2 edition of **Convergence and approximation results for non-cooperative Bayesian games** found in the catalog.

- 141 Want to read
- 17 Currently reading

Published
**1993**
by University of Illinois at Urbana-Champaign in Champaign
.

Written in English

- Economics

**Edition Notes**

Includes bibliographical references (p. 30-31).

Statement | Leonidas C. Koutsougeras, Nicholas C. Yannellis |

Series | BEBR faculty working paper -- no.93-0104, BEBR faculty working paper -- no.93-0104. |

Contributions | Yannelis, Nicholas C., University of Illinois at Urbana-Champaign. Bureau of Economic and Business Research |

The Physical Object | |
---|---|

Pagination | 31 p. ; |

Number of Pages | 31 |

ID Numbers | |

Open Library | OL25168433M |

OCLC/WorldCa | 535285639 |

Tue 2/ Convergence of no-regret dynamics in atomic splittable selfish routing and zero-sum games. References: AGT book, Section Fiat/Mansour/Nadav, On the Convergence of Regret Minimization Dynamics in Concave Games, STOC ' Thu 2/ PLS-completeness and negative convergence results. Primary reference: Section 3 of Roughgarden. The convergence of stochastic processes is defined in terms of the so-called “weak convergence” (w. c.) of probability measures in appropriate functional spaces (c. s. m. s.). Chapter 1. Let $\Re $ be the c.s.m.s. and v a set of all finite measures on $\Re $.

Keywords: Non-cooperative games; Bayesian equilibria; Approximate equilibria 1. Introduction The theory of strategic games with complete information starts with the works of J. von Neumann [10] and J. Nash [5]. Harsanyi in [3] introduces games with incomplete information, i.e. games . In models with nuisance parameters, Bayesian procedures based on Markov Chain Monte Carlo (MCMC) methods have been developed to approximate the posterior distribution of the parameter of interest. Because these procedures require burdensome computations related to the use of MCMC, approximation and convergence in these procedures are important issues.

() Bayesian Security Games for Controlling Contagion. International Conference on Social Computing, () Stochastic Nash equilibrium problems: sample average approximation and applications. This book brings together papers of well-known specialists in game theory and adjacent problems. It presents the basic results in dynamic games, stochastic games, applications of game theoretical methods in ecology and economics and methodological aspects of game theory.

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Convergence and approximation results for non-cooperative Bayesian games: learning theorems* Leonidas C. Koutsougeras and Nicholas C. Yannelis Department of Economics, University of Illinois at Urbana-Champaign, Champaign, IllinoisUSA Received: J ; revised version February 5, Summary.

LetT denote a continuous time horizon and {G t:t∈T} be a net (generalized sequence) of Bayesian games. We show that: (i) if {x t: t∈T} is a net of Bayesian Nash Equilibrium (BNE) strategies for Gt we can extract a subsequence which converges to a limit full information BNE strategy for a one shot limit full information Bayesian game, (ii) If {x t: t∈T} is a net of approximate or εt Cited by: "Convergence and Approximation Results for Non-cooperative Bayesian Games: Learning Theorems," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol.

4(6), pagesOctober. STX B COPY2 FACULTYWORKINGt*AFEK^^-U1U4 PoliticalEconomySeries#60 ConvergenceandApproximationResultsfor Non-CooperativeBayesianGames:LearningTheorems.

Convergence and approximation results for non-cooperative Bayesian games: learning theorems / By Leonidas C. Koutsougeras and Nicholas C. Yannelis. Get PDF (3 MB) Abstract.

Includes bibliographical references (p. Convergence and approximation results for non-cooperative Bayesian games: Learning theorems Leonidas C. Koutsougeras, Nicholas C.

Yannelis Pages OriginalPaper. BibTeX @MISC{Koutsougeras92econom/ctheory, author = {Leonidas C. Koutsougeras and Nicholas C. Yannelis}, title = {Econom/c Theory 9 Springer-Verlag Convergence and approximation results for non-cooperative Bayesian games: learning theorems*}, year = {}}.

Publisher Summary. This chapter explains that the Nash equilibria (NE) are of paramount importance in non-cooperative games. In these games, which can be static or dynamic, with complete or incomplete information, with perfect or imperfect observation, players are generally assumed to have a good knowledge of the structure of the game.

Convergence and Approximation Results for Non-Cooperative Bayesian Games: Learning Theorems (with L. Koutsougeras), Economic Theory, 4,; An Elementary Proof of the Knaster-Kuratowski-Mazurkiewitz-Shapley Theorem (with S.

Krasa), Economic Theory, 4, Convergence and Approximation Results for Non-Cooperative Bayesian Games: Learning Theorems (with ugeras), Economic Theory, 4,An Elementary Proof of the Knaster-Kuratowski-Mazurkiewitz-Shapley Theorem (with ), Economic Theory, 4,Convergence and Approximation Results for Non-Cooperative Bayesian Games: Learning Theorems (with L.

Koutsougeras), Economic Theory, 4,An Elementary Proof of the Knaster-Kuratowski-Mazurkiewitz-Shapley Theorem (with S. Krasa), Economic Theory, 4,The Value Allocation of an Economy with Differential Information (with.

Convergence and Approximation Results for Non-Cooperative Bayesian Games: Learning Theorems(with ugeras), Economic Theory, 4,An Elementary Proof of the Knaster-Kuratowski-Mazurkiewitz-Shapley Theorem(with S. Krasa), Economic Theory, 4, We examine repeated games with incomplete information where the type spaces of the players may be large.

It is shown that if the belief of each player, regarding future play of the game, accommodates the true play then a Nash equilibrium of the incomplete information game will evolve, with time, into an equilibrium of the complete information game, i.e., the realized game where the types of. Convergence and Approximation Results for Non-cooperative Bayesian Games: Learning Theorems.

t∈T} be a net (generalized sequence) of Bayesian games. We show that: (i) if {x t. A general condition called “coarser inter-player information” is introduced and shown to be necessary and sufficient for the validity of several fundamental properties on pure-strategy equilibria in Bayesian games, such as existence, purification from behavioral strategies, and convergence for a sequence of games.

Our sufficiency results. In this section, we state the main results of this paper, which characterize several fundamental properties of pure-strategy equilibria in Bayesian games, such as existence, purification from behavioral strategies, and convergence for a sequence of games, by the condition of coarser inter-player information.

Shapley (), 34 Koutsougeras and N. Yannelis (), “Convergence and Approximation Results for Non—Cooperative Bayesian Games: Learning. Summary "A Bayesian Theory of Games" introduces a new game theoretic equilibrium concept: Bayesian equilibrium by iterative conjectures (BEIC).

The new equilibrium concept achieves consistencies in results among different types of games that current games theory at times fails to. Dynamic Non-Cooperative Game Theory.

called the Bayesian Stackelberg Markov Games (BSMGs), that can model uncertainty over attacker types and the nuances of an MTD system and (2) a Bayesian. Political Economy Series # 60 Convergence and Approximation Results for Non-Cooperative Bayesian Games: Learning Theorems THE LIBRARY OF THE MAR 1 Rafael Gely.

These convergence and existence results also extend to some classes of games with discontinuous payoffs, such as first-price auctions, where bidders may be heterogeneous and reserve prices are.Abstract.

We provide random equilibrium existence theorems for non-cooperative random games with a countable number of players. Our results yield as corollaries generalized random versions of the ordinary equilibrium existence result of J. Nash [].Moreover, they can be used to obtain equilibrium existence results for games with incomplete information, and in particular Bayesian games.Consider an infinitely repeated normal form game where each player is characterized by a “type” which may be unknown to the other players of the game.

Impose only two conditions on the behavior of the players. First, impose the Savage () axioms; i.e., each player has some beliefs about the evolution of the game and maximizes its expected payoffs at each date given those beliefs.