Cognitive Systems Engineering – Designing autonomous and resilient systems2020HT
Cognitive Systems Engineering (CSE) is a discipline within the field of Human
Factors that takes a functional and systemic view on cognition and the use of
artefacts. This view is commonly referred to as Joint Cognitive Systems. This
alludes systems that comprise humans, artefacts and social constructions in
interplay. Such systems are common everywhere; in traffic, at hospitals, in
control rooms, in airplane cockpits, in management of production or economy
just to provide a few examples. In the field of CSE, design, usage of
automation, safety and modelling of joint systems is studied. Such studies
usually take on a macrocognitive perspective on research and what the
appropriate unit of analysis should be. This class focuses especially on the
areas of autonomy and resilience from a cognitive systems perspective.
Autonomy, in this case, refers to technical systems exhibiting goal-driven
behaviour in dynamic contexts. Resilience refers to the ability of a system to
adapt to circumstances and survive when facing disruptions. Students
participating in this class should gain knowledge about the theories and
models, research methods and applications that are appropriate to successfully
manage joint cognitive systems.
The course consists of five different parts: (1) General scientific background – what is CSE? (2) Basic models and theories – how joint systems cope with complexity in the surrounding world (3) Joint Cognitive Systems – the view on artefacts and automation (4) Safety – accident models, causality and resilience (5) applying CSE theory and concepts to own cases focusing on autonomy, resilience, or a mix thereof.
General Scientific background
General scientific background – what is CSE?
A short introduction to the field of Human Factors – why did it emerge? What is/was its focus? Why did CSE emerge? What other approaches resemble CSE?
Man-machine systems have traditionally been described from a structural point of view (from the ”parts” of the system and the interaction between them). An alternative view is to use a functional perspective, where behaviour and performance are more important than internal processes and states. Such a perspective utilizes a holistic stance. From a methodological point of view, this means an emphasis on qualitative studies, case studies, and to describe and understand continuous processes in their natural setting rather than step-wise processes in isolation.
Basic models and theories
SRK-model/Abstraction Hierarchy/Decision ladder/Contextual Control Model/Extended Control Model. Efficiency-Thoroughness-Trade-Off. Performance Variability Management. Recognition-Primed-Decision-Making. Joint Cognitive Systems. Micro-Macro perspective on cognition. The world around us is largely formed by the technologies that humanity has created. However, the constantly increasing level of technical dependencies also creates complexity, which in turn increases the demands on performance. The need to rapidly understand a situation, reason and act upon it must constantly be adjusted to available time, which in turn depends on the stability, or predictability, of the current situation. A crucial part of CSE is the study of how performance constantly is adapted to demands in the situation, and how knowledge about these processes can be used to prevent unwanted consequences. Because of this, it is important to be able to delimit the boundaries of a system since this has direct consequences for how specific functions are designed. The choice of method for task analysis and system descriptions are the foundations for working within the field of CSE.
Joint cognitive systems - view on artefacts
Almost all kinds of work demands the usage of artefacts, which may be both technical and social origin (some would argue that this in inseparable). Artefacts are used to increase the ability to execute and control tasks but may also create dependencies that have unwanted consequences, for example when using automation. The problem of complexity in cognitive systems is clear in certain work-places such as in industrial control rooms or in the transport sector (trains, planes, ships, cars). The view on risk and accidents in connection to these types of systems become central for the ability to handle them.
We commonly assume that the systems we use are ”safe”. Safety is however not a static state but something that emerges from our actions, but also from our view on risk in different situations. How does risk and accident models shape our view on what an accident is? How are we affected by ”folk models”? What is cause and what is effect? We will discuss the Efficiency-Thoroughness-Trade-Off, the theory of the ”bad apple” and the difference between ”safety 1” and ”safety 2”. A comprehensive introduction to Resilience Engineering will be given.
Autonomy and resilience
The increasingly rapid development of autonomous vehicles and other highly automated technologies has created a need for developing new theories and methods for evaluation, verification, and validation. When is an autonomous entity mature enough to be labeled as “safe”? How can we assure that such systems are designed in a way that they are resilient enough to maintain core functionality in a variety of situations and disruptions? This part of the course introduces novel theories and methods for understanding and analysing the design of autonomous entities and assuring a resilient behaviour. Student will apply the novel X10 framework for human-autonomy collaboration as well as the Systemic Resilience model to own cases/designs in a set of mini-projects.
The course consists of introductory lectures and following discussion about the course literature. The participants are expected to read the assigned literature prepare questions (three questions for each paper) that will serve as a basis for discussion before each class. Lastly, the students will apply the X10 and/or the Systemic Resilience model to own cases which will be reported in the form of a short paper and presentation.
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03