Randomness is contextual. Two popular contexts for which this works are (a) I am attempting to predict an event and cannot do so deterministically using inform

Randomness is contextual. Two popular contexts for which this works are (a) I am attempting to predict an event and cannot do so deterministically using information I and resources R or (b) I am attempting to characterize a sequence of events in an efficient way. Thus, randomness is thus always determined against a resource constraint. For instance, the digits of pi are random in the context where you aren't aware that the digits are arising from a deterministic process and don't have the resources to discover this. Another interesting example concerns the randomness of a PRNG. Given knowledge of the algorithm and the hidden state it is obvious that this is deterministically predictable, but eliminating the hidden state information destroys the predictability of the PRNG events. For the sequence of events modality, efficiency is important since we've lost the context of discovery/prediction. For instance, any finite sequence can obviously be perfectly described by itself, but we're often interested in compression. One can imagine an optimization problem where we want to achieve a representation of the sequence which has the simplest model and the lowest cumulative "error". Error and model can be defined in many ways, but oftentimes models have a "random" nature to them. It's an efficient shorthand in situations where the deterministic explanation for a sequence is difficult or impossible to describe.