Team Dynamics and Collaborative Problem-Solving for Lunar Construction: Lessons From Complex Construction Scenarios on Earth
Bibliographic record
Abstract
OBJECTIVE: This paper surveys the existing literature surrounding problem-solving and team dynamics in complex and unpredictable scenarios, and evaluates the applicability of studying Earth-based construction teams to identify training needs for Lunar construction crews. BACKGROUND: Lunar and other space exploration construction crews will work in extreme environments and face unpredictable challenges, necessitating real-time problem-solving to address unexpected contingencies. This work will require coordination with Mission Control and autonomous assistants, so crew training must account for multi-agent, distributed teamwork. METHOD: A narrative literature review identified processes, attributes, and skills necessary for the success of Lunar construction teams. We summarized relevant frameworks and synthesized collective findings into over-arching trends and remaining research gaps. RESULTS: While significant literature exists surrounding team performance, very little systematic inquiry has been done with a focus on Lunar construction crews and operations, particularly with respect to dynamic problem-solving and team-based decision-making. Established and standardized metrics for evaluating team performance are lacking, resulting in significant variation in reported outcomes between studies. CONCLUSION: Lunar and other space exploration construction teams will need training that focuses on developing the right approach to team-based problem-solving, rather than on preparing response execution for known contingencies. An investigation of successful Earth-based construction crews may facilitate the development of relevant metrics for training future Lunar construction crews. APPLICATION: Metrics and team training protocols developed for future Lunar construction teams may be adaptable and applicable to a wide range of extreme teams facing uncertain challenges, such as aircrews, surgical teams, first responders, and construction crews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".