Search arXiv⌕ Search

arXiv · 2009.04253

Structured Equilibria for Dynamic Games with Asymmetric Information and Dependent Types

Abstract

We consider a dynamic game with asymmetric information where each player observes privately a noisy version of a (hidden) state of the world V, resulting in dependent private observations. We study structured perfect Bayesian equilibria that use private beliefs in their strategies as sufficient statistics for summarizing their observation history. The main difficulty in finding the appropriate sufficient statistic (state) for the structured strategies arises from the fact that players need to construct (private) beliefs on other players' private beliefs on V, which in turn would imply that an infinite hierarchy of beliefs on beliefs needs to be constructed, rendering the problem unsolvable. We show that this is not the case: each player's belief on other players' beliefs on V can be characterized by her own belief on V and some appropriately defined public belief. We then specialize this setting to the case of a Linear Quadratic Gaussian (LQG) non-zero-sum game and we characterize linear structured PBE that can be found through a backward/forward algorithm akin to dynamic programming for the standard LQG control problem. Unlike the standard LQG problem, however, some of the required quantities for the Kalman filter are observation-dependent and thus cannot be evaluated off-line through a forward recursion.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nasimeh Heydaribeni, Achilleas Anastasopoulos. 2020-09-07. Structured Equilibria for Dynamic Games with Asymmetric Information and Dependent Types. https://arxiv.org/abs/2009.04253

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

From Data to Sliding Mode Control of Uncertain Large-Scale Networks with Unknown Dynamics

In this paper, we develop a compositional data-driven approach for the global stabilization of large-scale nonlinear networks with unknown dynamics and external perturbations. We first collect data along a single trajectory of each unknown nominal subsystem during a finite-time experiment. The data collected from each nominal subsystem are then used to design a feedback law that renders each nominal closed-loop subsystem input-to-state stable (ISS), certified by its corresponding ISS Lyapunov function. We derive conditions as data-dependent semidefinite programs that simultaneously yield local ISS controllers and the corresponding ISS Lyapunov functions. To cancel the effect of external perturbations on subsystem dynamics and, consequently, on the whole network dynamics, we then design a local integral sliding mode (ISM) controller for each subsystem using the collected data. Under a small-gain compositional condition, we employ data-driven ISS Lyapunov functions designed for the subsystems to construct a control Lyapunov function for the network, guaranteeing that the nominal closed-loop network is globally asymptotically stable (GAS) at the origin. We then extend this compositional result to perturbed networks, proving that the synthesized ISM controllers render the origin of the closed-loop network GAS even in the presence of perturbations. We demonstrate the efficacy of the proposed data-driven approach on large-scale interconnected networks with five distinct interconnection topologies.

eess.SY↗

Simultaneous improvement of control and estimation for battery management systems

Standard battery management systems treat the control and state estimation problems as decoupled objectives, relying on certainty equivalence controllers that are blind to the varying observability induced by nonlinear open-circuit voltage models. In this paper, we show that for a broad class of objectives, including the peak shaving and valley filling scenarios common in grid-connected energy storage, the expected cost of a stochastic battery system can be exactly parametrized by the conditional mean and covariance of the state of charge. This reformulation reveals a direct coupling between the control input and estimation quality, a coupling that certainty equivalence controllers ignore, and motivates a dual-control approach in which the controller actively reduces estimation uncertainty by driving the state to high observability regions without compromising the control objective. We derive a deterministic surrogate to this stochastic cost and pose the dual-control problem as a computationally tractable model predictive control problem. We validate our approach on a nine-battery system tracking a time-varying reference trajectory. We report simultaneous improvements in tracking cost (a 28\% reduction) and state estimation error (up to 18\% reduction). The estimation improvement is reported across different state estimators: extended Kalman filter, unscented Kalman filter, and a moving horizon estimator, confirming that the estimation improvement of our approach is not restricted to a specific state observer.

eess.SY↗

Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination

Advanced Air Mobility operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic management systems to ensure collision-free operations in highly congested environments. In this paper, we propose a multi-agent coordination framework that uses minimum time-to-reach (TTR) as a unifying metric for priority assignment, temporal separation, and safety filtering. We focus on the problem of coordinating multiple aerial vehicles merging into an air corridor while maintaining safe separation between vehicles. Vehicles are assigned arrival-consistent priority based on TTR, and target TTR values are used to enforce temporal spacing, which induces spatial separation. A priority-consistent safety filtering layer based on Hamilton-Jacobi reachability value functions promotes collision avoidance while minimally modifying the reference guidance. Simulation results in a highly congested corridor merging scenario show that the proposed method improves safety, fairness, and efficiency compared to time-optimal guidance and priority-agnostic safety filtering.

eess.SY↗