Stochastic
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The JC’s amateur guide to systems theory™
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Stochastic
/stəˈkæstɪk/ (adj.)
Of an event, to have a random distribution or pattern to which one may assign a probability but may not predict precisely. To be compared with deterministic (where one can predict the event precisely) and uncertain where one cannot even assign a probability.
The outcome of rolling dice is stochastic: there are 36 possible outcomes, each of them is equally likely (1/36), but it is impossible to determine which will happen next time you roll the dice. Nonetheless, you know the probability of a given outcome is 0.02777 recurring.
The price of a stock market index at a point in the future is sort of stochastic, in that it must be a number and that number must be between zero and the total value of all issued currency in the world, but beyond that it is not possible to accurately assign a probability, as David “25-Sigma-Several Days-In-A-Row” Viniar would — probably? — tell you. So it isn’t really stochastic. To qualify as a random distribution, events must be independent, each value must have a non-negative value, and the value of all events must add to 1. There must be a finite possible number of events.
What Elon Musk is going to say on Twitter tomorrow is uncertain. There is no way of predicting it, or even assigning a probability to it. It is not even random.