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Discrete–Time Stochastic Control and Dynamic Potential Games

SBM: DISCRETE-TIME STOCHASTIC CNTRL & DYNMC PTNTL GAMES

Gt Product Group

Binding Format

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B2B GT

CL Carton Count

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B2B & B2C

​There are several techniques to study noncooperative dynamic games, such as dynamic programming and the maximum principle (also called the Lagrange method). It turns out, however, that one way to characterize dynamic potential games requires to analyze inverse optimal control problems, and it is here where the Euler equation approach comes in because it is particularly well–suited to solve inverse problems. Despite the importance of dynamic potential games, there is no systematic study about them. This monograph is the first attempt to provide a systematic, self–contained presentation of stochastic dynamic potential games.

COLFR

POST SECONDARY FOUR YEAR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Editorial Board.
Title Page.
Copyright Page.
Dedication.
Preface.
Acknowledgements.
Contents.
Notation and Acronyms.
1: Introduction and Summary.
2: Direct Problem: The Euler Equation Approach.
3: The Inverse Optimal Control Problem.
4: Dynamic Games.
5: Conclusions and Suggestions for Future Research.
References.
Index.

Discrete–Time Stochastic Control and Dynamic Potential Games.css-1p8rvzy{position:absolute;padding-left:8px;}

Discrete–Time Stochastic Control and Dynamic Potential Games