September 1, 2008 8:19 pm by Markus.

Recently I had a fun discussion with Bill over lunch about intellectual property and how that might apply to statistical modeling work. Given that there are more and more companies making a living from forming predictions with a model they have built (churn-prediction, credit-scores and other risk-models) we were wondering if there were any means of protecting them as intellectual property. For example, the ZETA-model for predicting corporate bankruptcies is a closely guarded secret with having published only the variables being used (Altman E. I. (2000); Predicting financial distress for companies: revisiting the Z-Score and ZETA models). Obviously this model is useful for lending and can make serious money for the user. Making decisions guided by a formula is becoming more popular. This might be something over which legal battles will be fought in the future.

Copyrighted works and patents often count towards what a company would be worth should somebody acquire it. This means there would be motivation for start-up companies to protect their models. A mathematical formula (e.g. a regression equation) cannot be patented, and copyright probably won’t apply either; even if copyright would apply, it’s trivial to build a formula that does essentially the same thing (e.g. multiply all the weights in the formula by 10). This leaves only trade secret protection and means there is no recourse once the cat is out of the bag. Often it’s also the data-collection method that is kept secret - a company called Epagogix developed a method to judge the success of movies from a script by scoring it against some scales that they keep secret.

Currently, I don’t see any legal protections with the exception of trade-secrets for this. And given that there is infinitely many ways to express the same scoring rules in a different way, this would be a fairly hard problem for lawyers and politicians to formulate sensible rules for establishing protection for this kind of intellectual property.

Posted in Society, Politics, Data Mining, Machine Learning | Print | 1 Comment »

November 15, 2006 9:15 pm by Markus.

I just read an interesting article about how society is usually described in science fiction. Turns out that in all circumstances it is about a very hierarchical, military like structure. There are no suggestions as to how a civilian society might work in the future. Consider things like Star Trek where a bunch of officers command a star ship around and the rest of the people just function. The captain is smart, benevolent and there is rarely an abuse of power. No democracy, no voting, little about how the civilian society of the future might function. There are things like Futarchy, but that’s pretty much all I could find in a quick search (and it wasn’t proposed in a SciFi-novel so it can’t be any good ). One of the problems with Democracy might be that people don’t always make the right decision as they don’t have access to all the information or are easily swayed by bad arguments (e.g. negative ads - some of them are just factually wrong). My point is that there haven’t been that many viable alternatives proposed, not even some crazy, outlandish suggestions (think teleportation for means of transport) to give people some new ways to think about this.

There is an interesting book out there called The Wisdom of Crowds. It proposes that large crowds of people can be capable of making decissions better than individuals. Long story short, according to the book four key qualities are necessary to make a crowd smart. The crowd needs to be diverse, so that people are bringing different pieces of information to the table. It must not have somebody at the top dictating the crowd’s answer, and summarize people’s opinions into one collective verdict. The people in the crowd need to be independent, so that they pay attention mostly to their own information, and not worrying about what everyone around them thinks (i.e. being immune to persuasion concepts like social proof).

Random Forests in machine learning are an ensemble method that has very good classification performance. The way it works is that hundreds of decision trees are build, but each on a different training set and with a different choice of features. If all the classifiers are strong (i.e. not able to make perfect predictions, but they tend to do the right thing - they take the information they have and make independent decisions) , then the overall vote of all the trees in the ensemble will tend to minimize the misclassification error. Breiman gave a mathematical proof of why this minimizes the classification error (i.e. bad decisions).

I wonder if something like this might work for political decision making. Leaving problems like corruption and other human fallacies (e.g. looking at what others are doing) aside for a moment and assuming that for all things there are good arguments to be made for and against a bill, a senators vote would depend on how he or she weights the particular arguments for and against the bill. If we assume that senators tend to vote for what they perceive to be the right thing, would giving each senator a random subset of information make the overall senate vote for the “right thing”? Another idea would be to make a political decision, similar to jury duty, by picking a large number of people from the general population at random and have them decide on a particular issue.
Edit:I found some criticism of the Wisdom-of-Crowds theory, such as Wikipedia not being accurate enough or a democracy electing people like Hitler. A good question in both cases would be if people made their decisions independently in these cases or not. I think that independent decisions are difficult to achieve in practice. Also one has to wonder how robust this system is due to the assumption that everybody makes the best decission they can.

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August 21, 2006 8:18 pm by Markus.

Here’s something strange I found:

Gates Foundation helps company buy news-papers - I wonder how that fits in with the whole charity thing.

See also: MediaNews Buying in California Boosted by Bill and Melinda Gates Foundation.

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