Total Task Completion Statistics for Lunar Mission Agent-Based Model

Raymond Vera's dataset from 2026 contains simulation results from an agent-based model exploring human factors in future lunar missions. The model, representing astronauts with cognitive and emotional states, uses Monte Carlo simulations to show trade-offs in productivity and psychological well-being. It was created to support mission planning for the Artemis program.

Use Cases

• Analyze productivity and psychological well-being trade-offs based on Monte Carlo simulation results mentioned in the description
• Evaluate operational resilience and team structures for lunar missions based on simulated social and environmental interactions
• Study the effects of cognitive skills, emotional states, and personality traits on mission outcomes using the Agent_Astronaut framework

Strengths

• Model is based on literature from proxy environments like Antarctica and past space missions
• Monte Carlo simulations consist of tens of thousands of iterations
• Dataset is openly licensed under CC-BY-4.0

Limitations

• Dataset size is only 5.5 KB, indicating very limited scope
• Row count and column-level documentation are unknown
• Description metadata is limited; actual data quality requires manual inspection after download

Provenance

Source

Raymond Vera via figshare

Collection Method

Generated from an agent-based model (ABM) simulation of lunar mission scenarios

Freshness

Last updated 2026-05-27 17:24:17; freshness should be verified

Geography

Simulated lunar environment (Lunar Gateway and Lunar South Pole Base)