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Coping with Complexity: The psychology of human behavior in complex systems

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... Whenever this is not possible, faults must be diagnosed by humans who can flexibly apply their previous experience and system knowledge even to novel faults. As domain characteristics influence cognition and behavior (e.g., [1][2][3]), strategies and requirements for successful fault diagnosis are thought to differ between domains. This is illustrated by the following two scenarios. ...
... The data presented here suggest that the more support given, the less diagnosticians are required to possess functional (E4) or empirical knowledge (E3). For situations in which no or only little support is available, other authors especially highlight the need for experts to develop their own strategies [3,42], meaning that they have to decide which information is relevant and how it can be obtained. Thus, diagnosticians need functional and empirical knowledge to acquire relevant data and to evaluate their hypotheses [16]. ...
... As the current form of the model presents a collection of relevant domain differences as a starting point, we expect more factors to be added in the future. Among the most promising influencing factors are the simultaneous occurrence of multiple faults and the extent to which symptoms can be masked [14,50], the extent to which faults occur intermittently [14,51], or the consequences of faults for the overall system [3,41]. The extent to which these characteristics occur in different work domains and how this shapes the diagnostic process remains to be studied further. ...
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In complex work domains, not all possible faults can be anticipated by designers or handled by automation. Humans therefore play an important role in fault diagnosis. To support their diagnostic reasoning, it is necessary to understand the requirements that diagnosticians face. While much research has dealt with identifying domain-general aspects of fault diagnosis, the present exploratory study examined domain-specific influences on the requirements for diagnosticians. Scenario-based interviews were conducted with nine experts from two domains: the car domain and the packaging machine domain. The interviews revealed several factors that influence the requirements for successful fault diagnosis. These factors were summarized in five categories, namely domain background, technical system, typical faults, diagnostic process, and requirements. Based on these factors, we developed the Domain Requirements Model to predict requirements for diagnosticians (e.g., the need for empirical knowledge) from domain characteristics (e.g., the degree to which changes in inputs are available as domain knowledge) or characteristics of the diagnostic process (e.g., the extent of support). The model is discussed considering the psychological literature on fault diagnosis, and first insights are provided that show how the model can be used to predict requirements of diagnostic reasoning beyond the two domains studied here.
... There are various existing definitions for task complexity [41,42,43,44]. The definitions often aim to describe how much cognitive processing capabilities, skills, information, and knowledge availability are necessary to perform a task [45,41]. ...
... There are various existing definitions for task complexity [41,42,43,44]. The definitions often aim to describe how much cognitive processing capabilities, skills, information, and knowledge availability are necessary to perform a task [45,41]. Furthermore, objective task complexity can comprise of number of task components, goals, or possible solution paths [46,47]. ...
Conference Paper
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Human-AI teams have the potential to produce improved outcomes in various tasks as opposed to each team member working alone. However, there are various factors that influence human-AI team performance which potentially differ from classical Human-Computer Interaction settings. Specifically, there is existing work indicating that it is beneficial for AI systems to automatically adapt their autonomy within the team and task in order to work towards achieving a shared goal more effectively. Thus, in this paper, we describe a concept of situational adaptive autonomy for human-AI cooperation in a shared workspace setting. We discuss that task complexity and models for the AI system's understanding of their human teammate, i.e. Theory of Mind models (ToMMs) might be helpful to implement situational adaptation of AI autonomy such that interaction and team performance can be improved. We present an experimental setup for a cooperative real-world robotic task and a corresponding approximation in a grid-world in which we plan to investigate situational adaptive autonomy within a shared workspace in an interactive human-AI team.
... "Complexity" as a concept is used to characterize a wide range of topics and phenomena in a large number of knowledge domains, including physics [13], biology [89], psychology [136], sociology [26], economics [7], management [103], technology [70], and in cross-domain discourses such as systems and complexity science [45,107]. In common parlance, the adjective "complex" is closely tied to-and often mostly synonymous with-"complicated" or "intricate" (although see Norman [100] for a theoretical distinction between "complex" as a property of user interpretation and "complicated" as a property of the exterior world). ...
Preprint
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Complexity is often seen as a inherent negative in information design, with the job of the designer being to reduce or eliminate complexity, and with principles like Tufte's "data-ink ratio" or "chartjunk" to operationalize minimalism and simplicity in visualizations. However, in this position paper, we call for a more expansive view of complexity as a design material, like color or texture or shape: an element of information design that can be used in many ways, many of which are beneficial to the goals of using data to understand the world around us. We describe complexity as a phenomenon that occurs not just in visual design but in every aspect of the sensemaking process, from data collection to interpretation. For each of these stages, we present examples of ways that these various forms of complexity can be used (or abused) in visualization design. We ultimately call on the visualization community to build a more nuanced view of complexity, to look for places to usefully integrate complexity in multiple stages of the design process, and, even when the goal is to reduce complexity, to look for the non-visual forms of complexity that may have otherwise been overlooked.
... As operações são realizadas sob forte pressão de tempo decorrente tanto da necessidade de satisfazer os consumidores, como de manter a segurança das pessoas envolvidas. Este contexto condiz com os sistemas complexos, que se caracterizam por situações que evoluem de modo indeterminado ao longo do tempo, incerteza das informações sobre o sistema, quantidade de componentes altamente interconectados e elevado risco com custos associados às consequências das decisões tomadas (WOODS, 1998). A complexidade impõe exigências excessivas para execução das tarefas, exercendo pressão sobre o operador (LIMA et al., 2015). ...
... As operações são realizadas sob forte pressão de tempo decorrente tanto da necessidade de satisfazer os consumidores, como de manter a segurança das pessoas envolvidas. Este contexto condiz com os sistemas complexos, que se caracterizam por situações que evoluem de modo indeterminado ao longo do tempo, incerteza das informações sobre o sistema, quantidade de componentes altamente interconectados e elevado risco com custos associados às consequências das decisões tomadas (WOODS, 1998). A complexidade impõe exigências excessivas para execução das tarefas, exercendo pressão sobre o operador (LIMA et al., 2015). ...
... As operações são realizadas sob forte pressão de tempo decorrente tanto da necessidade de satisfazer os consumidores, como de manter a segurança das pessoas envolvidas. Este contexto condiz com os sistemas complexos, que se caracterizam por situações que evoluem de modo indeterminado ao longo do tempo, incerteza das informações sobre o sistema, quantidade de componentes altamente interconectados e elevado risco com custos associados às consequências das decisões tomadas (WOODS, 1998). A complexidade impõe exigências excessivas para execução das tarefas, exercendo pressão sobre o operador (LIMA et al., 2015). ...
... The control room team plays a vital role in maintaining production, preventing abnormalities, and mitigating potential accidents. High workload is a central topic given the operators' task of interpreting a substantial amount of complex information and, if required, performing critical time-pressured decisions (Woods 1988;Vicente 1999;Ha et al. 2006;Kim, Kim, and Jung 2014;Chen, Yan, and Tran 2019). However, despite the available guidance (O'Hara et al. 2012;Reinerman-Jones et al. 2015;ISO 2016) and substantial research over the last decades (Moray 1988;Young et al. 2015;Charles and Nixon 2019), practitioners of human factors engineering report challenges in selecting cognitive workload measures and interpreting the results of these measures (Pickup, Wilson, and Lowe 2010;Young et al. 2015;OECD NEA 2017;Braarud and Pignoni 2022). ...
Article
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Despite the substantial literature and human factors guidance, evaluators report challenges in selecting cognitive workload measures for the evaluation of complex human–technology systems. A review of 32 articles found that self-report measures and secondary tasks were systematically sensitive to human–system interface conditions and correlated with physiological measures. Therefore, including a self-report measure of cognitive workload is recommended when evaluating human–system interfaces. Physiological measures were mainly used in method studies, and future research must demonstrate the utility of these measures for human–system evaluation in complex work settings. However, indexes of physiological measures showed promise for cognitive workload assessment. The review revealed a limited focus on the measurement of excessive cognitive workload, although this is a key topic in nuclear process control. To support human–system evaluation of adequate cognitive workload, future research on behavioural measures may be useful in the identification and analysis of underload and overload.
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