arXiv subjects

Jaehan Im

Publications and source records attributed to Jaehan Im.

9 recordsLinked to original sources

arXiv2026

Secure Coordination for Vertiport Sequencing in Advanced Air Mobility

Advanced air mobility operations will require reliable coordination mechanisms for managing dense traffic near vertiports. However, sequencing decisions may become vulnerable when they rely on potentially falsified self-reported information such as estimated time of arrival. Self-interested vehicles may misreport their arrival times to obtain favorable landing priority, while malicious actors may spoof information to disrupt sequencing decisions or induce unnecessary congestion. This paper studies secure coordination for vertiport sequencing under sensing uncertainty. We consider a coordinator that combines self-reported Remote-ID information with externally obtained surveillance measurements to check reports and assign separation-feasible arrival schedules. Since surveillance-based estimates are uncertain, falsified reports may remain consistent with the sensing uncertainty region and cannot always be rejected outright. We therefore formulate sequencing as a robust design problem over this uncertainty region. Self-interested misreporting is modeled as a strategic deviation that improves the reporting vehicle's own sequencing outcome, whereas malicious spoofing is modeled as an adversarial disturbance that degrades the system-level objective. The final paper will develop robust sequencing rules over surveillance-consistent uncertainty sets and evaluate their performance in representative vertiport sequencing scenarios.

eess.SY↗

arXiv2026

Scalable Coordination with Chance-Constrained Correlated Equilibria via Reduced-Rank Structure

Chance-constrained correlated equilibrium enables coordination of noncooperative agents under cost uncertainty through probabilistic incentive-compatibility guarantees. However, computing such equilibria becomes intractable in large-scale systems due to the exponential growth of the joint action space. We develop an approximation method for computing chance-constrained correlated equilibria by showing that these equilibria admit a representation as convex combinations of a finite set of chance-constrained pure Nash equilibria, enabling tractable computation without solving the full correlated equilibrium program. Numerical experiments on large-scale multi-airline coordination scenarios demonstrate substantial reductions in computation time while achieving lower system delay costs compared to current operational practice. Under cost uncertainty, the proposed method consistently achieves lower deviation rate compared to the full formulation while achieving comparable coordination performance.

cs.GT↗

arXiv2026

Chance-Constrained Correlated Equilibria for Robust Noncooperative Coordination

Correlated equilibria enable a coordinator to influence the self-interested agents by recommending actions that no player has an incentive to deviate from. However, the effectiveness of this mechanism relies on accurate knowledge of the agents' cost structures. When cost parameters are uncertain, the recommended actions may no longer be incentive compatible, allowing agents to benefit from deviating from them. We study a chance-constrained correlated equilibrium problem formulation that accounts for uncertainty in agents' costs and guarantees incentive compatibility with a prescribed confidence level. We derive sensitivity results that quantify how uncertainty in individual incentive constraints affects the expected coordination outcome. In particular, the analysis characterizes the value of information by relating the marginal benefit of reducing uncertainty to the dual sensitivities of the incentive constraints, providing guidance on which sources of uncertainty should be prioritized for information acquisition. The results further reveal that increasing the confidence level is not always beneficial and can introduce a tradeoff between robustness and system efficiency. Numerical experiments demonstrate this tradeoff: CC-CE reduces realized coordination cost by up to 35% at intermediate confidence levels, while the proposed information-gain metric consistently identifies effective uncertainty sources to reduce.

cs.GT↗

arXiv2026

Noncooperative Coordination for Decentralized Air Traffic Management

Decentralized air traffic management requires coordination among self-interested stakeholders operating under shared safety and capacity constraints, where conventional centralized or implicitly cooperative models do not adequately capture this setting. We develop a unified perspective on noncooperative coordination, in which system-level outcomes emerge by designing incentives and assigning signals that reshape individual optimality rather than imposing cooperation or enforcement. We advance this framework along three directions: scalable equilibrium engineering via reduced-rank and uncertainty-aware correlated equilibria, decentralized mechanism design for equilibrium selection without enforcement, and structured noncooperative dynamics with convergence guarantees. Beyond these technical contributions, we discuss core design principles that govern incentive-compatible coordination in decentralized systems. Together, these results establish a foundation for scalable, robust coordination in safety-critical air traffic systems.

eess.SY↗

arXiv2026

Noncooperative Virtual Queue Coordination via Uncertainty-Aware Correlated Equilibria

Collaborative virtual queueing has been proposed as a mechanism to mitigate airport surface congestion while preserving airline autonomy over aircraft-level pushback decisions. A central coordinator can regulate aggregate pushback capacity but cannot directly control which specific aircraft are released, limiting its ability to steer system-level performance. We propose a noncooperative coordination mechanism for collaborative virtual queueing based on the correlated equilibrium concept, which enables the coordinator to provide incentive-compatible recommendations on aircraft-level pushback decisions without overriding airline autonomy. To account for uncertainty in airlines' internal cost assessments, we introduce chance constraints into the correlated equilibrium formulation. This formulation provides explicit probabilistic guarantees on incentive compatibility, allowing the coordinator to adjust the confidence level with which airlines are expected to follow the recommended actions. We further propose a scalable algorithm for computing chance-constrained correlated equilibria by exploiting a reduced-rank structure. Numerical experiments demonstrate that the proposed method scales to realistic traffic levels up to 210 eligible pushbacks per hour, reduces accumulated delay by up to approximately 8.9% compared to current first-come-first-served schemes, and reveals a trade-off between confidence level, deviation robustness, and achievable cost efficiency.

eess.SY↗

arXiv2025

Iterative Negotiation and Oversight: A Case Study in Decentralized Air Traffic Management

Achieving consensus among self-interested agents remains challenging in decentralized multi-agent systems, where agents often have conflicting preferences. Existing coordination methods enable agents to reach consensus without a centralized coordinator, but do not provide formal guarantees on system-level objectives such as efficiency or fairness. To address this limitation, we propose a regulated decentralized negotiation framework that augments a decentralized negotiation mechanism with limited regulatory oversight. The framework builds upon the trading auction for consensus, enabling self-interested agents with conflicting preferences to negotiate through asset trading while avoiding direct disclosure of private asset valuations. We introduce an oversight mechanism, which implements a taxation-like intervention that guides decentralized negotiation toward system-efficient and equitable outcomes while also regulating how fast the framework converges. We establish theoretical guarantees of finite-time termination and derive bounds linking system efficiency and convergence rate to the level of regulatory intervention. A case study based on the collaborative trajectory options program, a rerouting initiative in U.S. air traffic management, demonstrates that the framework can reliably achieve consensus among self-interested airspace sector managers, and reveals how the level of regulatory intervention regulates the relationship between system efficiency and convergence speed. Taken together, the theoretical and experimental results indicate that the proposed framework provides a mechanism for regulated decentralized coordination that preserves noncooperative final selection while safeguarding system-level objectives.

cs.MA↗

arXiv2025

Game-theoretic Regulated Decentralized Coordination for Airspace Sector Overload Mitigation

Decentralized air traffic management systems offer a scalable alternative to centralized control, but often assume high levels of cooperation. In practice, such assumptions frequently break down since airspace sectors operate independently and prioritize local objectives. We address the problem of sector overload in decentralized air traffic management by proposing a regulated decentralized protocol that models self-interested behaviors based on best response dynamics. Each sector adjusts the departure times of flights under its control to reduce its own congestion, without requiring centralized joint optimization. A tunable cooperativeness factor models the degree to which each sector accounts for overload in other sectors, while a minimal admissibility rule prevents local updates from creating new overloads. We prove that the proposed protocol satisfies a potential game structure, ensuring that best response dynamics converge to a pure Nash equilibrium under this restriction. In addition, we identify a sufficient condition under which an overload-free solution corresponds to a global minimizer of the potential function. Numerical experiments using 24 hours of European flight data demonstrate that the proposed algorithm substantially reduces overload even with only minimal cooperation between sectors, while maintaining scalability and achieving solution quality comparable to the centralized benchmark.

eess.SY↗

arXiv2025

Noncooperative Coordination via a Trading-based Auction

Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, when agents act in their own self-interest, they may prefer different choices among multiple feasible outcomes, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach consensus without communicating directly or disclosing private valuations. TACo facilitates coordination through a structured trading-based auction, where agents iteratively select choices of interest and provably reach an agreement within an a priori bounded number of steps. A series of numerical experiments validate that the termination guarantees of TACo hold in practice, and show that TACo achieves a median performance that minimizes the total cost across all agents, while allocating resources significantly more fairly than baseline approaches.

cs.GT↗

arXiv2024

Coordination in Noncooperative Multiplayer Matrix Games via Reduced Rank Correlated Equilibria

Coordination in multiplayer games enables players to avoid the lose-lose outcome that often arises at Nash equilibria. However, designing a coordination mechanism typically requires the consideration of the joint actions of all players, which becomes intractable in large-scale games. We develop a novel coordination mechanism, termed reduced rank correlated equilibria, which reduces the number of joint actions to be considered and thereby mitigates computational complexity. The idea is to approximate the set of all joint actions with the actions used in a set of pre-computed Nash equilibria via a convex hull operation. In a game with n players and each player having m actions, the proposed mechanism reduces the number of joint actions considered from O(m^n) to O(mn). We demonstrate the application of the proposed mechanism to an air traffic queue management problem. Compared with the correlated equilibrium-a popular benchmark coordination mechanism-the proposed approach is capable of solving a problem involving four thousand times more joint actions while yielding similar or better performance in terms of a fairness indicator and showing a maximum optimality gap of 0.066% in terms of the average delay cost. In the meantime, it yields a solution that shows up to 99.5% improvement in a fairness indicator and up to 50.4% reduction in average delay cost compared to the Nash solution, which does not involve coordination.

cs.GT↗