“Shannon’s ‘mathematical theory’ sets out two big ideas. The first is that information is

probabilistic. We should begin by grasping that information is a measure of the uncertainty we overcome, Shannon said – which we might also call surprise. What determines this uncertainty is not just the size of the symbol vocabulary, as Nyquist and Hartley thought. It’s also about the odds that any given symbol will be chosen. Take the example of a coin-toss, the simplest thing Shannon could come up with as a ‘source’ of information. A fair coin carries two choices with equal odds; we could say that such a coin, or any ‘device with two stable positions’, stores onebinary digitof information. Or, using an abbreviation suggested by one of Shannon’s co-workers, we could say that it stores onebit.But the crucial step came next. Shannon pointed out that most of our messages are not like fair coins. They are like

weightedcoins. A biased coin carrieslessthan one bit of information, because the result of any flip islesssurprising. Shannon illustrated the point with this graph. You see that the amount of information conveyed by our coin flip (on the y-axis) reaches its apex when the odds are 50-50, represented as 0.5 on the x-axis; but as the outcome grows more predictable in either direction depending on the size of the bias, the information carried by the coin steadily declines.—Rob Goodman & Jimmy Soni. “The Bit Bomb.”

Aeon. August 30, 2017.

Interesting thoughout.