The present study is an initial investigation of complex decision making performance. Specifically, thiswork investigated how individuals make decisions involving risk and uncertainty within a spatially focusedroute planning task involving multiple simulated unmanned vehicles and objectives. Forty-threeparticipants were instructed to create twenty-four route plans, half of which included a combination of riskyroute suggestions and enhanced icons. Given the high working memory demands of developing a route formultiple vehicles, it was expected that enhanced visualizations, which provided redundant information ontarget priority, uncertainty, and deadline, would reduce demands on working memory and improveperformance. Additionally, it was predicted that providing risky route suggestions would negatively impactperformance, yet enhanced visualizations would reduce the effect of risky route suggestions. However,findings supported neither hypothesis; there was no performance difference in providing enhancedvisualizations, and risky sub-optimal route suggestions actually improved the expected value of thesubmitted route. These results could have occurred due to multiple factors, including the multi-objectiveroute planning task being too complex, or the scenarios lacking sufficient variability in expected value. Anadditional interpretation of these findings is that humans are really poor at performing these complex multiobjectivetasks, and failed to comprehend the uncertainty and risk inherent in their decisions.
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