Crew Resource Management for Automated Teammates (CRM-A)

Crew Resource Management (CRM) is the application of human factors knowledge and skills to ensure that teams make effective use of all resources. This includes ensuring that pilots bring in opinions of other teammates and utilize their unique capabilities. CRM was originally developed 40 years ago in response to a number of airline accidents in which the crew was found to be at fault. The goal was to improve teamwork among airline cockpit crews. The notion of "team" was later expanded to include cabin crew and ground resources. CRM has also been adopted by other industries, most notably medicine. Automation research now finds itself faced with similar issues to those faced by aviation 40 years ago: how to create a more robust system by making full use of both the automation and its human operators. With advances in machine intelligence, processing speed and cheap and plentiful memory, automation has advanced to the point that it can and should be treated as a teammate to fully take advantage of its capabilities and contributions to the system. This area of research is known as Human-Autonomy Teaming (HAT). Research on HAT has identified reusable patterns that can be applied in a wide range of applications. These patterns include features such as bi-directional communication and working agreements. This paper will explore the synergies between CRM and HAT. We believe that HAT research has much to learn from CRM and that there are benefits to expanding CRM to cover automation

TASK HANDOFF BETWEEN HUMANS AND AUTOMATION

The Department of Defense (DOD) seeks to incorporate human-automation teaming to decrease human operators’ cognitive workload, especially in the context of future vertical lift (FVL). Researchers created a “wizard of oz” study to observe human behavior changes as task difficulty and levels of automation increased. The platform used for the study was a firefighting strategy software game called C3Fire. Participants were paired with a confederate acting as an automated agent to observe the participant’s behavior in a human-automation team. The independent variables were automation level (within; low, medium, high) and queuing (between; uncued, cued). The dependent variables were the number of messages transmitted to the confederate, the number of tasks embedded in those messages (tasks handed off), and the participant’s self-reported cognitive workload score. The study results indicated that as the confederate increased its scripted level of automation, the number of tasks handed off to automation increased. However, the number of messages transmitted to automation and the subjective cognitive workload remained the same. The study’s findings suggest that while human operators were able to bundle tasks, cognitive workload remained relatively unchanged. The results imply that the automation level may have less impact on cognitive workload than anticipated.Major, United States ArmyCaptain, United States ArmyCaptain, United States ArmyCaptain, United States ArmyCaptain, United States ArmyApproved for public release. Distribution is unlimited

Aviation Economics: Safety, Monopsony, and Small Sample Inference

This dissertation consists of three essays on aviation. The first chapter investigates the relationship between competition and airline safety. The U.S. airline industry transports almost a billion passengers a year. Accidents still cause much apprehension and angst among the public; the two 737 Max crashes are the latest examples. Despite massive improvements in safety over the last decades which are largely due to technological improvements, there is still a lack of research done on the effect of market conditions. We provide a simple theoretical model to explain the relationship between safety and competition. Using data from 1995 to 2018, we employ a negative binomial regression to evaluate the impact of competition through the Inverse Herfindahl Hirschman Index, Concentration Ratio 4 and 8, on injury outcome of passengers involved in an airline accident. Since accidents are costly both directly and indirectly through brand image deterioration, a competitive market may incentive airlines to reduce risks of accidents. Meanwhile, since safety is unobserved by passengers, under a highly competitive environment where profit margins are thinner, airlines cut costs, some of which are related to safety. Our result suggests that a less competitive industry has a positive impact on safety, suggesting that excess profits are in part reinvested into safety measures. The second chapter tests the hypothesis that monopsony power is an important determinant of wages and employment in the U.S. pilot labor market. We estimate the labor supply curve of the U.S. airline industry using firm-level employment and accident data from 1995 to 2018. Utilizing a labor demand instrument, the prevalence of aircraft accidents, allows us to directly measure monopsony power. We also investigate the effects of competition, as measured by the Inverse Herfindahl-Hirschman Index, on the labor supply elasticity. We estimate a labor supply elasticity of 2.56, indicating that airlines have substantial monopsony power in pilot hiring, resulting in a labor shortage and wages 28.11% below the marginal revenue products. We also find that as market competition rises, airlines slightly lose market power in hiring: wages increase as competition increases. The source of monopsony power lies elsewhere, mostly in the training and career structure, which we address and provide policy recommendations. The third chapter proposes a new method to calculate the p-values of a treatment variable in a cross-sectional small sample. Causal evaluation is becoming increasingly popular in industry and government. In small sample scenarios inference is more difficult. This often occur for several reasons such as budget constraints or noncompliance, but also in phenomena with low frequency. Small samples complicate causal evaluations for at least three reasons: (i) they are associated with greater sampling error, (ii) p-values based on standard tests are not trustworthy and the statistical power of these tests can be too low to detect significant program effects, (iii) the validity of parameter inference strongly depends on distributional assumptions. This paper proposes a simple approximation for the p-values to use in the regression analysis of treatment effects models with normal or nonnormal error distributions. The approximation is derived from recent developments in likelihood analysis and has a third-order distributional accuracy. Thus, for very small or medium-sized samples, the proposed method has remarkably higher accuracy compared to traditional ones that usually rely on normality or large samples. The method is then applied to aviation data to evaluate the impact of accidents on airfares, which is relevant to both airlines and insurance companies

Når tiden er knapp, forholdene er tøffe, men det står liv på spill: En spørreskjemaundersøkelse om emosjonell påvirkning, beslutningstaking og CRM hos luftambulanseflygere

Luftambulansetjenesten HF har i dag kontrakt med Avincis Aviation Services, som opererer luftambulanseflyene i Norge. Denne delen av tjenesten hadde 109.095 oppdrag i tiårsperioden 2013-2022, hvorav 30.0% var haste- eller akuttoppdrag. Luftambulansetjenesten har blitt beskrevet av flere som en samfunnskritisk ressurs, da befolkningen i Norge har rett på lik medisinsk behandling uavhengig av bosted. Beslutningstaking hos flygende personell er et tema som det har blitt forsket mye på tidligere. Det har imidlertid blitt forsket lite på hvordan flygere kan påvirkes av å fly alvorlig syke pasienter. Denne studien er en kvantitativ undersøkelse av hvordan flygerne i Avincis opplever dagene når de mest alvorlige oppdragene kommer, og eksterne faktorer vanskeliggjør operasjonen. Til sammen 52 flygere besvarte undersøkelsen, og spørsmål som ble undersøkt var hvorvidt flygerne blir emosjonelt berørt av pasienter som er alvorlig syke, og hvordan det påvirker besetningen. Undersøkelsen viste at flygerne tar eierskap til det medisinske aspektet av oppdragene, selv om deres primære oppgave er å fly flyet. Mer enn halvparten av flygerne rapporterte å bli emosjonelt påvirket av pasienter med alvorlige medisinske tilstander, og deres ønske om å hjelpe pasientene har i varierende grad innflytelse på hvordan de gjør sine vurderinger i flyet. Studien viste at flygernes beslutningstaking og samarbeid kan bli negativt påvirket som følge av emosjonell påvirkning av oppdragene, men den emosjonelle påvirkningen synes å bli mindre når man har lengre fartstid i tjenesten