Multiverse Musings – Plausibility vs. Probability

Multiverse Musings – Plausibility vs. Probability

Time magazine once quoted Bill Gates saying “It’s possible, you can never know, that the universe exists for me. If so, it’s sure going well for me, I must admit.” Even if Mr. Gates was not advocating solipsism, this statement is impossible to falsify from an experimental or evidential perspective. Thus, it is possible that the world exists solely for his benefit. But is it plausible?

In a paper published in the journal Theoretical Biology and Medical Modelling, David Abel seeks to distinguish between plausibility and possibility in a rigorous scientific framework. 1 This work has relevance for areas of research, such as the origin of life, where simple (and even sophisticated) probability estimates of natural processes generating a known outcome tend to be extremely small. Abel develops a procedure that accounts for the low probabilities and the available resources to identify plausible explanations among all the possible explanations. For example, assume a particular arrangement of protons had a very low probability and someone proffered an explanation relying on random rearrangements of protons. The metric would evaluate the plausibility of that arrangement by accounting for the probability, the number of protons in the universe, and the time it takes for protons to rearrange into different configurations under the known laws of physics. If the metric provides a number below some threshold, then the “random” model would be falsified (not plausible), even though it provides a possible explanation.

Such a metric provides a valuable research tool by eliminating implausible research avenues and by identifying areas lacking sufficient knowledge to properly apply the metric. In both cases, future research efforts are devoted to areas with the most promise for improving our understanding of particular phenomena. How does this relate to the multiverse?

The multiverse appears to provide an infinite resource supply. Thus, it becomes impossible to rule out any possible explanation regardless of its improbability. Such a scenario imposes major roadblocks in scientific disciplines, like cosmology and origin-of-life research, where repeat experiments cannot be performed.

In fact, scientists have proposed that natural selection explains the life-friendliness of our universe because super-intelligent beings created our universe with properties similar to theirs. Others invoked the multiverse to explain the origin of life on Earth.

The author of the “plausibility” paper takes such use of the multiverse to task. He argues multiverse models are completely speculative and untestable, therefore, they don’t belong in the scientific arena. While I agree that multiverse models have little to no empirical basis at present, they are testable in principle, and advocates seek to develop the means for testing both theoretically and observationally.

Furthermore, in cosmology, multiverse ideas flow out of our models to understand the evidence we see in this universe (as opposed to being invoked to explain highly improbable events). Thus, these still-speculative multiverse models have a valid place at the table of cosmological research. Biologists have a harder time justifying the desire to invoke the multiverse because they use a speculative cosmological model to explain biological data that does not otherwise indicate a need for a multiverse. Additionally, the paper argues that strictly physical processes may not be able to account for the information forming the genetic basis for life. In other words, life may not be strictly physical or derivable from the physical laws governing our universe. If so, the resources provided by a multiverse do not help the naturalist.

The fact that scientists invoke the multiverse to explain highly improbable events in life’s history demonstrates just how strongly the scientific data points to a supernatural Creator. And that evidence continues to mount.

1. David L. Abel, “The Univeral Plausibility Metric (UPM) and Principle (UPP),” Theoretical Biology and Medical Modelling 6, no. 27, (December 3, 2009): published at

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