I can’t tell if Suki Finn’s Beyond Reason: The Mathematical Equation for Unconditional Love is meant to be taken seriously or not. Irony on the internet is usually indistinguishable from earnestness. The fact that there is an addendum with a mathematical proof may indicate that it’s serious, but maybe it’s a droll bit of farce?1
I read it with interest, in any case. Finn offers an analysis of conditional and unconditional love that is modeled on conditional and unconditional credence. As I’ve discussed in some recentposts, I think that recognizing the difference between conditional and unconditional value is crucial for understanding the relation between values and belief.2
My paper Science, Values, and the Priority of Evidence has been accepted at Logos&Episteme. I worked over the manuscript to meet their style guidelines, sent it off, and put the last draft on my website. Since it’s an OA journal, in the gratis and author-doesn’t-pay sense, I will swap in the published version when it appears.
In his PhD thesis, Stijn Conix briefly considers the suggestion “that it does not make sense to think of values and epistemic standards as taking priority over each other.”1 In a footnote, he cites Matthew Brown “who refers to Magnus making a similar remark in personal communication.”
That’s cool, because I have made such a remark. I have a draft paper in which I defend it.
Frustratingly, today I got another rejection notice for that paper. I’ll take a day to cool off before looking at the referee comments again, and then I’ll decide on my next move. The most effective strategy for disseminating ideas might be to just talk to Matt Brown more often. Alas, that’s hard to document on my CV.
It occurs to me that there is a mistake in my previous post, but it can be patched up.
To review: Considerations of inductive or ampliative risk can make the difference between it being appropriate to believe something and it being inappropriate. If the stakes are high, then you might demand more evidence than if the stakes are low.
Schematically, what’s relevant are conditional values: the benefit of believing P if it is true, the cost of believing P if it is false, the cost of not believing P if it is true, and the benefit of not believing P if it is false.
In cases of ampliative risk, the evidence does not overwhelmingly speak for or against. So the determination to believe or not depends in part on the stakes involved. I’ve typically put this in terms of conditional values: the benefit of believing P if it is true, the cost of believing P if it is false, the cost of not believing P if it is true, and the benefit of not believing if it is false. Heather Douglas calls this values playing an indirect role.
Implicit in this is that believing P if it is false is a cost. And so on. Ending up with accurate beliefs is generally good, and ending up with inaccurate beliefs is bad. What’s at issue is not the general valence of certain outcomes but instead their intensity.
Abstract: Scott Aikin and Robert Talisse have recently argued strenuously against James’ permissivism about belief. They are wrong, both about cases and about the general issue. In addition to the usual examples, the paper considers the importance of permissiveness in scientific discovery. The discussion highlights two different strands of James’ argument: one driven by doxastic efficacy and another driven by inductive risk. Although either strand is sufficient to show that it is sometimes permissible to believe in the absence of sufficient evidence, the two considerations have different scope and force.
Having just taught a seminar on pragmatism and reading a recent book review by Alex Klein, I realized that there’s a reading of William James’ “Will to Believe” which isn’t universally recognized even though it seems obvious to me.
The LA Times has an interesting interview with self-described “data skeptic” Cathy O’Neil, the author of Weapons of Math Destruction. Although the Times puts her skepticism in terms of big data, her concerns are really about values in science. Algorithms, she suggests, have a veneer of objectivity but always reflect choices and valuations. When the algorithms are secret, then the values incorporated in them aren’t open to scrutiny. She says:
I want to separate the moral conversations from the implementation of the data model that formalizes those decisions. I want to see algorithms as formal versions of conversations that have already taken place.
[P]olitical polls are actually weapons of math destruction. They’re very influential; people spend enormous amounts of time on them. They’re relatively opaque. But most importantly, they’re destructive in various ways. In particular, they actually affect people’s voting patterns. … Polls can change people’s actual behavior, which disrupts democracy in a direct way.
I’ve ordered a copy of her book, and when it arrives I will put it on top of the stack of books I regret not having read.