CREWS: Crew Recommender for Effective Work in Space

Team composition, the configuration of member attributes and their relationships, is a critical enabling feature of fostering effective teamwork and likely to play an important role in the effectiveness of future long-duration space exploration (LDSE). Limited research on team composition in environments analogous to LDSE exists, and currently how team composition can be used to optimize crew functioning and performance is unclear.

In the past year, we continued to make progress on each of our three research aims (as identified in previous task book and annual reports): (1) to develop an agent-based model of team composition for LDSE based on empirical data linking key model inputs (e.g., individual difference variables, network relational factors, task characteristics) to team functioning (e.g., social integration, team processes, team cohesion, team conflict) in LDSE-relevant contexts; (2) to conduct virtual experiments using characteristics and relationships identified in Phase I to identify the team functioning patterns that arise under different member compositions, and create a predictive model of team composition; and (3) to conduct an initial validation of the model we developed in LDSE analogs.

In addition to the above tasks, we have also worked to incorporate additional data into our models and analyses, in order to establish credibility and robustness of our results. This has included both the incorporation of new data from HERA (Campaign 6), as well as adjusting models originally developed using HERA to also incorporate data from NEK SIRIUS (Nezemnyy Eksperimental’nyy Kompleks / Scientific International Research In a Unique terrestrial Station) analog missions. These steps help ensure that our model is appropriate given the availability of new data.

Progress on Research Aim #1: Our first research aim is to develop an agent-based model of team composition for LDSE based on empirical data linking key model inputs (e.g., individual difference variables, network relational factors, task characteristics) to team functioning (e.g., social integration, team processes, team cohesion, team conflict) in LDSE-relevant contexts. Over the reporting period, we continued to make incremental developments and refinements on the agent-based model (ABM) we developed of social relationships within crews. We split our model into two parts: one part focusing on positive and negative relationships (task affect and hindrance) between crew members, and the other part focusing on claiming and granting leadership between crew members. Within each of the two parts, we implemented code to model the interaction between different types of ties: interactions between positive and negative relationships, and interactions between relationships claiming leadership and relationships granting leadership.

Progress on Research Aim #2: Our second research aim is to conduct virtual experiments using characteristics and relationships identified in Phase I to identify the team functioning patterns that arise under different member compositions, and create a predictive model of team composition. During the past reporting period, using the new model fit, we began conducting these virtual experiments using computational simulation. In these virtual experiments, we examine how different hypothetical team compositions (team member’s demographics, relationships, task schedules) would perform during LDSE according to our model. Using optimization methods, we have developed a way to select high-performance teams based out of a pool of different potential crew members.

Progress on Research Aim #3: Our third research aim is to conduct an initial validation of the model developed in LDSE analogs. This year, we analyzed results of the validation test of the model conducted in Campaign 5, and began compiling the results for presentation and publication. Our ABM was used to preemptively recommend “best” and “worst” pairings of crew members for the Rover and Phobos tasks in mission. Using surveys gathered over the course of each mission, we were able to evaluate the outcomes of using both “best” and “worst” pairings (the change in social relationships, their satisfaction with each pairing, viability). By performing new statistical analyses, we were able to demonstrate the viability of using our ABM to identify best pairings in advance of a future mission. These results will be presented at the upcoming International Astronautical Congress in October 2021.