La Teorí­a de Juegos en la Administración Estratégica Empresarial

Este artí­culo proporciona una revisión de literatura de la historia de la teorí­a de juegos y sus pronósticos, así­ como también modelos mentales y emociones estratégicas aplicadas en las instituciones económicas y la sociedad en general. El hilo común en este trabajo es referirse a las teorí­as en la administración estratégica empresarial basada en la teorí­a de juegos para de esta manera tener una comprensión profunda de la aplicabilidad de estas teorí­as en los mercados competitivos

On the Interplay between Social Welfare and Tractability of Equilibria

Computational tractability and social welfare (aka. efficiency) of equilibria are two fundamental but in general orthogonal considerations in algorithmic game theory. Nevertheless, we show that when (approximate) full efficiency can be guaranteed via a smoothness argument \`a la Roughgarden, Nash equilibria are approachable under a family of no-regret learning algorithms, thereby enabling fast and decentralized computation. We leverage this connection to obtain new convergence results in large games -- wherein the number of players $n \gg 1$ -- under the well-documented property of full efficiency via smoothness in the limit. Surprisingly, our framework unifies equilibrium computation in disparate classes of problems including games with vanishing strategic sensitivity and two-player zero-sum games, illuminating en route an immediate but overlooked equivalence between smoothness and a well-studied condition in the optimization literature known as the Minty property. Finally, we establish that a family of no-regret dynamics attains a welfare bound that improves over the smoothness framework while at the same time guaranteeing convergence to the set of coarse correlated equilibria. We show this by employing the clairvoyant mirror descent algortihm recently introduced by Piliouras et al.Comment: To appear at NeurIPS 202

Econometric Inference on Large Bayesian Games with Heterogeneous Beliefs

Econometric models on games often assume observation of many replications of a single representative game. Such a framework is not adequate when one observes multiple heterogeneous many-player games, as in many models of social interactions. This paper considers a static large Bayesian game, and develops inference methods which does not require a common prior assumption, and allows for the players to form beliefs differently from other players. By drawing on the main intuition of Kalai (2004), this paper introduces the notion of a hindsight regret which measures each player's ex post value of other players' type information, and obtains its belief-free bound. From this bound, this paper derives testable implications and develops an asymptotic inference procedure for the structural parameters