In recent years progress has been made especially along the lines of interpreting the probabilities in decision-theoretic terms for a 'splitting' agent (see in particular Deutsch (1999) and Wallace (2003b, 2007)).[24] The most useful application of decoherence to Everett, however, seems to be in the context of the problem of the preferred basis.
For the moment, assume that we can interpret the probability over hypotheses as an expression of epistemic uncertainty.
To make expected utility track efficacy rather than auspiciousness, causal decision theory interprets the probability of a state if one performs an act as a type of causal probability rather than as a standard conditional probability.
How should decision theory interpret the probability of a state S if one performs an act A, that is, P(S if A)? Probability theory offers a handy suggestion.
Lewis interprets the probabilities involved as temporally indexed single-case chances.
On the other hand, it does interpret the probabilities over sample space as components of a support relation, and thereby as pertaining to the epistemic rather than the physical realm.
In the analysis of the results we interpret the estimated probability p k as the probability to find a player of type k in the population under observation.
The difference between the original and predicted SD value interprets the existence probability of the targets in a scene.
We also need to think hard about the kind of genomic information it is appropriate to share directly with patients and doctors, and how they can be helped to interpret the complex probabilities it might reveal.
