Bayesian Apologetics

What Christians should be mindful of when presenting truthful apologetics (providing a competent defence of the Christian faith) is that, even though what we say is the truth, there will usually be a presentable counter-argument available to the atheists that sounds just as convincing to them from their perspective. At first glance, that may sound obvious – it is nearly always possible to provide some kind of counter-argument to nearly all propositions. But it may strike some Christians as strange, and perhaps disconcerting, that we can churn out strong Christian perspectives that speak truth to the world, but find that atheists always have contrasting perspectives, and often seem impervious to the strength of our apologetics. But I think that just says more about the nature of the discussion, and underscores the complexity of God’s created world, that even truths about Christianity presented in rich and elegant propositional prose are not often compelling enough to convince most objectors and bring closure to the debate.

I suppose, although strange in the sense just implied, it shouldn’t be that surprising really; a good Christian apologist might reasonably expect to be able to present their own hypothetical counter-arguments to their work - in a steel-manning, ideological Turing test-kind of manner – and in being able to do so, resolve to understand the strength of their own position with even more confidence. And if we can do it to our own offerings, it is perhaps to be expected that others will too, even if what we say is the truth.

It’s also important to recognise in Christian apologetics that what we are doing, and what the atheist is doing too, is employing a Bayesian framework, which is a combination of probabilistic reasoning, statistical modelling and though provocation, in order to build a coherent and internally consistent worldview based on numerous subset propositions. To that end, the debate about every proposition is a threefold; 1) the base rate in terms of the probability of a proposition being true regarding the information or data contained; 2) the probability that the evidence can be explained with the affirmative hypothesis; 3) the probability that the evidence could be explained better with an alternative hypothesis.

That is to say, the fundamental starting question concerns assessment of prior probability, and the following questions concern whether the claim is more likely to be true or false given the evidence presented. From that we try to ascertain the strength of the opening position, and whether further consideration of the evidence strengthens the initial claim or weakens it. Whether or not the participants are aware of it, this kind of Bayesian analysis is at the heart of every matter under discussion.

And I think this is the answer to the little conundrum stated at the top. Yes, there are always possible counter-arguments that can be offered, but the more strictly we adhere to the principles outlined above, and the more stringent way we can be in weeding out bad and extraneous arguments, the stronger the dialogue will be. The point is, having acknowledged that it’s always fairly easy to offer a counter argument - even to the most truthful statements – there is individual responsibility to ensure that you are not just making bad contributions and being fooled by their merit on account of your being able to make them so readily. Just because it’s always easy to conjure up a counter-argument, it doesn’t mean those counter-arguments are proficient or accurate rebuttals.