Combinatorics of Correlated Equilibria

This page contains auxiliary files to the article:
Marie-Charlotte Brandenburg, Benjamin Hollering, and Irem Portakal: Combinatorics of correlated equilibria
In: Experimental mathematics, Vol. not yet known, pp. not yet known
../_images/traffic-lights.png ../_images/face_lattice.png

Abstract: We study the correlated equilibrium polytope \(P_G\) of a game \(G\) from a combinatorial viewpoint. We introduce the region of full-dimensionality for this class of polytopes, and prove that it is a semialgebraic set for any game. Through the use of the oriented matroid strata, we propose a structured method for describing combinatorial types of \(P_G\), and show that for \((2 \times n)\)-games, the algebraic boundary of each stratum is the union of coordinate hyperplanes and binomial hypersurfaces. We provide a computational proof that for \((2 \times 3)\)-games there exists a unique combinatorial type of maximal dimension.

Supplementary Material

This page contains supplementary material for the computations performed in the article. All files with ending .nb are run with \(\verb|Mathematica|\). Files endig with .py are run in \(\verb|SageMath|\) and diles endig with .m2 are run in \(\verb|Macaulay2|\). A file with ending .ipnynb is a Jupyter Notebook that is executed with a \(\verb|SageMath|\) kernel.

Computing the Polytope from Payoffs

We provide code to compute the correlated equilbrium polytope and the correlated equilibrium cone of a fixed \((d_1 \times \dots \times d_n)\)-game with payoff tensor in \(X\) in \(\verb|SageMath|\). First, download the file correlated_polytope_cone.py and place it in the same folder as your Jupyter Notebook for SageMath. An example of a \((2 \times 3)\)-game can then be computed by the lines

from correlated_polytope_cone import *
D_i = [2,3]
X = [
    [9,4],     # X_{11} = [X_{11}^(1), X_{11}^(2)]
    [4,0],     # X_{12} = [X_{12}^(1), X_{12}^(2)]
    [6,2],     # X_{13} = [X_{13}^(1), X_{13}^(2)]
    [5,5],     # X_{21} = [X_{21}^(1), X_{21}^(2)]
    [3,8],     # X_{22} = [X_{22}^(1), X_{22}^(2)]
    [9,7]      # X_{23} = [X_{23}^(1), X_{23}^(2)]
]
P = correlated_equilibrium_polytope(D_i, X, base_ring = QQ)
C = correlated_equilibrium_cone(D_i, X, base_ring = QQ)

The input \(D\_i = [d_1,\dots,d_n]\) is the type of the game, e.g. D_i = [2,3] for a \((2 \times 3)\)-game as in the example above. X above corresponds to the payoff table.

Player 2

\(s_1^{(2)}\)

\(s_2^{(2)}\)

\(s_3^{(2)}\)

Player 1

\(s_1^{(1)}\)

(9,4)

(4,0)

(6,2)

\(s_2^{(1)}\)

(5,5)

(3,8)

(9,7)


Section 4: The Region of Full-Dimensionality

Section 5: Combinatorial Types of Correlated Equilibrium Polytopes

Proof of Theorem 5.9

Code for Random Sampling Combinatorial Types of Polytopes


Credits
Project page created: 14/09/2022
Project contributors: Marie-Charlotte Brandenburg, Benjamin Hollering, and Irem Portakal
Software used: SageMath (Version 9.6), Mathematica (Version 13.0), Macaulay2 (Version 1.20)
System setup used: MacBook Pro with macOS Monterey 12.5.1, Processor 3,1 GHz Quad-Core Intel Core i7, Memory 16 GB 2133 MHz LPDDR3, Graphics Intel HD Graphics 630 1536 MB.
Corresponding authors of this page: Marie-Charlotte Brandenburg (marie.brandenburg@mis.mpg.de) and Benjamin Hollering (benjamin.hollering@mis.mpg.de)