Kodama, DaikiTakayama, ChihiroShimizu, Shinya2024-08-282024-08-282024-08-28Kodama D, Takayama C, Shimizu S. Study on predicting collaborative performance: from personality traits vs. behavior in a different task. Poster presented at: The 30th International Conference on Collaboration Technologies and Social Computing (Collabtech); 2024 Sep 11-14; Barcelona, Spain.http://hdl.handle.net/10230/60940Pòster presentat a: The 30th International Conference on Collaboration Technologies and Social Computing (Collabtech), del 11 al 14 de setembre a Barcelona, Espanya.Most social activities are conducted with multiple entities to improve efficiency. It is difficult to estimate post-learning performance in advance when multiple entities (e.g., another user, robot, or autonomous agent) make decisions because they should consider each other’s subsequent behavior. Personality traits analyzed from responses to the questionnaire are easy to use for estimating performance but have low estimation accuracy. Machine learning using behavioral data on a target task can provide high accuracy, but such data is not available in advance. To solve this trade-off between in-advance estimability and estimation accuracy, we focused on the consistency of how people adjust their behavior to other collaborators in multiple tasks. We propose utilizing this consistency to estimate collaborative performance without performing the target task. We assumed that analyzing behavior adjustment in another task helps estimate performance of the target task. Our experimental results indicated that analyzing behavior adjustment in another task was beneficial to estimate collaborative performance in the target task, compared with personality-related questionnaire answers or performance of another task.application/pdfengLlicència CC Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0)info:eu-repo/semantics/conferenceObjectJoint ActionEstimate PerformanceDecision Makinginfo:eu-repo/semantics/openAccess