| Instructors for courses in decision and risk analysis | |||
|---|---|---|---|
| S. Browne | F. Chen | A. Federgruen | P. Glasserman |
| L. Green | P. Kolesar | D. Lehmann | S. Masri |
The development of models for security pricing, portfolio analysis and risk management. Particular attention is given to computer-based models for option pricing and hedging; mean-variance analysis; multiperiod portfolio optimization; analysis of the term structure and interest ratesensitive securities, including swaps, swaptions and mortgage-backed securities. Techniques used include binomial methods, Monte Carlo simulation, linear and quadratic programming and regression. Models are implemented and tested in spreadsheets or specialized software.
Primary emphasis is on the construction of explanatory probabilistic models. Preliminary coverage of some probability theory; moments and cumulants; moment generating functions; major probability distributions and their interrelationships; methods of estimation, including maximum likelihood and the method of moments; and tests of goodness of fit.
A comprehensive introductory survey of multivariate methods. Matrix algebra, simple and multiple regression, data reduction, factor analysis, discriminant analysis, multidimensional scaling, experimental design and analysis of variance.
This course is about the art and science of creating agreements between two or more parties. Students discuss and apply concepts developed in behavioral science and game theory as guides to improved negotiating. Students will develop and sharpen negotiating skills by negotiating with other students in real-world cases. This course offers a refreshing perspective on both competition and cooperation.
Students wishing to enroll in the course should register for Human Resource Management B8412-06, Managerial Negotiations, in the fall or B8412-07 in the spring. These sections are different from the other sections of B8412 in that instructors place greater emphasis on game-theoretical foundations of the negotiation process.
The solution of management problems using mathematical modeling. Emphasis is placed on the application of the models through the use of case studies. Topics include advanced optimization models, Markov processes, queuing models, dynamic programming and simulation. Applications are selected from production management, inventory control, finance, corporate strategic planning, facility layout and design and other management areas.
Special topics in the area of management science. Recent topics have included analytical models in finance, discrete event simulation, distribution models, multi-echelon inventory management, planning models for portfolio management and securities pricing and Brownian and fluid models of manufacturing systems.
The first in a two-course sequence in probability and statistics. Topics include basic probability theory, general characteristics of random variables, particularly probability distributions that are frequently used in statistics, and elementary random (stochastic) processes. The intent is to develop in the student an intuitive feel for the subject of probability theory and enable him or her to think probabilistically.
A rigorous exposition of the fundamentals of mathematical statistics. In particular, estimation theory and hypothesis testing are covered via the likelihood principle. A thorough introduction to linear models follows, with emphasis on regression and analysis of variance.
Topics include stochastic models in business research, covering an introduction to and analysis of stochastic models used in allied business fields, such as marketing, management, economics and finance, and advanced statistical modeling and analysis for business research. Emphasis on independent study.
Mathematical optimization is essential for modeling rational behavior, competition and decision making. It is therefore used extensively as a theoretical tool in a variety of fields, including economics, finance, marketing, engineering, operations management and management science. This course is intended to provide PhD students in these fields with a firm foundation in the theory of mathematical optimization. Both static and dynamic optimization (optimal control) are covered. Topics include unconstrained and constrained nonlinear programming, Lagrange multiplier theory, duality, convex analysis, min-max theorems, the Euler conditions, the Pontryagin maximum principle and vector space optimization methods.
The following three courses, offered jointly by the Business School and the Department of Industrial Engineering and Operations Research of the School of Engineering and Applied Science, carry interdisciplinary 6000-level numbers. However, all three are taught at a level equivalent to Business 8000- and 9000-level courses.
Characterization and computation of optimal policies for dynamic inventory and production planning models with deterministic requirements.
Construction and analysis of mathematical models used in the design and analysis of inventory systems. Deterministic and stochastic demands and lead times. Optimality of (s, S) policies. Multiproduct and multi-echelon systems. Computational methods.
Models are studied for problems arising in the planning of the logistics of multi-echelon systems. Focus is on the following operational and strategic problems: facility location; vehicle routing; inventory allocation; and capacity expansion. Emphasis is on devices to integrate these areas. Case studies and applications.