The 89^th Annual Meeting of the Texas Section of the MAA

April 16-18, 2009

3:00 - 8:00 – Registration at Holiday Inn – Holiday Inn Lobby Area

4:00 - 6:00 – Executive Committee Meeting – Holiday Inn Board Room

7:00 - 9:00 – Student Calculus Bowl – Chemistry Building, Room 109

7:00 - 9:00 – Short Course – Environmental Science Building, Room 190

Precalculus Course Redesign Using Technology
Phoebe Rouse, Louisiana State University

You may also take the Holiday Inn shuttle directly to the Gateway center. If you do, you will need to make arrangement at the hotel for transportation both ways.

7:30 - 11:00 – Refreshments – Ground Floor Gateway Conference Center

7:30 - 4:00 – Registration – Ground Floor Gateway Conference Center

8:00 - 10:15 – Student Contributed Presentations – Gateway Conference Center

Student Session I: Differential Equations	Room 41
Student Session II: Biomathematics, Probability	Room 42
Student Session III: Graph Theory, Combinatorics	Room 48
Student Session IV: Geometry, Topology	Room 51
Student Session V: Mathematics Education, Number Theory	Room 52
Student Session VI: Miscellaneous	Room 44

9:00 - 10:45 – Texas NExT Meetings – Room 53

Connecting High School and College Mathematics
Dan Teague, North Carolina School of Science and Math

Bringing Historic or Current Events in the Classroom
Jennifer McLoud-Mann, University of Texas at Tyler

9:00 - 3:30 – Exhibits/Book Sales – Ground Floor Gateway Conference Center

10:45 - 11:45 – American Mathematical Association of Two Year Colleges - Room 51

10:45 - 11:45 – TAAAMS Department Chairs’ Forum – Room 52

Implementing Course Redesign Using Technology
Phoebe Rouse, Louisiana State University

10:45 - 11:45 – Editorial and Advisory Board of the Texas College Mathematics Journal - Room 41

James Epperson
Chair, Advisory Board TXCMJ
University of Texas at Arlington

10:40 - 11:30 Student Forum #1 - Room 42

Intrinsic Value Methodology (IVAL): A mathematical model of the human decision-making process
Bud Simrin
Lockheed Martin Aeronautics (retired)

Abstract: When faced with the problem of selecting the best from a large number of alternatives, we often turn to heuristic techniques. For example, an aircraft company generates 1000 candidates for the next generation fighter aircraft. These are inexpensive, computer-generated designs. The company has budget to build 10 expensive, scaled-down models suitable for wind tunnel testing. During the computer-aided design phase, the company produces 100 design attributes for each of the 1000 candidates. This includes such things as cost, weight, speed, fuel capacity, range, max altitude, etc. The problem is to select the best 10 among the 1000 designs for building the wind-tunnel models.

It is not uncommon in complex decision-making processes such as the above that there is no physics-based formula for combining the attributes into an overall score. In fact, it can be impossible to develop such a formula because the attribute list may include intangibles, such as attractiveness of design, that have no basis in physics. Thus, the most commonly used approach to this type of problem is to assign weights to the attributes, normalized to sum to unity, and then to calculate an overall weighted average. Wind tunnel models will then be generated for the top 10 scores.

How can we measure the goodness of this type of evaluation? Intuitively it seems reasonable that the weighting factor methodology gives higher scores to the better designs, but does it really? In the absence of a truth model, how are we to know?

In this presentation we take a simplified case of the aircraft-design problem where we examine only 3 attributes for which there is a known truth model, based upon physics, which combines the attributes into an overall score. It is shown that the weighting-factor approach can provide substantially erroneous answers. The presentation goes on to introduce a heuristic mathematical model that mimics the human decision-making thought process and which generates excellent agreement with the truth model.

10:40 - 11:30 Student Forum #2 - Room 48

An Introduction to the Simplex Method
John Quintanilla
University of North Texas

Abstract: Lagrange multipliers are used to find extremal values of a function under equality constraints. But what should we do if the constraints are inequalities? This talk will introduce the simplex method, a technique for finding the extremal values of a linear objective under linear constraints. The simplex method is one of the techniques used in the field of operations research, which is concerned with optimal decision-making in deterministic and probabilistic systems under the need to allocate limited resources.