Ensemble Predictors and Democracy

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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