  Individual probabilities refer to the probabilities of outcomes that are
realized only once: the probability that it will rain tomorrow, the probability
that Alice will die within the next 12 months, the probability that Bob will be
arrested for a violent crime in the next 18 months, etc. Individual
probabilities are fundamentally unknowable. Nevertheless, we show that two
parties who agree on the data -- or on how to sample from a data distribution
-- cannot agree to disagree on how to model individual probabilities. This is
because any two models of individual probabilities that substantially disagree
can together be used to empirically falsify and improve at least one of the two
models. This can be efficiently iterated in a process of "reconciliation" that
results in models that both parties agree are superior to the models they
started with, and which themselves (almost) agree on the forecasts of
individual probabilities (almost) everywhere. We conclude that although
individual probabilities are unknowable, they are contestable via a
computationally and data efficient process that must lead to agreement. Thus we
cannot find ourselves in a situation in which we have two equally accurate and
unimprovable models that disagree substantially in their predictions --
providing an answer to what is sometimes called the predictive or model
multiplicity problem.
