Best Approximate Solution for Generalized Nash Games and Quasi-optimization Problems

TL;DR

This paper introduces the concept of best approximate solutions for generalized Nash games with non-self constraint maps, establishing their existence under certain conditions and extending the idea to quasi-optimization problems.

Contribution

It defines and proves the existence of best approximate solutions for generalized Nash games with infinitely many players and non-self constraints, a novel extension in game theory.

Findings

Existence of best approximate solutions under quasi-concavity and weak continuity.

Application of maximum theorem and fixed point results to establish solutions.

Extension of the concept to quasi-optimization problems.

Abstract

In this article, we consider generalized Nash games where the associated constraint map is not necessarily self. The classical Nash equilibrium may not exist for such games and therefore we introduce the notion of best approximate solution for such games. We investigate the occurrence of best approximate solutions for such generalized Nash games consisting of infinitely many players in which each player regulates the strategy variable lying in a topological vector space. Based on the maximum theorem and a fixed point result for Kakutani factorizable maps, we derive the existence of best approximate solutions under the quasi-concavity and weak continuity assumption on players' objective functions. Furthermore, we demonstrate the occurrence of best approximate solutions for quasi-optimization problems.