 Where \(\sigma\) is a piece of strongly semantic (hence true) information, and \(z\) is the target situation that it describes with total accuracy, the more distant \(\sigma\) gets from \(z\), the larger the number of situations to which it applies and the lower its degree of informativeness. Floridi uses ‘\(\Theta\)’ to refer to the distance between a true \(\sigma\) and \(z\) (recall that Floridi is not interested in non-factive information, and might well deny that there is any sensible such commodity). \(\Theta\) indicates the degree of support offered by \(z\) to \(\sigma\). Given a specific \(\sigma\) and a corresponding target \(z\), Floridi maps the values of \(\Theta\) onto the x-axis of a Cartesian diagram. We now need a formula to calculate the degree of informativeness \(\iota\) of \(\sigma\)—\(\iota(\sigma)\)—in relation to \(\Theta(\sigma)\). Floridi’s proposal is that we calculate the value of \(\iota(\sigma)\) via the complement of the distance of \(\Theta(\sigma)\) squared: