Humans often employ cognitive heuristic principles when making decisions. These cognitive heuristic principles allow the human to simplify the decision making task, and can, by their very nature, lead to deviations, referred to as cognitive biases, which influence the quality of the decisions.
While the role of heuristics and biases have been studied in judgmental decision making tasks, very little research on cognitive heuristics and biases has been done on decision making in complex, dynamic tasks. The research undertaken and discussed herein investigates the existence and impact of cognitive biases in time-critical decision making. To do so, this research uses the target identification task undertaken by military image analysts.
This research had three goals. The first goal was to identify the search strategies commonly employed in the object identification task. The second was to identify heuristics and biases that occur during this complex reasoning task. The third goal was to develop a decision support system that improves decision making performance by successfully mitigating the biases that arise during time-critical decision making.
To achieve these goals three experiments were conducted. The first, a preliminary study, was done to verify the potential existence of biases in the object identification task. Once the preliminary study indicated the potential existence of biases, a second study was undertaken to identify which specific biases were present. The information uncovered in the second study was evaluated and based on these results a decision support system was constructed using cognitive engineering principles. This decision support system consisted of three artifacts; an image repository, a message board, and a marking aid. The decision support system was then evaluated in the third study. Additionally, this third study permitted the identification of four specific search strategies commonly employed in the object identification task, including peripheral rings, topographic partitions, systematic scanning, and building blocks.
The results of the empirical study show that the use of the decision support system produces statistically significant improved performance across each of the five measured dimensions; time taken to identify the targets, accuracy of identification of actual targets, accuracy of classifying targets by type, number of false positives, and number of biases expressed.
The results of the research clearly indicate that a decision support system developed using cognitive engineering principles can successfully mitigate the negative impacts of cognitive biases, and improve performance in object identification tasks. While the decision support system developed here produced significant improvements, this research indicates that further gains can likely be made by refining the decision support system through consideration of the specific search strategies that are used to complete the object identification task.