Associations of Space Technology Cooperation and Social Legitimacy with Mission Learning in Multinational Satellite Projects
Keywords:
Space Technology Cooperation, Mission Learning, Social Legitimacy, Satellite Projects, Multidisciplinary ResearchAbstract
Multinational satellite projects represent a critical frontier in modern space exploration, combining advanced technological integration with complex geopolitical and institutional alignments. This paper examines the mechanisms of mission learning within these collaborative endeavors, focusing on how space technology cooperation and social legitimacy interact to drive organizational adaptation and capability enhancement. Mission learning, defined as the institutionalized acquisition, retention, and application of technological and operational knowledge across successive space missions, is often constrained by geopolitical rivalries, technological barriers, and shifting public support. By analyzing several prominent multinational satellite consortia, this study conceptualizes a dual-pathway framework. First, space technology cooperation facilitates direct knowledge transfer, risk mitigation, and technical standard harmonization. Second, social legitimacy, derived from public approval, political consensus, and compliance with international space governance norms, secures the sustained resource flows and institutional stability necessary for long-term knowledge retention. This paper argues that high-quality mission learning occurs only when technological cooperation is reinforced by robust social legitimacy, creating a virtuous cycle of operational success and institutional resilience. The insights generated offer valuable policy recommendations for space agencies, international consortia, and policymakers aiming to optimize collaborative space initiatives.References
1. Orr, J.; Dutta, A. Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey. Sensors 2023, 23, 3625.
2. Puspitasari, A.A.; Lee, B.M. A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications. Sensors 2023, 23, 2554.
3. Han, S.; Dastani, M.; Wang, S. Sparse Communication in Multi-Agent Deep Reinforcement Learning. Neurocomputing 2025, 625, 129344.
4. Ke, Z.; Wang, X.; Du, Z.; Xiong, T.; Xu, Y.; Chen, J. Intelligent frequency reuse for dynamic spectrum anti-jamming: A hybrid-reward-based multi-agent deep reinforcement learning approach. IEEE Wirel. Commun. Lett. 2025, 14, 771–775.
5. Zhu, C.; Dastani, M.; Wang, S. A Survey of Multi-Agent Deep Reinforcement Learning with Communication. Auton. Agents Multi-Agent Syst. 2024, 38, 4.
6. Armengou, C., Bargheer, M., Gingold, A., Holsinger, S., Laakso, M., Mitchell, D., Mounier, P., Pölönen, J., Rooryck, J., Ševkušić, M., Souyioultzoglou, I., & Varachkina, H. (2024). Operational diamond OA criteria for journals. Available online: https://zenodo.org/records/12721408 (accessed on 10 March 2026).
7. Feng, Z.; Huang, M.; Wu, Y.; Wu, D.; Cao, J.; Korovin, I.; Gorbachev, S.; Gorbacheva, N. Approximating Nash Equilibrium for Anti-UAV Jamming Markov Game Using a Novel Event-Triggered Multi-Agent Reinforcement Learning. Neural Netw. 2023, 161, 330–342.
8. Bengio, Y.; Léonard, N.; Courville, A. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. arXiv 2013, arXiv:1308.3432.
9. Pilatti, L. E., Pilatti, L. A., de Carvalho, G. D. G., & de Resende, L. M. M. (2025). From fees to free: Comparing APC-based and diamond open access journals in engineering. Publications, 13(2), 16.
10. KOZO KEIKAKU ENGINEERING Inc. MAS Community. Available online: https://mas.kke.co.jp/en/ (accessed on 13 March 2025).
11. Mustafa, E.; Shuja, J.; Rehman, F.; Namoun, A.; Bilal, M.; Iqbal, A. Computation Offloading in Vehicular Communications Using PPO-Based Deep Reinforcement Learning. J. Supercomput. 2025, 81, 547.
12. Hu, F.; Fu, Q.; Zhang, S.; Huang, J. A Multi-Agent Deep Reinforcement Learning-Based Task Offloading Method for 6G-Enabled Internet of Vehicles with Cloud-Edge-Device Collaboration. Comput. Mater. Contin. 2026, 87, 1.
13. Gronauer, S.; Diepold, K. Multi-Agent Deep Reinforcement Learning: A Survey. Artif. Intell. Rev. 2022, 55, 895–943.
14. Liu, X.; Shi, M.; Wang, M. Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning. Electronics 2023, 12, 4529.
15. Oroojlooy, A.; Hajinezhad, D. A Review of Cooperative Multi-Agent Deep Reinforcement Learning. Appl. Intell. 2023, 53, 13677–13722.
16. Becerril, A., Bosman, J., Bjørnshauge, L., Frantsvåg, J. E., Kramer, B., Langlais, P.-C., Mounier, P., Proudman, V., Redhead, C., & Torny, D. (2021). OA diamond journals study. Part 2: Recommendations. Zenodo.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors

This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), unless otherwise stated.