Many of us in the Science and Religion program have recently been examining miracles in our course on science and scripture, and today we took on the resurrection of Jesus. As you might imagine, this is a formidable topic, and one can come at it from all kinds of directions. One of the more inventive approaches we discussed comes from philosopher of religion Richard Swinburne in his book The Resurrection of God Incarnate. Swinburne begins with the hypothesis that Jesus was God incarnate and that he was resurrected, and he attempts to convince his readers using Bayesian reasoning that the likelihood of this hypothesis being true is overwhelmingly high. In fact, he comes up with a specific number: according to Swinburne’s analysis, there is a 97% probability that his hypothesis is true.
How on earth does Swinburne justify such a calculation? I’ll spare you the gory details and refer you to Wikipedia for more on Bayes’ Theorem. Suffice it to say that everything comes down to our confidence about a handful of scenarios. For instance, given the evidence of natural theology, how confident are we that a “traditional” God exists? Assuming God exists, what is the likelihood that God would become incarnate? How probable is it that the specific circumstances of Jesus’ life would come to pass if he was in fact God incarnate? Swinburne chooses values for the probability of each scenario in rather arbitrary fashion, applies the theorem, and voilà – mathematical proof that belief in the incarnation and resurrection is highly rational.
On the other hand, philosopher Larry Shapiro has a recent piece on miracles in which he wields probability to more skeptical ends. Instead of attempting to assign probabilities to theological claims surrounding the resurrection, he focuses on the reliability of human witnesses, the most direct evidence available to us. His result:
The base rate for the resurrection is (let’s say) one in 1 billion. The witnesses go wrong only one time in 100,000. One billion divided by 100,000 is 10,000. So, even granting the existence of extraordinary witnesses, the chance that they were right about the resurrection is only one in 10,000; hardly the basis for a justified belief.
Why do two approaches that use such similar methods yield such different results? I think the answer becomes clear when we examine the type of event that each author emphasizes. Swinburne attempts to quantify theological propositions that have deep implications for the nature of reality, while Shapiro turns his attention to measurable human behavior – a far more appropriate domain in which to apply statistical methods. Even as a believer, I think Shapiro’s skepticism rings more true. My natural instinct is to be suspicious of any model that claims to mathematically prove a theological proposition beyond all reasonable doubt; such a model trivializes faith. It seems fitting to me that a numerical analysis of the resurrection should yield a low probability because that result confirms my instinct.
What do you think? Are either of these approaches convincing? Can we plausibly estimate the numbers we need for this calculation? And of course, the question that’s been in the back of your mind this whole time: is there any theological value in applying mathematical methods to the resurrection, or are we wasting our time?