Fall 2013 - 63875 - PA397 - Introduction to Empirical Methods for Policy Analysis
|Instructor(s):|| Hagquist, Ronald F.
|Day & Time:||T 6:00 pm -9:00 pm|
|Waitlist Information:||For LBJ Students: UT Waitlist Information|
|Final Exam Information:||December 11, 2013 - 9:00am - 12:00pm SRH 3.216/219|
This course helps students develop an understanding of how basic quantitative tools are used in policy analysis. The major concepts discussed include modeling, optimization, sensitivity analysis, statistical inference, estimation, and prediction. These concepts are covered in the context of applications such as constrained decisionmaking based on calculus and on linear programming; policy choices with probabilistic information; evaluating and updating information with Bayesian techniques; estimating the impact of policy factors using regression models; and practical methods for forecasting. As the first course in the quantitative sequence, the emphasis is on broad exposure of techniques and appreciation of their contributions as well as their limitations in policymaking. Students must have fulfilled prerequisites in college-level algebra, calculus, and statistics before enrolling in this course. It is usually taken during the fall semester of the first year.
Decisions are generally about selecting among alternatives, allocating limited funds, or determining strategies to minimize costs. Data, particularly when voluminous as in data bases, are necessary but often not sufficient for making such decisions. Some sort of structured process must be used to “make sense” of the information, both to assure that the best decision is made and to provide transparency for the stakeholders bearing the consequences of that decision. In what is likely to be a long epoch of austerity in terms of public-sector funding, these two issues have become enormously important.
Fortunately, over the last half-century, the field of management science has been devoted to developing such structured decision-making processes. The primary organization of which is the Institute for Operations Research and the Management Sciences (INFORMS), which succinctly defines their discipline as “the science of better.” The recent product survey by its trade publication, OR/MS Today, shows 35 decision-support applications on the market which use one or more MS techniques (as opposed to many “decision support” applications which are simple data bases with graphics). This course is designed to provide the capacity to recognize decision problems in the public sector which are amenable to established analytics, operations research, or management science methods.
This section of 397C will be taught by Ronald F. Hagquist.