Information

Information is a difference that makes a difference. Gregory Bateson’s formulation captures what Shannon’s mathematical theory formalized: information is what reduces uncertainty. A coin flip carries one bit of information because it resolves one binary question. A message that tells you nothing you didn’t already know carries zero information, no matter how many words it uses.

Shannon (1948) separated information from meaning. In his framework, information is a measure of surprise — the less probable a message, the more information it carries. This lets you quantify information without knowing what it’s about, which is why the same theory works for telegraph cables, DNA, and hard drives. The unit is the bit: the amount of information needed to distinguish between two equally likely outcomes.

But information is not data. Data is the raw material — the signal, the measurement, the record. Information is what you extract from data when you interpret it: the pattern recognized, the uncertainty resolved, the state of affairs inferred. The same data can yield different information depending on what question you bring to it. A list of temperatures is just numbers; read against a baseline, it becomes information about whether something is wrong.

Information requires a receiver capable of being in different states. A thing that cannot change cannot be informed. This is why Bateson’s definition works across domains — from thermostats to organisms to organizations, information is whatever distinction the system can register and respond to.