universe is a purely metaphysical consideration. This paper argues to the contrary that
the existence of other universes is a meaningful hypothesis, which can be tested based
on observable data. The paper applies the perspective of observer bias as an approach for assigning probabilities to the mutually excluding hypotheses of universe vs. multiverse, i.e., whether the known universe is the only universe, or just one universe in an ensemble of universes, a so-called multiverse. The basic idea of the paper relies on the following consideration. In a multiverse, the vast majority of observers would live in universes that are more life friendly. Thus, conditional on there being a multiverse, we should expect to find ourselves in a universe with values of the fundamental parameters that provide particularly fertile grounds for life. In contrast, if there is only a single universe, it may well be the case that there is still a few observers even if the parameters are far from ideal for life, and in that case, those observers will find values of the parameters that are not ideal for intelligent life. It may well be the case that, among all parameter configurations that allow life at least somewhere, those that are not ideal for life far outnumber those that are ideal for life. Based on this elementary consideration, the paper proceeds to propose a quantitative framework to determine probabilities for either hypothesis. In particular, it is described how a future ab initio determination of some of the factors in the Drake equation may be used to infer which one of the two hypotheses is the more likely. A quantitative approach to this end is proposed. Expressing these factors of the Drake equation in the context of the two hypotheses, a general likelihood approach is first described. Then, to support intuition, example computations are provided, illustrating how an actual hypothesis test would work in practice.
The authors would like to sincerely thank several colleagues who have engaged in discussion on the topic of this paper and enriched the perspective of the paper. In particular, the authors would like to thank Dr. Torben Knudsen, who has carefully reviewed the statistical framework of the paper. Also, the authors would like to express heartfelt thanks to the anonymous reviewers, who have shown a strong engagement in improving the paper.
- Drake equation
- Observer bias
- Hypothesis testing
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