In this paper we present the extraproximal method for computing the Stackelberg/Nash equilibria in a class of ergodic controlled finite Markov chains games. We exemplify the original game formulation in terms of coupled nonlinear programming problems implementing the Lagrange principle. In addition, Tikhonov’s regularization method is employed to ensure the convergence of the cost-functions to a Stackelberg/Nash equilibrium point. Then, we transform the problem into a system of equations in the proximal format. We present a two-step iterated procedure for solving the extraproximal method: (a) the first step (the extra-proximal step) consists of a “prediction” which calculates the preliminary position approximation to the equilibrium point, and (b) the second step is designed to find a “basic adjustment” of the previous prediction. The procedure is called the “extraproximal method” because of the use of an extrapolation. Each equation in this system is an optimization problem for which the necessary and efficient condition for a minimum is solved using a quadratic programming method. This solution approach provides a drastically quicker rate of convergence to the equilibrium point. We present the analysis of the convergence as well the rate of convergence of the method, which is one of the main results of this paper. Additionally, the extraproximal method is developed in terms of Markov chains for Stackelberg games. Our goal is to analyze completely a three-player Stackelberg game consisting of a leader and two followers. We provide all the details needed to implement the extraproximal method in an efficient and numerically stable way. For instance, a numerical technique is presented for computing the first step parameter (λ) of the extraproximal method. The usefulness of the approach is successfully demonstrated by a numerical example related to a pricing oligopoly model for airlines companies.
Keywords
- extraproximal method
- Stackelberg games
- convergence analysis
- Markov chains
- implementation
Global Behavior of a Multi–Group Seir Epidemic Model with Spatial Diffusion in a Heterogeneous Environment Bootstrap Methods for Epistemic Fuzzy Data Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data Joint Feature Selection and Classification for Positive Unlabelled Multi–Label Data Using Weighted Penalized Empirical Risk Minimization Hybrid Deep Learning Model–Based Prediction of Images Related to Cyberbullying Fault–Tolerant Tracking Control for a Non–Linear Twin–Rotor System Under Ellipsoidal Bounding A Multi–Model Based Adaptive Reconfiguration Control Scheme for an Electro–Hydraulic Position Servo System Reliability–Aware Zonotopic Tube–Based Model Predictive Control of a Drinking Water Network A Graph Theory–Based Approach to the Description of the Process and the Diagnostic System On Some Ways to Implement State–Multiplicative Fault Detection in Discrete–Time Linear Systems A Kalman Filter with Intermittent Observations and Reconstruction of Data Losses Parameter Identifiability for Nonlinear LPV Models