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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.
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This is an introduction to stochastic control theory with applications to economics. Most of the existing books on this subject are written for students in mathematics or in finance. For those who are interested in the relevance and applications of this mathematical machinery to economics, there must be a thorough and concise resource for learning. This book is designed for that purpose. The mathematical methods are discussed intuitively and illustrated with economic examples. More importantly, the mathematical concepts are introduced in language and terminology familiar to graduate students in economics.About 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.
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