime scene. While these technologies could help to find a perpetrator early in the investigation, they may also strengthen confirmation bias when an incorrect scenario directs the investigation this early. In this study, 40 experienced Crime scene investigators (CSIs) investigated a mock crime scene to study the influence of rapid identification technologies on the investigation. This initial study shows that receiving identification information during the investigation results in more accurate scenarios. CSIs in general are not as much reconstructing the event that took place, but rather have a “who done it routine.” Their focus is on finding perpetrator traces with the risk of missing important information at the start of the investigation. Furthermore, identification information was mostly integrated in their final scenarios when the results of the analysis matched their expectations. CSIs have the tendency to look for confirmation, but the technology has no influence on this tendency. CSIs should be made aware of the risks of this strategy as important offender information could be missed or innocent people could be wrongfully accused.
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