 Now, it turns out that the absolute value score cannot ground an accuracy argument for Probabilism. In fact, there are situations in which non-probabilistic credence functions accuracy dominate probabilistic credence functions when inaccuracy is measured using the absolute value score. Let \(\mathcal{F} = \{X_1, X_2, X_3\}\), where \(X_1\), \(X_2\), and \(X_3\) are mutually exclusive and exhaustive propositions. And consider the following two credence functions: \(c(X_i) = \frac{1}{3}\) for each \(i = 1, 2, 3\); \(c'(X_i) = 0\) for each \(i = 1, 2, 3\). The former, \(c\), is probabilistic; the latter, \(c'\), is not. But, if we measure inaccuracy using the absolute score, the inaccuracy of \(c\) at each of the three possible worlds is \(\frac{4}{3}\), whereas the inaccuracy of \(c'\) at each of the three possible worlds is \(1\). The upshot of this observation is that it is crucial, if our accuracy argument for Probabilism is to succeed, to rule out the absolute value score. The problem with the Leitgeb and Pettigrew characterization is that it rules out this measure essentially by fiat. It rules it out by demanding that the inaccuracy of a credence function at a world supervenes on the Euclidean distance between the credence function and the omniscient credence function at that world. But it gives no reason for favouring this measure of distance over another, such as Manhattan distance.