Re-pairing to Repair: A Countermeasure that Enhances Crew Relations in Deep Space

Supporting effective social relationships is critical for teams - especially the crews of space exploration missions, who must collaborate on tasks autonomously under extreme conditions that have been shown to degrade social relationships. We develop an agent-based computational model (ABM) that simulates social relationships in crews as they complete their work, as well as the evolution of these social relationships during long-duration space exploration (LDSE). We calibrate the model using data gathered from crew completing missions in LDSE analogs, on Earth, over a period of 30- to 45-days. This empirical calibration allows the model to predict the formation of social relationships based on team composition as well as task scheduling and LDSE characteristics. We demonstrate how empirically driven ABMs enable the recommendation of countermeasures that can support teams, in this case to promote positive and dissuade negative social relationships within teams. We consider a “re-pairing” countermeasure, in which we reassign which pairs of crew members are assigned to collaborate based on simulation results from the ABM. We test the effectiveness of these ABM-recommended countermeasures among four member crews completing 45-day missions in the HERA LDSE analog. Crews are assigned to work on tasks in either “recommended” or “worst” pairs determined by the ABM. Use of “recommended” or “worst” pairs was alternated each quarter of the mission, as part of an “A-B-A-B” block experimental design within each crew. During the “recommended” pairings, the crew members were more likely to report positive social relationships and were less likely to report that working together was damaging to their relationship. These findings demonstrate the viability of using computational models to recommend team countermeasures for deep space exploration.