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The complexity of monitoring and controlling a large-scale system, such as a communication network, is considered. Relevant literature is reviewed, with emphasis on both behavioral and nonbehavioral approaches to measuring complexity. A simulated large-scale network is described that is used in an experiment to assess the effect of network redundancy and number of systems levels on human fault-diagnosis performance. Experimental data are also used to evaluate two time-varying measures of task complexity (using analysis of variance and time-series analysis). The first measure is dependent on the structure of the system; the second measure is dependent on the strategy of the person controlling the system. Results suggest that this distinction is appropriate. In addition, results emphasize the different implications that complexity can have for normal system operation and human failure diagnosis performance. Although system design characteristics such as redundancy may help to avoid the short-term effects of failures, these same characteristics may have the dual effect of making the human supervisory controller's task more difficult.