Conspiracy Theorising is a Bayesian Nightmare

What does Bayesian epistemology have to say about conspiracy theorising, and how might this inform the generalist versus particularist debate? This paper addresses that question by applying Bayes’ Theorem to the problem of evaluating conspiracy theories. I begin by outlining how Bayesian reasoning works as a method for updating beliefs in light of new evidence. I then argue that conspiracy theories are especially difficult to analyse in this way. First, the prior probabilities we assign to them are unstable, because they depend heavily on how we define ‘conspiracy’ and on incomplete knowledge of how often conspiracies really occur. Second, the weight of new evidence often fails to shift our views, because signs that might seem to count against a conspiracy can just as easily be taken as evidence of its success. Together, these problems make conspiracy theorising a Bayesian nightmare. I conclude by supporting a caveated form of particularism: conspiracy theories should not be dismissed simply because they are conspiracy theories, but they should be acknowledged as especially difficult to evaluate.