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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 Tverksy 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
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