Category Archives: Prediction Markets

Suppose you want to predict which of three candidates will win a political election. One approach is to get a group of experts and allow them to buy and sell shares of each candidate, in a way similar to how … Continue reading →

Suppose you want to predict the outcome of a political election that has four candidates. One approach is to get a group of experts and allow them to buy and sell shares of the candidates in a way that’s similar … Continue reading →

I’ve been working with an interesting system called a prediction market. The goal of a prediction market is to predict an outcome such as the winner of a basketball tournament with 64 teams. You get a group of experts and … Continue reading →

The goal of a prediction market is to predict an outcome such as the winner of a political election. In a prediction market, a group of experts buys and sells shares in the options in a way similar to the … Continue reading →

Suppose you want to predict which of three soccer teams will win a tournament. In a prediction market, you get a bunch of experts and let them buy and sell shares of each team. The price of a team will … Continue reading →

Suppose you want to predict which of three basketball teams will win a tournament. In a prediction market, you get a bunch of experts and let them buy and sell shares of each team. The price of a team will … Continue reading →

I was working on a “computational economics” project recently. That project used something called the logarithmic scoring rule. I noticed that the logarithmic scoring rule is essentially the negative of something called cross entropy error, which is often used with … Continue reading →

The softmax function appears in many machine learning algorithms, in particular neural networks and prediction markets. The idea is, if you have a set of values, to scale them so they sum to 1.0 and therefore can be interpreted as … Continue reading →