Spring 2010 Course Description

Advanced Empirical Methods for Policy Analysis

Section Title: Quantitative Methds for Managment
Instructor(s): Kenneth Matwiczak
Course: P A 397C - Advanced Empirical Methods for Policy Analysis
(previously Applied Quantitative Analysis II)
Unique Number: 62731
Day & Time: Mondays, 2:00 PM - 5:00 PM
Room: SRH 3.355
Waitlist Information:For LBJ Students: UT Waitlist Information

This course fulfills requirements for the following specialization(s):

Description: In an effort to broaden and expand the student’s “toolkit” of decision-making and management aids, this “topical” course covers additional decision analysis and modeling methods not addressed in IQA. It is intended for students who are interested in quantitative methods applied to management and administration. Topics covered in this course include:
(1) Optimization:
(a)Linear Programming: Expanding on Sensitivity and Post-Optimality Analysis
(b)Integer Programming: Solving linear programs in which solutions are constrained to be integer values, such as problems involving personnel assignments, etc.
(c)Goal Programming: Optimization problems in which we would like to achieve multiple objectives. Problems of this nature might include maximizing profit, while minimizing the total project budget, and meeting all time deadlines.
(d)Network Analysis: The application of linear programming and other algorithms to the optimization of networks, as in transportation, communication, utilities, and project schedules.
(2) Decision Analysis:
(a)Utility Theory: How to incorporate/assign value and assess risk associated with qualitative decision criteria.
(b)Multi-Criteria Decision Models: Modeling and analyzing decisions based on several quantitative and/or qualitative criteria, such as contract bidder evaluation /selection, personnel selection, etc.
(c)Game Theory: Two-party, competing decision strategies.
(3) Queuing Theory: An introduction to the mathematics of waiting lines.
(4) Inventory Theory: How much stock to keep on hand? How much to order?
(5) Simulation: A means for gathering information about and studying complex processes or systems, that cannot be modeled mathematically or that have no analytical solution. Monte Carlo Methods can be used to generate stochastic inputs for processes and decisions involving uncertainty.

The course is an “applied” course in the use of the management tools described above, and in the analysis and interpretation of the results. You will make extensive use of computer spreadsheets throughout the course. There will be several homework problem sets assigned, as well as a semester “project” which will require an oral presentation at the end of the semester. A mid-term exam and a final exam are included in the course.

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