uncertainty in the receiver that pre-existed the receipt of the message. |n the binary

uncertainty in the receiver that pre-existed the receipt of the message. |n the binary coding scheme of digital electronic operations, the unit of information is the bit, a choice made between 0 or 1 in the resolution of a two state ambiguity at each place of some power of two number of places. Our relatively common computers these days have 32 or 64 bit processors. If these 0,1 choices are made in a random sequence in which each step is independent of the previous one, the sequential probabilities, _, are multiplicative: e.g. the probability of getting two 1’s (heads in a fair coin) in a row are the product of each 0.5 probability: p,;=0.5 x p2=0.5 = P1 P2 = 0.25. Using the common base ten system of logarithms to demonstrate the algebraic fact that multiplicative probabilities are logarithmically additive (and ignoring the minus sign that comes with making logarithms of the decimal fractions of probability), we notice that /og70(0.5) = 0.693147 and /og70(0.25) = 1.386294 and that 0.693147 + 0.693147 = 1.386294. The dot-dash choices of Morse code machines, the go, no-go gates of transistors, the open versus closed ion channel-mediated neuronal membrane discharge and the left, right spins of the single electrons of today’s quantum computers lead naturally to an information encoding of multiplicative sequences as the sum of logarithms in base (equal to the number of available states) two, each p= 0.5 choice called, /og2(0.5) = 1, a bit. Shannon’s 1938 master’s thesis mapped George Boole’s algebraic scheme for doing yes-no, either-or computation onto current switching devices such that circuit closed was “true” and circuit open was “false.” Using Boole’s laws such as “Not(A and B)” always equals “(Not A) or (Not B)” led to schemes for circuit routing through electronic gates which also serve for information storage in gadgets ranging from cell phone directories to computer hard disks. Following Claude Shannon, each logarithmically additive entropy term is expressed as the sums, ~%, of its probability, p,, times the probability’s logarithm, =.(p.x /ogz) (p,in base two. A logarithm is an exponent of its relevant base such that, for example, the logarithm, base two, of 2 x 2 x 2, 2° = 3 and 3 bits can encode eight binary (0,1) numbers: (000, 001, 010,011,100,101,110, and 111). Shannon used a hill-like, called convex, entropy function S (p)= -X(p In (p)). The amount of 77 HOUSE_OVERSIGHT_013577