Heuristics to Improve Human Factors Performance in Aviation

This paper reviews literature related to heuristic cognitive strategies as they are used by flight crews. A review of heuristic and naturalistic cognition is presented. An example set of heuristics and cognitive biases are presented and where possible exemplified by 19 airline accidents. The paper suggests two tentative research designs which could be pursued to quantitatively study heuristics and its impact on aviation decision making. The paper concludes that aviation pilot training would benefit by introducing pilots to the concepts and constructs of heuristic thinking. This paper reviews the literature related to a variety of cognitive strategies under the broad umbrella of heuristics and biases. Where possible, the heuristics are demonstrated against 19 example airline accidents. Following the cognitive introduction, an inventory of heuristics is presented aimed at demonstrating that time limited decision making is defined by heuristic cognitive strategies. A research model is suggested to quantitatively study heuristics in aviation. The paper concludes that aviation pilot training would benefit by introducing pilots to the concepts and constructs of heuristic thinking. Human Factors and Heuristics Background In their book, The Limits of Expertise, Dismukes, Berman, and Loukopoulos analyze 19 airline accidents occurring over a near ten year period. While the authors draw out many themes and proximate causes of the accidents, Table 1 (see Appendix) summarizes the cognitive aspects of each accident. The cognitive aspects often point out faults in human decision making, such as plan continuation bias, the anchoring heuristic and availability heuristic (Dismukes, Berman, & Loukopoulos, 2007). Throughout this paper, reference will be made to Table 1 as examples of cognitive constructs. Heuristic thought processes fall under the broader topic of naturalistic decision making and bounded rationality. The term heuristic is an often misunderstood term. In the popular mathematics book, How to Solve It, P6lya introduces heuristic as, Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible JAAER, Spring 2011 only, whose purpose is to discover the solution of the present problem ... We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building. (P6lya, 1985, p. 113) P6lya is pointing out that many aspects of problem solving may end up in a state of complete certainty, but problems often require thinking which is sufficient for the problem at hand. Heuristic reasoning may therefore either be a means or an end in problem solving: for probabilistic solutions, heuristics can be an end, while for problems requiring certainty, heuristics may be a means to an end. P6lya' s heuristics view supports and complements the views of Nobel Prize economist Herbert A. Simon who posited a view of problem solving known as satisficing. Simon suggests real-world solutions by organisms and economics are often practically defined not by rationality or even probabilistic, recursive optimization, instead by a solution which is sufficient for the problem at hand, Since the organism, like those of the real world, has neither the senses nor the wits to discover an 'optimal path'---even assuming the concept of optimal to be clearly defined-we are concerned only with finding a choice mechanism that will lead it to pursue a 'satisficing' path, a path that will