 There are further signs of the rehabilitation of classical and logical probability, and in particular the principle of indifference and the principle of maximum entropy, by authors such as Paris and Vencovská (1997), Maher (2000, 2001), Bartha and Johns (2001), Novack (2010), White (2010), and Pettigrew (2016). Relevant here may also be advances in information theory and complexity theory. Information theory uses probabilities to define the information in a particular event, the degree of uncertainty in a random variable, and the mutual information between random variables (Shannon 1948, Shannon &amp; Weaver 1949). This theory has been developed extensively to give accounts of complexity, optimal data compression and encoding (Kolmogorov 1965, Li and Vitanyi 1997, Cover and Thomas 2006; see the entry on  information  for more details). It is applied across the sciences, from its natural home in computer science and communication theory, to physics and biology. Interpreting information in these areas goes hand-in-hand with interpreting the underlying probabilities: each concept of probability has a corresponding concept of information. For example, Scarantino (2015) offers an account of ‘natural information’ in biology that is compatible with either a logical interpretation of probability or objective Bayesian interpretation, while Kraemer (2015) offers one that rests on a finite frequency interpretation. 