Professors Erik Hollnagel and David Woods (HOLLNAGEL e WOODS, 2005) start their book “Joint Cognitive Systems: foundations of cognitive systems engineering”
by listing what they call “driving forces” – forces that originated the need for an approach
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to systems engineering based on cognitive aspects of work. These forces, according to Hollnagel and Woods are:
The growing complexity of socio-technical systems: due to the constant growth of computerisation or applied information technology, computers have become the dominating medium for work, communication, and interaction, transforming work performance and creating new fields of activity;
Problems and failures created by clumsy use of the emerging technologies:
rapid changes in work performance worsened the conditions for practitioners who already had insufficient time to adjust to the existing imposed complexity. The major consequence of this scenario is a succession of real world failures of complex systems that made human factors, human actions, and, in particular, human error, more noticeable;
Limitations of linear models and information processing paradigm:
engineering and computer science communities subtly adopted the notion that humans are information-processing systems, fragmenting the view of human-machine interaction.
Still according to Hollnagel and Woods, one must distinguish technological system from organizations. In technological systems, technology plays a central role in determining what happens; while in organizations humans play the central role in determining what happens. Thus, Hollnagel and Woods propose an approach to cognitive systems engineering that considers organizations as artefacts of a social nature made for a specific purpose.
Hoffman and Woods (HOFFMAN e WOODS, 2000) introduce the concept of
“complex cognitive systems”, i.e. work environments in which the knowledge and reasoning of individuals play an important role, but so do the cognition and reasoning of larger groups of people, including teams and even entire organizations. In addition, these complex cognitive systems often involve people interacting with computers and interacting with each other via computers in intricate networks of humans and technology. If one wants to support - or improve – the complex work performed in these systems observing their
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actions is not enough. One must understand what they are thinking while performing their activities.
Professors Beth Crandall, Gary Klein, and Robert Hoffman propose a set of methods for studying thinking and reasoning in the performance of work in complex systems. Their cognitive task analysis approach provides procedures for understanding work in complex work settings. Their approach supports the systematic identification of key cognitive issues in people’s work, useful in the development of tools and technologies, as well as work processes (CRANDALL, KLEIN e HOFFMAN, 2006).
Crandall, Klein, & Hoffman’s approach is based on three primary aspects:
knowledge elicitation, data analysis, and knowledge representation. Knowledge elicitation comprises a set of methods used to obtain information about what people know and how they know it; data analysis consists in structuring data, identifying findings, and discovering meaning; knowledge representation includes tasks of displaying data, presenting findings, and communicating meaning and discoveries.
Earlier, Rasmussen also stated that every system, regardless how automated it is, rely on human intervention in some level (RASMUSSEN, 1979). Even though they do not depend of human interaction while in normal functioning, their existence depends on extensive support by a human staff to maintain the necessary conditions for satisfactory operation, especially if their operation involves high possibility of unforeseen conditions.
Rasmussen suggests that in highly automated sociotechnical systems, as humans supposed to act goal-oriented, technology experts tend to model human activity with focus on the discrepancy between what is intended and what is actually achieved. However, human activity in a familiar environment will not be goal-oriented, but oriented towards the goal and controlled by rules previously proven successful. In unfamiliar situations, behaviour may be goal-oriented in the sense workers make different attempts to reach the goal and, then, select a successful sequence.
Thus, Rasmussen proposes a set of categories of models of human activity to stratify the span between the physical reality and human purposes, i.e., the reason for the
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physical systems in which people work. The author defines the following structure for models of human activity:
Models of physical form: represent the spatial distribution of matter in the environment, like a portrait of the physical landscape. It is objective, i.e., independent of the intentions of the modeller, although is it dependent upon the intended use of the environment;
Models of physical function: represent the physical structure of the system and its functional properties, e.g. technical components, and their properties.
Physical objects are limited by boundaries that can be rearranged according the level of aggregation or decomposition into objects;
Models of functional structure: the main element of these models is a set of relations among variables across boundaries of physical parts, or
“functions”. Such functions represent standardized, generic elements of system purposes;
Models of abstract function: represent the overall function of a system in a generalized causal network, moving in abstraction level independently of the local physical or functional properties;
Models of functional purpose: represent the observable constraints within the relationship among the variables of the system. These models describe the properties of a system in terms of relations between variables or states and events in the environment.
The taxonomy of models of human activity proposed by Rasmussen appears in Vicente’s (1999) work as a framework for work analysis called Cognitive Work Analysis (CTA). Vicente proposes an integrated framework based on behavior-shaping constraints of the work environment and contains models of the work domain, control tasks, strategies, social-organizational factors, and worker competencies. According to Vicente, the constraints of the work environment are limits between the possibilities for behaviour of workers.
The CWA approach is ecological, i.e. it is centered on the analysis of the constraints that the environment imposes on action. Thus, it gives designers the possibility of
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developing interfaces compatible with such environment constraints. The objective of CWA is to ensure that workers will acquire mental model of the environment that represents, as accurately as possible, the actual behaviour of the context in which workers are involved.
The CWA framework comprises five phases as follows:
Work Domain Analysis: the purpose of this phase is to identify a set of constraints on the actions of workers and provide a description of the domain in which work is performed. The abstraction hierarchy (RASMUSSEN, 1979) is the main modeling tool for this phase (see Figure 3-1).
Control Task Analysis: the objective of this phase is to identify the requirements associated with recurring classes of situations, and the constraints on work performance, no matter who performs the activities or how they are carried out. We use the decision ladder (RASMUSSEN, 1979) as the tool for writing control task models.
Strategies Analysis: this phase aims at understanding the different ways of accomplishing the activities identified in a control task analysis. Therefore, its models must describe how work is done rather then what is done.
Information flow maps (RASMUSSEN, 1979; RASMUSSEN, 1980) is the modeling tool suggested by Vicente in order to perform this.
Social Organization and Cooperation Analysis: this phase addresses how work requirements are distributed among human workers and automation, and how such actors communicate and cooperate. Modeling tools used in the previous phases are revisited in the social organization and cooperation analysis in order to represent how the social and technical factors in a sociotechnical system can enhance the performance of the system.
Worker Competencies Analysis: the fifth and final phase of CWA focuses on the identification of the competencies that workers in the analyzed domain must have. This is performed by letting requirements of the application domain determine what kinds of competencies workers need, in
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order to accomplish their goals. The modeling tool used to conduct worker competencies analysis is the skills, rules, and knowledge taxonomy (RASMUSSEN, 1983).
Figure 3-1 shows the elements of an abstraction hierarchy model. The structure of the abstraction hierarchy represents means-end relationship between the elements of its five levels, which increases the understanding of the system. By moving up the hierarchy, we focus on the purposes; by moving down the hierarchy, we focus on how those purposes can be carried out. Higher levels are less detailed than lower levels. Shifting from a low to a higher level of abstraction can make complex domains look simpler.
Figure 3-1: An example of abstraction hierarchy
Figure 3-2 presents the decision ladder as proposed by Rasmussen (1979). Used as the main modeling tool in control task analysis, the decision ladder represents the relationships between information-processing activities and states of knowledge.
Information-processing activities are the expert routines in which actors need to engage to accomplish task goals. Furthermore, states of knowledge are the results of information- processing activities, e.g. the products of information-processing activities.
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Figure 3-2: An example of Rasmussen’s decision ladder
Relationships between information-processing activities and states of knowledge can be of two kinds: shunts or leaps. Shunts are the followed by experts, therefore connect an information-processing activity to a state of knowledge. Leaps connect two states of knowledge directly, without any information-processing activity in between them.
Vicente uses information flow maps (see) to describe the categories of cognitive task procedures that constitute workers’ strategies. Information flow maps illustrate the sequence followed by a particular worker during a specific troubleshooting episode.
According to Vicente, action sequence instances are variable, but treating strategies are idealized categories that can be instantiated during particular situations, providing ways of coping with complexity
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Figure 3-3: An example of information flow map
Vicente recommends the use of the skills, rules, knowledge (SRK) taxonomy (RASMUSSEN, 1983) in the final phase of CWA to organize knowledge into a form that is more useful for systems design. Its structure is a three-level taxonomy, since each level of cognitive control is based on a different type of human performance.
Figure 3-4: Rasmussen’s (1983) SRK taxonomy
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Figure 3-4 shows the structure of an SRK taxonomy. It comprises three kinds of behavior of workers: knowledge-based behavior, i.e. analytical reasoning based on a symbolic representation of environment constraints; skill-based behavior, i.e. automated and highly integrated actions performed by workers without conscious attention; and rule- based behavior, i.e. previously stablished rules and procedures, experience, instruction, or problem-solving activities.
These models, used along the phases of CWA, should provide designers better insight about workers cognition while performing activities. Due to the ecological orientation, CWA focuses on both the environment and human cognition. Thus, by describing the related constraints it enables the design of more suitable support technology for workers on complex sociotechnical systems.