This evening, I had a chance to go do some work with the Meaningful Science Consortium.  The consortium is a group out of a few Chicago-based Universities that advocates teaching more science in inner-city Chicago schools.  My task this afternoon was to grade some of the student projects, which were assigned in advance.  For the chemistry project, they were tasked with inventing some kind of board game to help other students learn about the periodic table. For the physics project, they were to design a roller coaster and calculate the requisite numbers (i.e. velocity, kinetic energy, etc).  The project that I didn’t grade but was the most excited about with the 9th grade biology project.

Their project was to design a school building in Florida, with certain guidelines (had to be three buildings, you have a predesigned plot of land, etc).  Their primary constraint, was to not kill off a specific tortoise species that existed on this plot of land.  The tortoise existed in a complex food network.  The student’s goal was to place the buildings in such a way as to maintain the ecosystem while still constructing a functional campus.  They had to develop an objective along with more “constraints” and “considerations”.  Further, there were some additional tasks which are now escaping me.  Basically, they couldn’t just drain the swamp and build the school.

My immediate response: Awesome! This is a computer science / operations research problem!  Essentially, the students were tasked with writing a linear program; maximize some objective function with respect to (binding) constraints. Even more interestingly, their constraint involved a graph! With node dependencies!  This is the kind of program that researchers who work in algorithm design or optimization struggle with every day — how can we design better algorithms and solution concepts for these problems? Even better, this is an interesting and applied problem that includes finding ways to protect the environment with human considerations.   Academics sometimes forget the reason that they consider these problems in the first place; focusing on making “the numbers work” and forget about the interesting ways this research can improve the human condition.

I hope stuff like this inspires a new generation of students interesting in questions of optimization, algorithm design, and similar topics.  We need more people thinking about these kinds of problems and developing new approaches.

I go through the analytics records for this website from time-to-time just to see how people get to my site or what search terms are popular.  Today, while going through, I found I’m getting hits for:

1- “How Tough is Mas-Colell”. Answer: Tough but feasible.  See my previous post on the standard microeconomics textbook for PhD students.

2- “Fantasy Football Linear Programming”. This one is interesting. I run a mailing list (that has no activity), where people can talk about fantasy football analysis techniques.  However, nobody on the mailing list has used linear programming (to the best of my knowledge). I wonder – are people using linear programming to determine a fantasy football roster? If so – awesome.

For those of you who don’t know – linear programming is a technique in optimization. In words, it is something like this: You have something that you need to maximize (or minimize), like profits for a company or points for your fantasy football team. You have a set of “constraints” – i.e. limitations on this maximization. Things like, for a company, you can only produce a limited quantity of some good or producing one good decreases the production of another good, and so on. I’m not sure how this would go back to fantasy football, though – like what would this constraints look like?

My best simple formulation of a linear program for fantasy football is something like this:

Maximize Potential Points!

Subject to:

• Limited number of positions (i.e. only 2 quarterbacks)
• Players are taken by other teams in the draft.
• Different Bye Weeks. (i.e. you don’t want all players with the same bye week, usually)
• Potential injuries and/or players with chronic injuries (I’m looking at you, Clinton Portis)

Perhaps there might be a way to develop some optimization technique that uses linear programming. The formulation of this program is feasible – but I’m not sure about:

• Computational limitations. The calculations for this optimization may be unreasonable – i.e. this may be an “NP Hard” problem.  I’m not sure about the complexity, yet.
• Points calculations. This is what I’ve been mostly working on the past two years – developing a good prediction algorithm that can account for variation in performance. I’m up to about 50% but this needs to be improved.