Duration: 6 months
The commercialization of space operations introduces multi-actor coordination challenges where strategic decision-making must be analyzed under operational and regulatory constraints. Modeling and solution approaches to these problems include simulation-based methods, agent-based modeling, and qualitative game theory, but these lack systematic capabilities for policy optimization and counterfactual analysis.
Mathematical optimization models for strategic interactions offer distinct advantages: (i) they enable systematic policy design by optimizing over regulatory instruments rather than evaluating discrete scenarios, (ii) they provide provable properties of equilibria including existence, uniqueness, and comparative statics, (iii) they support rigorous sensitivity analysis under parametric uncertainties, and (iv) they integrate naturally with mission analysis through constraint formulations derived from mission constraints, trajectory optimization and systems engineering.
This research seek to develop game-theoretic optimization frameworks for economic and policy evaluation of space operations, focusing on two modeling paradigms: non-cooperative games formulated as complementarity problems (capturing pure competition among operators without regulation) and Stackelberg games formulated as bilevel programs (capturing hierarchical regulatory design). The objective is to develop modeling and solution algorithms based on complementarity reformulation, regularization, and decomposition techniques, integrate the frameworks with space mission simulation and trajectory optimization capabilities at ACT, and demonstrate applications to modeling and analyzing the emerging space economy, particularly In-Situ Resource Utilization scenarios (lunar water extraction, asteroid mining). The methodology generalizes to other multi-actor space operations including active debris removal, on-orbit servicing markets, and mega-constellation management.
