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More About This Textbook
Overview
Most of the current books on stochastic control theory are written for students in mathematics or finance. This introduction is designed, however, for those interested in the relevance and applications of the theory's mathematical principles to economics. Therefore, mathematical methods are discussed intuitively and illustrated with economic examples. More importantly, mathematical concepts are introduced in language and terminology familiar to graduate students in economics.
Editorial Reviews
From the Publisher
"Chang discusses various solution techniques, including the inverse optimum methodology." - University of Chicago Magazine"The manner of fixing the dialectical relation between information set (a common term to the economists) and $\sigma $ algebra (a theoretical mathematical concept) is remarkable.... The book is well written and should prove useful in graduate courses for economists and also in courses for other professionals who are willing to go into the mathematics of economic models." - Zentralblatt MATH
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Meet the Author
Professor Chang received his BS from National Taiwan University, holds a PhD in Economics from the University of Chicago and a PhD in Mathematics from State University of New York at Stony Brook. His graduate-level courses include price theory sequence and mathematical economics (stochastic control theory and applications, economics of uncertainty). He has been an invited visiting scholar to the Center for Economic Studies (CES) of the University of Munich, Germany, and the Economic Research Center (ERC) of Nagoya Univeristy, Japan. He is also a recipient of the 1986 Outstanding Junior Faculty Awards of Indiana University, a recipient of the 2004 IU Trustees Teaching Awards, and a research fellow of the CESifo Research Network. Professor Chang has published papers in prestigious journals in economics and mathematics, including Econometrica, the Review of Economic Studies, the Journal of Economic Theory, the Proceedings of American Mathematical Society, and the Journal of Optimization Theory and Applications. In 2004 he published Stochastic Optimization in Continuous Time with Cambridge University Press.
Table of Contents
List of figures; Preface; 1. Probability theory; 2. Wiener processes; 3. Stochastic calculus; 4. Stochastic dynamic programming; 5. How to solve it; 6. Boundaries and absorbing barriers; Appendix. Miscellaneous applications and exercises; Bibliography; Index.