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Falsifiability is Bayesian Evidence

I’ve explained how Bayes Theorem demonstrates nicely why an explanation being unfalsifiable is a bad explanation. An explanation that can be used to explain some data and explain the complete opposite of those data is a bad explanation. But I should note that “bad” is doing a lot of work here; on topics besides the existence of god (a hypothesis of maximum entropy; one of the most common and most unfalsifiable explanations out there) it should be more comparative. Yet, “bad” doesn’t necessitate “wrong”.

If you don’t feel like clicking on my many previous posts on the topic, I’ll explain here again using a similar example.

Say I have two pants pockets. One pocket has only $USD coins and the other pocket has coins from all across the world, including $USD coins. If I pull a coin from a random pocket and it’s a $USD dime, does this mean that I pulled it from the pocket with only $USD coins or from the pocket that has coins from all over the world?

All else being equal (e.g., the same number of coins or same number of $USD coins in each pocket) it’s more likely that I pulled it from the pocket that only has $USD coins. This is because there are more possible coins available in the pocket with coins from all over the globe. The citizen of the world pocket is less falsifiable than the $USD pocket. But that doesn’t mean it’s wrong. That the citizen of the world pocket can be used to explain both pulling out a $USD coin and also not pulling out a $USD coin makes it a worse explanation than the $USD only pocket. Less likely doesn’t mean wrong though.

What does an unfalsifiable explanation look like? Again, I’ll invoke the god hypothesis. One pocket has only $USD coins and the other pocket is a pocket of miracles. Any coin from any civilization on any planet in the entirety of existence is possible from the other pocket. The reason this is unfalsifiable is because there are more possibilities for coins. Someone trying to use the miracle pocket to explain pulling out a $USD quarter would reply “but you can’t prove it wrong!

The fact that you can’t prove it wrong is what makes it less likely to be true. Less likely than something that can be proven wrong.

Again, you can look at the math behind this logic here.

The operating principle here is that, the more potential (and mutually exclusive) data a hypothesis, explanation, or model can explain, the less likely it is to explain any particular datum. Somewhat counter-intuitive, but this is what probability theory tells us. Do not trust your intuitions when it comes to probability. They are wrong.

As I wrote in one of the other posts on Bayes Theorem and falsifiability, god could have had us live on any planet in the solar system. With god, all things are possible. But we just so happen to live on the only planet in our solar system where it’s possible for intelligent life to exist without supernatural intervention. Yet if we invoke god, that hypothesis could be used to explain us living on Jupiter or Mercury or a comet orbiting the sun every 150 years; the naturalism hypothesis cannot explain those possibilities, if one of them is what had actually happened. God has more possible locations to create humanity (i.e., can’t be proven wrong) than a godless hypothesis.

For a more real-world example so I can stop beating up on god, the same situation happens between evolutionary psychology and blank-slatism. Blank-slatism and the social constructionist hypotheses / social role theories claim that any configuration of human behavior is possible due to socialization, but we just so happen to live in a world where e.g., men have a high murder rate among their fellow men, just like the males of our primate cousins.

The social constructionist hypothesis isn’t as egregious as the god hypothesis, but it’s still less falsifiable than any evolutionary explanation. In this case, intrasexual competition.

Let’s continue with the examples.

What if I told you that the number of men against abortion is higher than the number of women who are anti-abortion? Of course, Patriarchy makes men misogynists who treat women as baby machines so of course they don’t want women to have a choice.

What if I told you that the number of women against abortion is higher than the number of men who are anti-abortion?

Of course, Patriarchy makes women have to behave like men, that makes women internalize their misogyny so that means they’ll be viewing themselves as baby machines.

Do you see what I did there? A hypothesis (Patriarchy) that can explain data and be used to explain the complete opposite of those data? That means it can’t be proven wrong. That means it’s behaving like the god hypothesis.

That means it’s unfalsifiable. That means it’s a bad explanation.

What would be a good hypothesis? One that explains only one of those situations and completely fails at explaining the other. Something like intrasexual competition, which would say that abortion is a woman on woman problem and men care less either way. Intrasexual competition can only apply to one of those hypotheticals, and ***spoiler alert*** it’s the one that we see:

Intrasexual competition makes no sense for men being more anti-abortion than women, (and if that was indeed the case we could rule intrasexual competition out, like how getting a non-$USD coin rules out the $USD-only pocket) but makes sense to explain why women are more anti-abortion than men (and more pro-abortion than men): They are competing with other women. Indeed, the poll above shows that the abortion debate is mainly a battle between republican women and democrat women. Now it shouldn’t surprise you that the same thing happens in other domains related to sex and reproduction among humans. Like going topless, sex work, fat shaming, promiscuity/slut shaming, wearing makeup, etc. All of those are topics where men are less polarized about it than women, just like abortion.

Now I’m not saying that all current models of evolutionary psychology are gold. But with no other information, evolutionary hypotheses have a higher prior of being correct than social role hypotheses when it comes to large scale human behavior. Indeed, human beings are biased to apply social rules to phenomena when they don’t actually apply. The opposite — not applying social rules when they should — rarely happens. Remember, our intuitions about invoking social rules as an explanation are because we want to curry favor, support allies, or denigrate enemies. It makes System 1 sense that society tells men to be more violent than women, or that society forces women to be less aggressive than men. Unfortunately, your intuitions are highly unlikely to be true. And as I demonstrated above, the social role explanation isn’t a very robust explanation.

As a matter of fact, a lot of the problems people point out with evolutionary psychology today are the same ones that were encountered with evolutionary biology in the 19th century. Darwin’s original formulation had no mechanism (DNA hadn’t been discovered), no predictions (it was explaining things that people had already seen, i.e., a “just-so” story), and no mathematical models.

But it was still a good explanation; a better explanation than alternatives. A lot better explanatory framework than social constructionism Creationism. And that’s all that really matters. The more falsifiable explanation wins the race.

The moral of the story is, that there are very few things we encounter in either science or every day life that are wholly unfalsifiable (besides the existence of god). Unfalsifiability shouldn’t be viewed as a binary option. Some explanations are either more or less falsifiable than others. Falsifiability is Bayesian, and like all things Bayesian, should be used in a comparative, greater than/less than manner and not in a binary is/is not formulation. I’m just a lone voice crying out in the wilderness, but I would like “unfalsifiable” to be replaced with “more” or “less” falsifiable in almost all common usage; only save “unfalsifiable” for truly egregious cases like omnipotent gods or Last Thursdayism.

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Posted by on April 30, 2019 in Bayes, cognitive science, religion

 

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