Tags: academic, political
A popular method for learning from large data sets is Random Forests (see my class on the topic, in Spanish). I would like to drive a paralellism between the way they work and our political decision structures and the so called Wisdom of the crowd.
Random Forests are what is called an ensemble method as they perform better than individual methods by combining their results. The individual method used in Random Forests are Decision Trees, trained from a subset of all the available data (and because of this property of operating on subsets of the data, they are a good method for applying on large datasets).
More interestingly, Random Forests (as discussed in the Machine Learning article by Leo Breiman in 2001), can not only train each of their trees on a subset of the data but also use a subset of the available information (features) when training each decision node in the tree. That makes each of the trees that are part of the ensemble truly random! When creating each individual tree we only see a subset of the data and only a subset of its characteristics. To decide the outcome of the decision, each of these random trees is given a vote. The most voted decision wins.
Now, the "magical" part is that they perform better than a decision tree trained on all available data. Even if the tree were made "smart" by prunning poorly constructed branches (the trees that make the ensemble are unpruned). And they are so high performant that a recent comparative study of 179 different classifiers found them to be consistently top performing across a large set of problems.
Now, if you think for a second, this is the way direct democracy works: each voter has access to a subset of the information and only sees that subset from a particular perspective (their own unique perspective). By using a majority vote, we are actually implementing a Random Forest. And from the theory (Breiman paper is quite delightful) we can see that we don't need more informed voters, just more of them. Food for thought.