Monday, August 10, 2009

Bounded Rationality piquing my interest

I was reading an article this weekend talking about the microfoundations of macroeconomic theories having series structural issues. The author maintained that uber-rational homo economics does not exist, and that people do not have perfect foresight, or even optimization skills. The author invoked bounded rationality. This led me to think about a book I haven't thought of in years, Max Bazerman's "Judgement in Managerial Decision Making." I recommend the book; it is very accessible. There are plenty of surveys and games that you can play with others to see just how limited our cognitive functions are, and it is, dare I say, fun.

Basically Bazerman theorizes that humans have two thinking states: the 1st is based on simple rules of thumb and allow you to make quick decisions. These rules are created by you either through your own experience or training. The second type of thinking is the rational version of deliberate debate, weighing the pros and cons of an action on several measures, such as: defining the problem, creating alternatives, weighing trade-offs. The reason humans have these two systems is that if we had to base every decision with the superior method of system two we would take a disproportionate amount of time choosing between different brands of Frosted Flakes.

Here is a quick example of what Bazerman terms an availability heuristic. You receive a newsletter in the mail and it states that the stock market will go up over the next 6 months. You ignore it. 6 months hence the market has gone up and you receive another letter that states that market will go down over the next half year. 6 months hence the market is down and you receive another letter stating that the market will go up. This time you consider the letter for awhile, maybe it sits on the coffee table for a week, but you still throw it away. 6 months go by and the market is up and you receive another letter stating that the market will go up again. This time you thoroughly read the letter and send your money into the broker who has been sending you the letter for two years. You never hear another word and lose 10,000 dollars. What happened?

Well imagine instead you are this deceptive broker. You have a list of 100,000 people. Every 6 months you send out the letter to everyone, except that only half get the market goes up letter. The other half get the market goes down letter. So in the first half only 50,000 are correct. Then 25,000. Then 12,500. Finally, the last letter 6,250. The broker gets 10,000 from each he walks away with 62,500,000 dollars. Not bad for two years work. This is also what people call survivorship bias, which happens in the mutual fund industry. Not that I am calling them crooks, but when you read stats like 90% of our funds have been up every year, well that is with the caveat that the poor performing funds are dead. Thus, no longer in the reported data.

Sunday, August 9, 2009

Correlation on draft day?

I was building a data set for an upcoming football draft. What I am going to attempt to do is set up a draft heuristic that tells you what position you can go the most from by drafting it at any particular moment in the draft. So if QB's are a hot commodity this year, it tells you what performance remains on the draft board and whether you will have better performance from drafting a QB or a running back in your draft slot. So its essence is to instead of taking the best player on the draft board, which analyst will tell you to do because you can trade (of course this analysis ignores how hard it is to trade and what happens if you end up with 5 RB's, which depending on your gift of gab may not be the ideal foundation for making trade propositions,) instead shows you relative outperformance for each subset of positions. So if you can get a +5 over the median/mean QB but you can get a +7 RB you would or should take the RB. The analysis will also be helpful in making trades to complete your team.

However, when investigating the data set I came upon an unsettling set of numbers. Here is the first set in graph form.

So what I was seeing was tiers of players. You can see distinctly the upper echelon of players, then a another, then another, setting up a power law. A regression puts the R-squared at 72%. So then I wondered about the format in which a draft is set up. The normal way in which I have participated is a serpentine format that goes from 1 to 12 and then 12 to 1. So I ran those numbers below.

It definitely shows a two-tiered system of haves and have-nots with the first 5 draft positions able to parlay the superstars outperformance versus the mitigating lower second round draft pick. This leads me to believe that auction drafts, as many claim, have more fair outcomes than the serpentine method. Something to consider as draft season approaches. Good luck out there.