A Computational Theory of Subjective Probability

In this article we demonstrate how algorithmic probability the-ory is applied to situations that involve uncertainty. When peo-ple 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 proba-bility. In Experiment 2 we examine the conjunction fallacy and demonstrate that the materials used by Tverksy and Kahne-man (1983) involve model uncertainty. We then provide a for-mal 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