AI Series: A Performance Measure

As mentioned previously, I'm planning on following along with Stanford's Introduction to Artificial Intelligence class this coming semester. I've just received the book, Artificial Intelligence A Modern Approach and am starting to work my way through it.

As I go through the book, I'll be thinking about what I would do if I were building my own program to analyze loans and writing up my analysis here.

Chapter 2 discusses the idea of rationality and how we determine whether an agent (program, in this case) has done well. Specifically we would create a performance measure. In the case of Peer to Peer lending, I think the following performance measure would be in order:

1. The total return from investing in a loan
2. Subtracting a small percentage of the investment for the time the money is invested but the loan as not yet started
3. Subtracting some amount for a loan that goes over 30 days late

The first rule speaks for itself. Since our goal is to maximize return we want our agent to choose to invest in loans which will give the most return on investment. Let's say that we are investing $100 in every loan and the total return from the loan is $110. We know that this loan has done well for us but it hasn't done as well as a loan that returns $115. And it has done much better than a loan that returns $40.

The second rule is a rule to encourage the agent to pick loans which are closest to closing. Given two loans that are exactly equal in every other way, we'd rather invest in the one that is two days from being funded than the one that is 10 days from being funded.

The third rule is more of a personal preference. Even if the agent were able to pick out borrowers who would pay over 30 days late and still end up paying off all of the loan value (and perhaps even more, with penalties) I don't want these loans. They would drive me crazy every time I would look at my portfolio. So rule three biases these loans downwards to make the agent value these loans less.