東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Optimization and games for controllable Markov chains = numerical methods with application to finance and engineering /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization and games for controllable Markov chains/ by Julio B. Clempner, Alexander Poznyak.
Reminder of title:	numerical methods with application to finance and engineering /
Author:	Clempner, Julio B.
other author:	Poznyak, Alexander.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xviii, 332 p. :ill., digital ;24 cm.
[NT 15003449]:	Controllable Markov Chains -- Multiobjective Control -- Partially Observable Markov Chains -- Continuous-Time Markov Chains -- Nash and Stackelberg Equilibrium -- Best-Reply Strategies in Repeated Games -- Mechanism design -- Joint Observer and Mechanism Design -- Bargaining Games or How to Negotiate -- Multi-Traffic Signal-Control Synchronization -- Non-cooperative bargaining with unsophisticated agents -- Transfer Pricing as Bargaining -- Index.
Contained By:	Springer Nature eBook
Subject:	Markov processes. -
Online resource:	https://doi.org/10.1007/978-3-031-43575-1
ISBN:	9783031435751

Optimization and games for controllable Markov chains = numerical methods with application to finance and engineering /
Clempner, Julio B.

Optimization and games for controllable Markov chainsnumerical methods with application to finance and engineering /[electronic resource] :by Julio B. Clempner, Alexander Poznyak. - Cham :Springer Nature Switzerland :2024. - xviii, 332 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v. 5042198-4190 ;. - Studies in systems, decision and control ;v. 504..

Controllable Markov Chains -- Multiobjective Control -- Partially Observable Markov Chains -- Continuous-Time Markov Chains -- Nash and Stackelberg Equilibrium -- Best-Reply Strategies in Repeated Games -- Mechanism design -- Joint Observer and Mechanism Design -- Bargaining Games or How to Negotiate -- Multi-Traffic Signal-Control Synchronization -- Non-cooperative bargaining with unsophisticated agents -- Transfer Pricing as Bargaining -- Index.

This book considers a class of ergodic finite controllable Markov's chains. The main idea behind the method, described in this book, is to develop the original discrete optimization problems (or game models) in the space of randomized formulations, where the variables stand in for the distributions (mixed strategies or preferences) of the original discrete (pure) strategies in the use. The following suppositions are made: a finite state space, a limited action space, continuity of the probabilities and rewards associated with the actions, and a necessity for accessibility. These hypotheses lead to the existence of an optimal policy. The best course of action is always stationary. It is either simple (i.e., nonrandomized stationary) or composed of two nonrandomized policies, which is equivalent to randomly selecting one of two simple policies throughout each epoch by tossing a biased coin. As a bonus, the optimization procedure just has to repeatedly solve the time-average dynamic programming equation, making it theoretically feasible to choose the optimum course of action under the global restriction. In the ergodic cases the state distributions, generated by the corresponding transition equations, exponentially quickly converge to their stationary (final) values. This makes it possible to employ all widely used optimization methods (such as Gradient-like procedures, Extra-proximal method, Lagrange's multipliers, Tikhonov's regularization), including the related numerical techniques. In the book we tackle different problems and theoretical Markov models like controllable and ergodic Markov chains, multi-objective Pareto front solutions, partially observable Markov chains, continuous-time Markov chains, Nash equilibrium and Stackelberg equilibrium, Lyapunov-like function in Markov chains, Best-reply strategy, Bayesian incentive-compatible mechanisms, Bayesian Partially Observable Markov Games, bargaining solutions for Nash and Kalai-Smorodinsky formulations, multi-traffic signal-control synchronization problem, Rubinstein's non-cooperative bargaining solutions, the transfer pricing problem as bargaining.

ISBN: 9783031435751

Standard No.: 10.1007/978-3-031-43575-1doiSubjects--Topical Terms:

532104
Markov processes.

LC Class. No.: QA274.7

Dewey Class. No.: 519.233

Optimization and games for controllable Markov chains = numerical methods with application to finance and engineering /
LDR:03716nmm a2200337 a 4500 001 2368047
003 DE-He213
005 20231213084441.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031435751 $q (electronic bk.)
020 $a 9783031435744 $q (paper)
024 7 $a 10.1007/978-3-031-43575-1 $2 doi
035 $a 978-3-031-43575-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA274.7
072 7 $a TBJ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a TBJ $2 thema
082 0 4 $a 519.233 $2 23
090 $a QA274.7 $b .C626 2024
100 1 $a Clempner, Julio B. $3 3296504
245 1 0 $a Optimization and games for controllable Markov chains $h [electronic resource] : $b numerical methods with application to finance and engineering / $c by Julio B. Clempner, Alexander Poznyak.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xviii, 332 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4190 ; $v v. 504
505 0 $a Controllable Markov Chains -- Multiobjective Control -- Partially Observable Markov Chains -- Continuous-Time Markov Chains -- Nash and Stackelberg Equilibrium -- Best-Reply Strategies in Repeated Games -- Mechanism design -- Joint Observer and Mechanism Design -- Bargaining Games or How to Negotiate -- Multi-Traffic Signal-Control Synchronization -- Non-cooperative bargaining with unsophisticated agents -- Transfer Pricing as Bargaining -- Index.
520 $a This book considers a class of ergodic finite controllable Markov's chains. The main idea behind the method, described in this book, is to develop the original discrete optimization problems (or game models) in the space of randomized formulations, where the variables stand in for the distributions (mixed strategies or preferences) of the original discrete (pure) strategies in the use. The following suppositions are made: a finite state space, a limited action space, continuity of the probabilities and rewards associated with the actions, and a necessity for accessibility. These hypotheses lead to the existence of an optimal policy. The best course of action is always stationary. It is either simple (i.e., nonrandomized stationary) or composed of two nonrandomized policies, which is equivalent to randomly selecting one of two simple policies throughout each epoch by tossing a biased coin. As a bonus, the optimization procedure just has to repeatedly solve the time-average dynamic programming equation, making it theoretically feasible to choose the optimum course of action under the global restriction. In the ergodic cases the state distributions, generated by the corresponding transition equations, exponentially quickly converge to their stationary (final) values. This makes it possible to employ all widely used optimization methods (such as Gradient-like procedures, Extra-proximal method, Lagrange's multipliers, Tikhonov's regularization), including the related numerical techniques. In the book we tackle different problems and theoretical Markov models like controllable and ergodic Markov chains, multi-objective Pareto front solutions, partially observable Markov chains, continuous-time Markov chains, Nash equilibrium and Stackelberg equilibrium, Lyapunov-like function in Markov chains, Best-reply strategy, Bayesian incentive-compatible mechanisms, Bayesian Partially Observable Markov Games, bargaining solutions for Nash and Kalai-Smorodinsky formulations, multi-traffic signal-control synchronization problem, Rubinstein's non-cooperative bargaining solutions, the transfer pricing problem as bargaining.
650 0 $a Markov processes. $3 532104
650 1 4 $a Mathematical and Computational Engineering Applications. $3 3592737
650 2 4 $a Applied Dynamical Systems. $3 3538870
650 2 4 $a Engineering Mathematics. $3 3301900
700 1 $a Poznyak, Alexander. $3 2059551
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in systems, decision and control ; $v v. 504. $3 3711661
856 4 0 $u https://doi.org/10.1007/978-3-031-43575-1
950 $a Engineering (SpringerNature-11647)