Learning Convex Partitions and Computing Game-theoretic Equilibria from Best Response Queries

Suppose that an \(m\)-simplex is partitioned into \(n\) convex regions having disjoint interiors and distinct labels, and we may learn the label of any point by querying it. The learning objective is to know, for any point in the simplex, a label that occurs within some distance \(\epsilon\) from th...

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Bibliographic Details
Published in:arXiv.org 2019-04
Main Authors: Goldberg, Paul W, Marmolejo-Cossío, Francisco J
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Computation
Equilibrium
Fixed points (mathematics)
Game theory
Queries
Online Access:Get full text
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Summary:Suppose that an \(m\)-simplex is partitioned into \(n\) convex regions having disjoint interiors and distinct labels, and we may learn the label of any point by querying it. The learning objective is to know, for any point in the simplex, a label that occurs within some distance \(\epsilon\) from that point. We present two algorithms for this task: Constant-Dimension Generalised Binary Search (CD-GBS), which for constant \(m\) uses \(poly(n, \log \left( \frac{1}{\epsilon} \right))\) queries, and Constant-Region Generalised Binary Search (CR-GBS), which uses CD-GBS as a subroutine and for constant \(n\) uses \(poly(m, \log \left( \frac{1}{\epsilon} \right))\) queries. We show via Kakutani's fixed-point theorem that these algorithms provide bounds on the best-response query complexity of computing approximate well-supported equilibria of bimatrix games in which one of the players has a constant number of pure strategies. We also partially extend our results to games with multiple players, establishing further query complexity bounds for computing approximate well-supported equilibria in this setting.
ISSN:2331-8422