While Foresight has been heretofore described in the more abstract terms of its intrinsic value, it can technically be described as a user’s prediction performance expressed in terms of the platform-wide performance of all users. Mathematically, it should rely on a k-value that controls the maximum possible points earned or lost per prediction, as well as a constant that controls the rate of Foresight growth. Foresight will increase with correct predictions and decrease with incorrect predictions. As Foresight increases, the number of points gained or lost per prediction will decrease, making the metric more accurate over time.
While Hypothesis intends to be transparent about as much of its technology as possible, the processes describing Foresight are currently under development and cannot be made public. Relevant stakeholders may contact [email protected] for more information regarding Hypothesis algorithms.
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