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Category Archives: Prediction Markets
Prediction Market System Architecture
Recently I was working with a prediction market system. In a prediction market, the goal is to predict something such as the winner of a political election with three candidates. You get a group of experts who buy and sell … Continue reading
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Prediction Market Cost of Transaction
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
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Prediction Market Liquidity Constant
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
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Safe Calculation of ln(exp(a) + exp(b))
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
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Graphing a Prediction Market Cost Function
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
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Prediction Market Probabilities
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
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Initializing a Prediction Market
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
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Cross Entropy Error and Logarithmic Scoring Rule
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
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The Max Trick when Computing Softmax
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
Posted in Machine Learning, Prediction Markets
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