Supporting Control Room Operators in Highly Automated Future Power Networks

Operating power systems is an extremely challenging task, not least because power systems have become highly interconnected, as well as the range of network issues that can occur. It is therefore a necessity to develop decision support systems and visualisation that can effectively support the human operators for decision-making in the complex and dynamic environment of future highly automated power system. This paper aims to investigate the decision support functions associated with frequency deviation events for the proposed Web of Cells concept

Metacognition, Numeracy, and Automation-aided Decision-making

Automated decision aids can improve human decision-making but the benefits are often compromised by inefficient use. The current experiment examined whether metacognition—the ability to assess self-performance—and numeracy—the ability to understand and work with numbers—predict the efficiency of automation use in a signal detection task. Two-hundred twenty-one participants classified random dot images as blue or orange dominant, receiving assistance from an 84% reliable decision aid on some trials. Type 1 and metacognitive signal detection measures were estimated from participants’ confidence ratings, and numeracy was measured using a subjective scale. The inefficiency of automation use was assessed by measuring the deviation from optimal bias following cues from the aid (bias error). Data gave strong evidence that metacognition was not associated with bias error, and anecdotal evidence that numeracy and suboptimality were weakly negatively correlated. These results suggest that operators used a strategy of combining the aid’s judgments with their own that is not metacognitively driven, but may depend on numeracy

Not Good Enough: Ironic Efficiency in Automated-Aided Signal-Detection

During applied signal-detection (e.g., airport-baggage screening) human operators can be assisted in their decision-making process by automated devices. Automation implementation is aimed at increasing performance relative to unaided levels. Generally, this intended effect is empirically observed. However, operators consistently fall short of optimal levels of aided performance, indicating suboptimal aid-use efficiency. Previous research suggests aid-use efficiency might vary depending on the sensitivity levels of each agent in the human + automation team. In the present research we manipulated Task Difficulty (easy vs. difficult) and Aid Reliability (low vs high) to examine how measures of sensitivity and aid-use efficiency vary across these factors. Participants completed a numerical signal-detection task with automated-support manipulated within-subjects. Bayesian inference analyses suggested higher sensitivity gains were achieved at higher levels of difficulty and aid reliability. Interestingly, however, aid-use efficiency was lower at these conditions. These findings replicate and expand previously observed ironic patterns of aided performance where operators fall shorter of optimal levels in conditions where empirical and potential levels of aid-benefit are higher. These findings provide valuable insight for system designers and highlight the need to better understand factors contributing to suboptimal human-automation interaction during aided signal-detection to procure safety and efficiency in naturalistic settings