Quantitative Modeling of Human Performance in Complex, Dynamic Systems (1990) / Chapter Skim
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1. Introduction
1. Introduction
Pages 1-15
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From page 1... ...
The primary focus is modeling system operators performing supervisory and manual control tasks. The report does not address models of the designer or manager of a complex system, and it addresses models of maintainers only briefly (see Unkind, Card, Hochberg, and Huey, 1989, for a discussion of models pertinent to designers and managers)
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In addition to models that are appropriate for single tasks or activities, it is necessary to model the ways in which human operators manage their own resources so as to cope with the changing and sometimes conflicting demands of disparate activities. A major question that arises is: Can this be accomplished by integrating single-task models that have been developed previously for the activities performed in isolation, or is it necessary or better to model the complex task in a completely unified manner?
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versus descriptive, top-down versus bottom-up, and single-task versus multitask Models can also be characterized according to the types of theories or tools used in their development. Output Versus Process The dimension of output versus process relates to the degree to which a model (or modeling approach)
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This is particularly true when prescriptive models include in their formulation, representations of human limitations that constrain performance. Top-Down Versus Bottom-Up The top-down/bottom-up distinction refers to the extent that a model is dictated by system goals or by human performance capabilities.
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For example, discrete event modeling of the system will tend to lead to task network models for the operator, whereas continuous time system models would involve corresponding representations of the humans. 1 "System, " in the report, refers to an interconnected set of parts making up a whole entity that has a common purpose.
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Theory Development and Evaluation ~ develop a model, one must be specific about one's theories of human performance. If a working model has been developed, the model may be exercised to detains if the simulated behavior of the modeled constituents corresponds to the behavior of those same constituents in the real world under similar conditions.
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Then, · if predicted performance does not satisfy the goals, redefine the goals or rethink the method and try again or · if the predictions and goals seem to match fairly well, simulate the configuration, test it with human subjects, and, based on the results, proceed with development, make additional adjustments to the goals, or modify the model as dictated by the experimental data. This iterative procedure helps to extract those system characteristics that are essential to meeting predefined system performance goals and are, at the same the, responsive to human performance capacities and limitations.
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Moreover, human performance modeling will, for the foreseeable future, require experimental verification in simulators (just as simulator results often require real-world verification)
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Laboratory experiments can be a relatively inexpensive way to make early decisions when they must be made. They also can be used to test or develop component models for single tasks that are used in constructing more comprehensive models.
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Of interest are the antecedents, and possible components, of the approaches to modeling described in this report Figure 1-1 summarizes this history diagrammatically by highlighting four main approaches to human performance modeling: information processing approaches, control theory approaches, task network approaches (network and reliability modeling) , and knowledge-based approaches.
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Control Theory Models Interest in manual control models was first stimulated by the need to understand how humans control antiaircraft guns and other closedloop systems. The seminal paper on this subject was by Justin (1947)
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and for exploring the question of what is learned as one acquires tracking skill (Levison, 1979~. The OCM has been applied widely, and the ~nformation-processing portion of the model has been extended to tasks other than manual control lithe introduction of automation in aircraft cockpits and the vast increase in complete of the avionics resulting from it have forced considerat~on of manual aircraft control in the larger context of aircraft systems management.
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This language has been used to study performance in a wide range of systems including digital avionics systems, command and control networks, and a hot strip mill; SLAM II represents the current state of the art with respect to task network simulation languages and modeling tools. Knowledge-Based Models About the same time that component models of information processing were being developed, Newell, Shaw, and Simon (1958)
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Many people believe that human-machine system modeling is the wave of the future, especially for situations in which the modeling effort views a person as a planner rather than a sensor or movement controller.
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