Alexandre Marois, D. Lafond, Amandine Audouy, Hugo Boronat, P. Mazoyer
{"title":"Policy Capturing to Support Pilot Decision-Making","authors":"Alexandre Marois, D. Lafond, Amandine Audouy, Hugo Boronat, P. Mazoyer","doi":"10.1027/2192-0923/a000237","DOIUrl":null,"url":null,"abstract":"Abstract. Single-pilot operations are cognitively challenging for pilots and could benefit from decision-support tools to mitigate risk-prone situations. The Cognitive Shadow is a prototype tool that employs policy capturing, a data-driven technique used to model decisions, to learn users’ judgement policies and alert decision discrepancies from one’s decision pattern. This proof-of-concept study investigates the potential of policy capturing to model pilots’ policies facing unstable approaches. Pilots were presented simulated cases and asked whether to continue descent or to go-around while the policy-capturing tool learned their decision pattern and provided feedback. Individual models reached mean predictive accuracy of ~ 89% while the group model reached 100%. These results speak to the potential of extracting pilots’ knowledge using policy capturing to create decision aids.","PeriodicalId":121896,"journal":{"name":"Aviation Psychology and Applied Human Factors","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aviation Psychology and Applied Human Factors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1027/2192-0923/a000237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Abstract. Single-pilot operations are cognitively challenging for pilots and could benefit from decision-support tools to mitigate risk-prone situations. The Cognitive Shadow is a prototype tool that employs policy capturing, a data-driven technique used to model decisions, to learn users’ judgement policies and alert decision discrepancies from one’s decision pattern. This proof-of-concept study investigates the potential of policy capturing to model pilots’ policies facing unstable approaches. Pilots were presented simulated cases and asked whether to continue descent or to go-around while the policy-capturing tool learned their decision pattern and provided feedback. Individual models reached mean predictive accuracy of ~ 89% while the group model reached 100%. These results speak to the potential of extracting pilots’ knowledge using policy capturing to create decision aids.