A Computational Theory of Subjective Probability
  In this article we demonstrate how algorithmic probability theory is applied
to situations that involve uncertainty. When people are unsure of their model
of reality, then the outcome they observe will cause them to update their
beliefs. We argue that classical probability cannot be applied in such cases,
and that subjective probability must instead be used. In Experiment 1 we show
that, when judging the probability of lottery number sequences, people apply
subjective rather than classical probability. In Experiment 2 we examine the
conjunction fallacy and demonstrate that the materials used by Tversky and
Kahneman (1983) involve model uncertainty. We then provide a formal
mathematical proof that, for every uncertain model, there exists a conjunction
of outcomes which is more subjectively probable than either of its constituents
in isolation.
