Shannon information... [which] he also called entropy or ... involves things (...Shannon ...messages) and probabilities for those things. - Julian Barbour

The concepts of message and probability enable one, for a definite source of <math>N</math> messages, to define Shannon’s information. If <math>p_i,\quad i = 1, 2, ..., N</math>, is the relative probability of message <math>i</math> and <math>\log p_i</math> is its base-2 logarithm, then the information <math>I</math> of the given source is(1) <math>\quad I = - \sum_{k=1}^N p_i \log p_i</math>.The minus sign makes <math>I</math> positive because all probabilities, which are necessarily greater than or equal zero, are less than unity (their sum being<math>\textstyle \sum_{i}^N p_i = 1</math>, so that their logarithms are all negative.

If we are to speak about ontology, as opposed to efficient coding in communication channels, the most important symbol in (1) is not <math>p</math> for probability but <math>i</math> for the thing, or configuration, that has the probability <math>p_i</math>.

There is nothing in between... [NOWs]. Each are separate snapshots. ...These [real photographs] ...are not changed by ...reversing the order ...It may be convenient for ...for the way we think about the world and for ordering our experiences, to suppose that these come in a certain order; but ...the picture is not changed... the snapshot is... self-contained.