Visualisation of topological structures as a training tool for acquiring causal models of complex systems

Klostermann, Anne; Thüring, Manfred

doi:10.1145/1362550.1362618

Cited by 2 publications

(3 citation statements)

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“…If the vehicle breaks down, the driver must diagnose the cause of the failure (casual knowledge) (Klostermann & Thüring, 2007), and judge their capacity to control it (sense of agency) (Degani, 2004). However, these issues do not only arise during vehicle break down, but also as a result of the continued development of automated functions (e.g., Electric Power Assisted Steering (EPAS), Semi-Automatic Parking (SAP), Adaptive…”

Section: Agency and Causalitymentioning

confidence: 99%

“…A real-world example in which causal knowledge and one's sense of agency have consequences in controlling a CDC system is cars. If the vehicle breaks down, the driver must diagnose the cause of the failure (casual knowledge; Klostermann & Thu ¨ring, 2007), and judge his or her capacity to control it (sense of agency; A. Degani, 2004).…”

Section: Agency and Causalitymentioning

confidence: 99%

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Controlling uncertainty: A review of human behavior in complex dynamic environments.

Osman

2010

Psychological Bulletin

189

160

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Complex dynamic control tasks (CDC tasks) are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (Economics, Engineering, Ergonomics, Human Computer Interaction (HCI), Management, Psychology), which remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (Monitoring and Control framework) as a tool for integrating theory and findings. The main thesis of the Monitoring and Control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks. Controlling Uncertainty 3Key words: Monitoring, Control, Uncertainty, Dynamic, Complex tasks, Causality, Agency Controlling Uncertainty 4Controlling Uncertainty: A Review of Human Behavior in Complex Dynamic Environments Problem solving is regarded by many (Anderson & Lebiere, 1998;Laird, Newell, & Rosenbloom, 1987;Newell & Simon, 1972;Sweller, 2003) as the most important cognitive activity in everyday and professional contexts, especially problem solving of the kind needed to control complex dynamic environments. As the complexity of the systems that we interact with in our daily lives grows (e.g., phones, computers, automated driving systems) so too does the importance of research on how we learn to control dynamic environments. Before detailing the issues surrounding the various research domains that investigate control behaviors, the illustrations below provide a general flavor of the kind of real world and laboratory tasks in which control behaviors have been studied. Illustration 1: Ecosystem Control System. In a laboratory-based task, people are presented with a simulated environment of an African landscape with different flora and fauna. The goal is to manage the environment in order to improve the living conditions of the native population.This involves controlling the system over several weeks by managing many interconnected variables that have a complex feedback process, both negative and positive, and with time delays.Illustration 2: Automated Pilot System. Trained pilots are required to take part in flight scenarios (including take-off and landing) in a flight simulator, in which they control the system while responding to a variety problems, including delays between their commands and their actual execution in the system, failings in the system, and miscued problems from the control panel. Controlling Uncertainty 5Illustration 3: Solid Incineration Plant. A municipal solid waste incineration plant is a largescale industrial syste...

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Section: Agency and Causalitymentioning

confidence: 99%

Section: Agency and Causalitymentioning

confidence: 99%

Controlling uncertainty: A review of human behavior in complex dynamic environments.

Osman

2010

Psychological Bulletin

189

160

View full text Add to dashboard Cite

show abstract

“…A real world example in which causal knowledge and our sense of agency have consequences in controlling a CDC system is cars. If the vehicle breaks down, the driver must diagnose the cause of the failure (casual knowledge) (Klostermann & Thüring, 2007), and judge their capacity to control it (sense of agency) (Degani, 2004). However, these issues do not only arise during vehicle break down, but also as a result of the continued development of automated functions (e.g., Electric Power Assisted Steering (EPAS), Semi-Automatic Parking (SAP), Adaptive Cruise Control (ACC), Lane Departure Warning (LDW)).…”

Section: Reciprocal Relationship Between Agency and Causalitymentioning

confidence: 99%

Controlling Uncertainty

Osman¹

2010

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Visualisation of topological structures as a training tool for acquiring causal models of complex systems

Cited by 2 publications

References 1 publication

Controlling uncertainty: A review of human behavior in complex dynamic environments.

Controlling uncertainty: A review of human behavior in complex dynamic environments.

Controlling Uncertainty

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