An approach to testing theories describing a multiverse, that has gainedinterest of late, involves comparing theory-generated probability distributionsover observables with their experimentally measured values. It is likely thatsuch distributions, were we indeed able to calculate them unambiguously, willassign low probabilities to any such experimental measurements. An alternativeto thereby rejecting these theories, is to conditionalize the distributionsinvolved by restricting attention to domains of the multiverse in which wemight arise. In order to elicit a crisp prediction, however, one needs to makea further assumption about how typical we are of the chosen domains. In thispaper, we investigate interactions between the spectra of available assumptionsregarding both conditionalization and typicality, and draw out the effects ofthese interactions in a concrete setting; namely, on predictions of the totalnumber of species that contribute significantly to dark matter. In particular,for each conditionalization scheme studied, we analyze how correlations betweendensities of different dark matter species affect the prediction, and explicatethe effects of assumptions regarding typicality. We find that the effects ofcorrelations can depend on the conditionalization scheme, and that in each caseatypicality can significantly change the prediction. In doing so, wedemonstrate the existence of overlaps in the predictions of different"frameworks" consisting of conjunctions of theory, conditionalization schemeand typicality assumption. This conclusion highlights the acute challengesinvolved in using such tests to identify a preferred framework that aims todescribe our observational situation in a multiverse.
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