Dynamic Signaling Games under Nash and Stackelberg Equilibria

Download

index.pdf

Date

2016-01-01

Author

Sarıtaş, Serkan
Gezici, Sinan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

88
views

33
downloads

In this study, dynamic and repeated quadratic cheap talk and signaling game problems are investigated. These involve encoder and decoders with mismatched performance objectives, where the encoder has a bias term in the quadratic cost functional. We consider both Nash equilibria and Stackelberg equilibria as our solution concepts, under a perfect Bayesian formulation. These two lead to drastically different characteristics for the equilibria. For the cheap talk problem under Nash equilibria, we show that fully revealing equilibria cannot exist and the final state equilibria have to be quantized for a large class of source models; whereas, for the Stackelberg case, the equilibria must be fully revealing regardless of the source model. In the dynamic signaling game where the transmission of a Gaussian source over a Gaussian channel is considered, the equilibrium policies are always linear for scalar sources under Stackelberg equilibria, and affine policies constitute an invariant subspace under best response maps for Nash equilibria.

URI

https://hdl.handle.net/11511/94394

DOI

https://doi.org/10.1109/isit.2016.7541575

Conference Name

IEEE International Symposium on Information Theory (ISIT)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

On Multi-Dimensional and Noisy Quadratic Signaling Games and Affine Equilibria Sarıtaş, Serkan; Gezici, Sinan (2015-01-01) This study investigates extensions of the quadratic cheap talk and signaling game problem, which has been introduced in the economics literature. Two main contributions of this study are the extension of Crawford and Sobel's cheap talk formulation to multi-dimensional sources, and the extension to noisy channel setups as a signaling game problem. We show that, in the presence of misalignment, the quantized nature of all equilibrium policies holds for any scalar random source. It is shown that for multi-dime...
Learning Graph Signal Representations with Narrowband Spectral Kernels Kar, Osman Furkan; Turhan, Gülce; Vural, Elif (2022-01-01) In this work, we study the problem of learning graph dictionary models from partially observed graph signals. We represent graph signals in terms of atoms generated by narrowband graph kernels. We formulate an optimization problem where the kernel parameters are learnt jointly with the signal representations under a triple regularization scheme: While the first regularization term aims to control the spectrum of the narrowband kernels, the second term encourages the reconstructed graph signals to vary smoot...
Probabilistic distance clustering on networks İyigün, Cem (null; 2018-11-05) In this study, a soft clustering problem on networks is investigated. It is assumed that cluster centers are located not only on vertices, but also on the edges of the network. Two different soft assignment schemes are studied where different membership functions are considered for the assignments. Structural properties for the clustering problem have been derived for different problem settings and the relation to the p-median problem has been shown under specific conditions. As a solution approach, a metah...
Finite-Difference Sensitivity Calculation in Iteratively Solved Problems Eyi, Sinan (2011-01-01) In this study, the accuracy of the finite-difference sensitivities is examined in iteratively solved problems. The aim of this research is to reduce the error in the finite difference sensitivity calculations. The norm value of the finite-difference sensitivity error in the state variables is minimized with respect to the finite-difference step size. The optimum finitedifference step size is formulated as a function of the norm values of both convergence error and higher order sensitivities. In order to cal...
Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals Acar, Abdullah Burak; Vural, Elif (2022-01-01) In this work, we study the problem of learning time-vertex dictionaries for the modeling and estimation of time-varying graph signals. We consider a setting with a collection of partially observed time-varying graph signals, and propose a solution for the estimation of the missing signal observations by learning time-vertex dictionaries from the available observations. We adopt a time-vertex dictionary model defined through a set of joint time-vertex spectral kernels, each of which captures a different spec...

Citation Formats

S. Sarıtaş and S. Gezici, “Dynamic Signaling Games under Nash and Stackelberg Equilibria,” presented at the IEEE International Symposium on Information Theory (ISIT), Barcelona, İspanya, 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94394.