The final chapter of my thesis, which is number 7 (in the file directory), number 6 (according to the PDF) or number 5 (as my supervisors would have it) is my third peg, so to speak, in why I think we have a prima facie case for our suspicion of conspiracy theories; they are typically examples of Inferences to Any Old Explanation (which you might know better as “Just So” stories, ala Mr. Kipling ((And his marvellous cakes.)) ).
Originally I was going to base this chapter on large chunks of Peter Lipton’s book on the Inference to the Best Explanation (which, funnily enough, is called “Inference to the Best Explanation” and was published in 2004 (the second edition) by Routledge), and not just because he specifically mentions (and then glosses over) conspiracy theories on page 60.
“Perhaps some conspiracy theories provide examples of this. By showing that many apparently unrelated events flow from a single source and many apparent coincidences are really related, such a theory may have considerable explanatory power. If only it were true, it would provide a very good explanation. That is, it is lovely. At the same time, such an explanation may be very unlikely, accepted only by those whose ability to weigh evidence has been compromised by paranoia.”
Lipton runs a contrast between the loveliness of explanations (just how powerful they are as explanatory hypotheses, essentially) and the likeliness of such explanations (i.e. just how probable is the explanatory hypothesis); he thinks ((Well, thought; Peter Lipton is dead.)) that conspiracy theories were good explanations only in the lovely sense as they were unlikely.
Lipton doesn’t come back to conspiracy theories, which is useful for me, because, in some important respects, it rather gives away the kind of analysis I have in kind for chapter 5/6/7.
As I wrote, my original intention was to develop Lipton’s view with specific respect to conspiracy theories, but that is no longer the case. Instead, I am developing the Inference to Any Old Explanation analysis that Dr. Jonathan McKeown-Green and I worked up for our Critical Thinking course (PHIL105 to the fans) at Auckland. The Lipton material is useful in talking about when the inferential practices of epistemic agents can be said to `go right’ but my analysis is really about when such practices `go wrong.’ It is much easier to work up a bespoke philosophy/epistemology than it is to try and make the work of someone else fit your particular analysis (ask me about my MA thesis for detailed reasons as to how that doesn’t necessarily work out for the best).
Still, I should point out that whilst I was writing this post I realised that there was a particular part of Lipton’s analysis of IBE (as the cool kids these days call “Inference to the Best Explanation”) which I could use to fill this particular hashed out section of the chapter (the “%” marks are playing the role of hashes in my LaTeX documents):
%However, we should also be aware that there is a kind of tradeoff between the probability of an hypothesis and the extent to which said hypothesis suggests the explanans.
%[This is the stuff that motivates Bayesianism. We might need to say a bit about it somewhere to help set up this discussion of the Inference to Any Old Explanation.] – Will need my Goldman…
I’m no Bayes scholar; I know how the theory works and the difference between prior and posterior probabilities (which it is important never to confuse), but the specific details… Well, I’d need to spend quite some time with a primer and a notepad to get myself sufficiently up to speed on Brother Bayes and his mathematical theorems. However, Lipton has a gloss on Bayes, since Bayesianism is often trotted forth as a contender for a theory of the Inference to the Best Explanation, and so I might use the Lipton gloss (which I’ve partially written up) after all.
Which goes to show that this new “thesis-centric” blog ethos is already delivering. Huzzah.
Next time: What it is I am actually trying to say in chapter 5/6/7.