20 research outputs found
The future of SSM given Generative AI:the power of ‘purpose’ and its context (or “A Grok 3 enabled story regarding a future for systems thinking and practice”)
Full text linkThis paper presents a recent discussion between the author and Grok 3, a Generative AI programme. The ‘conversation’ begins with the author ‘testing’ the software to discover its ability to describe three related systems concepts: the idea that “all perception is selection” (Youngson, 1996), Vickers’ concept of an “appreciative system” (1965) and Checkland’s Soft Systems Methodology (SSM) (1981). The software produces a surprisingly discerning description of the three concepts and their associated ideas. However, it goes a step further than mere description and evaluation of these ideas by inviting the author to consider a “thought experiment” where it applies SSM to the historical context discussed by Youngson. This unexpected experiment prompts the author to explore the software’s ability to implement SSM-type modelling: the author requests Grok 3 to use SSM’s Root Definitions (RDs) to provide insight into different stakeholders’ views of the current Ukraine-Russia conflict (as of 04/03/2025). The software offers a range of different RDs and their associated CATWOE elements. One RD is selected for refinement and the development of a Conceptual Model (CM) through an iterative process which is documented. The purpose of the exercise presented is to explore the potential future role of SSM and associated systems of inquiry (such as the Appreciative Inquiry Method) given the increasingly powerful AI programmes available. The conclusion is that AI is not only able to offer innovative analysis of existing ideas but also more than capable of undertaking the sophisticated modelling of ‘purpose’ (T) within its wider appreciative context (W) that lies at the heart of SSM. Consequently, we no longer need the skills to produce such models - we merely need to know how to use the models to create appropriate and useful inquiry
Understanding the Whole From the Parts
No full textThis chapter reports on an application of systems theory to a complex area of human endeavor, classical dressage. The area is well represented in a rich literature dating back to the time of Xenophon (c.380BC) and has many practitioners worldwide today. The author uses her interpretive systems perspective to explore classical dressage theory and practice and, throughout, uses examples from the classical equitation literature to support and illustrate the analysis presented. The chapter offers a description of classical dressage from considering its component parts and suggests that it concerns not only the “correct” and ethical training and riding of horses but, fundamentally, the personal development of the trainer/rider. The chapter concludes by (1) inspecting the relationship between classical and competition dressage and suggests that they contain important elements that are mutually exclusive and (2) considering the potential contribution of “systems” to the study of the human-horse relationship as complementary to the increasingly popular approach of equitation science. </jats:p
Towards a subjective knowledge elicitation methodology for the development of expert systems
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Examining human-horse interaction by means of affect recognition via physiological signals
Full text linkFor some time, equine assisted therapy (EAT), i.e. the use of horse-related activities for therapeutic reasons, has been recognised as a useful approach in the treatment of many mental health issues such as post-traumatic stress disorder (PTSD), depression, and anxiety. However, despite the interest in EAT, few scientific studies have focused on understanding the complex emotional response that horses seem to elicit in human riders and handlers. In this work, the potential use of affect recognition techniques based on physiological signals is examined for the task of assessing the interaction between humans and horses in terms of the emotional response of the humans to this interaction. Electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals were captured from humans interacting with horses, and machine learning techniques were applied in order to predict the self-reported emotional states of the human subjects in terms of valence and arousal. Supervised classification experiments demonstrated the potential of this approach for affect recognition during human-horse interaction, reaching an F1-score of 78.27% for valence and 65.49% for arousal
Affect Detection for Human-Horse Interaction
Full text linkIn this work, we aim to study the potential use of affect recognition techniques for examining the interaction between humans and horses using qualitative and quantitative methods. To this end, we propose a multi-modal portable system for physiological signal acquisition such as the electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). The proposed system is used to acquire signals while users are interacting with horses. The captured signals will then be used in order to quantitatively evaluate human and equine interaction by mapping the signals to the emotional state of the subjects using machine learning techniques. In this preliminary study, ECG based features were utilised in order to create a supervised classification model that can identify emotions elicited during human-horse interaction. Experimental results provide evidence about the efficiency of the proposed approach in distinguishing between negative and positive emotions, reaching a classification accuracy of 74.21%
Automated Detection of Substance-Use Status and Related Information from Clinical Text
Full text linkThis study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability
A Systemic Approach to Eliciting and Gathering the Expertise of a ‘Knowledge Guardian’: an Application of the Appreciative Inquiry Method to the Study of Classical Dressage
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