decision ladder
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Author(s):  
Christopher M. Lilburne ◽  
Maureen E. Hassall

The Decision Ladder (DL) Template is often used to understand workers’ reasoning processes. In high-hazard industries such as oil refining, frontline workers’ decisions are crucial to managing abnormal situations and preventing unwanted events. This paper describes research undertaken–using the DL Template–to analyze workers’ responses to abnormal situations as elicited during Critical Decision Method interviews. The paper firstly describes the evolution DL Template since Rasmussen’s original writings. It then describes challenges encountered when using the traditional DL Template to analyze interview responses. A modified DL Template is proposed that provides a more detailed top portion of the template to allow more explicit analysis of how knowledge-based reasoning might be used when addressing abnormal, safety-critical situations.


Eco-Driving ◽  
2017 ◽  
pp. 125-152
Author(s):  
Rich C. McIlroy ◽  
Neville A. Stanton
Keyword(s):  

2016 ◽  
Vol 56 ◽  
pp. 1-10 ◽  
Author(s):  
Christine M. Mulvihill ◽  
Paul M. Salmon ◽  
Vanessa Beanland ◽  
Michael G. Lenné ◽  
Gemma J.M. Read ◽  
...  

Author(s):  
Yeti Li ◽  
Catherine Burns ◽  
Rui Hu

We propose that representing stages and levels of automation on a decision ladder (DL) could help to identify information requirements for designing automation interfaces. We look at automated financial trading systems, a domain with variable degrees of automation (DOA). We give examples of modelling a financial trading task for two DOAs: basket trading (a low DOA) and trend following trading (a high DOA). On the resulting DLs, both human and automated information-processing activities are presented. The steps and states of knowledge allocated to automation are first categorized by the commonly known four stages of automation, and then shaded to represent the level of automation in each stage. This work advances the understanding of automated trading, and automation in general, and may provide a deeper representation of human-automation interactions and thus better understanding of design requirements.


Author(s):  
Robert R. Hoffman ◽  
Michael J McCloskey
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