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Brownian Models of Performance and Control


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Table of Contents

1. Brownian motion; 2. Stochastic storage models; 3. Further analysis of Brownian motion; 4. Stochastic calculus; 5. Optimally stopping a Brownian motion; 6. Reflected Brownian motion; 7. Optimal control of Brownian motion; 8. Brownian models of dynamic inference; 9. Further examples; Appendix A. Stochastic processes; Appendix B. Real analysis.

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Michael Harrison returns to an important topic in stochastic process theory, and illustrates its many influential applications in business and economics.

About the Author

J. Michael Harrison has developed and analyzed stochastic models in several different domains related to business, including mathematical finance and processing network theory. His current research is focused on dynamic models of resource sharing, and on the application of stochastic control theory in economics and operations. Professor Harrison has been honored by the Institute for Operations Research and Management Science (INFORMS) with its Expository Writing Award (1998), the Lanchester Prize for best research publication (2001), and the John von Neumann Theory Prize (2004); he was elected to the National Academy of Engineering in 2008. He is a fellow of INFORMS and of the Institute for Mathematical Statistics.

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