 Information-theoretic accounts of measurement are based on an analogy between measuring systems and communication systems. In a simple communication system, a message (input) is encoded into a signal at the transmitter’s end, sent to the receiver’s end, and then decoded back (output). The accuracy of the transmission depends on features of the communication system as well as on features of the environment, i.e., the level of background noise. Similarly, measuring instruments can be thought of as “information machines” (Finkelstein 1977) that interact with an object in a given state (input), encode that state into an internal signal, and convert that signal into a reading (output). The accuracy of a measurement similarly depends on the instrument as well as on the level of noise in its environment. Conceived as a special sort of information transmission, measurement becomes analyzable in terms of the conceptual apparatus of information theory (Hartley 1928; Shannon 1948; Shannon and Weaver 1949). For example, the information that reading \(y_i\) conveys about the occurrence of a state \(x_k\) of the object can be quantified as \(\log \left[\frac{p(x_k \mid y_i)}{p(x_k)}\right]\), namely as a function of the decrease of uncertainty about the object’s state (Finkelstein 1975: 222; for alternative formulations see Brillouin 1962: Ch. 15; Kirpatovskii 1974; and Mari 1999: 185).