(1700 words; 8 minute read)
The core claim of this series is that political polarization is caused by individuals responding rationally to ambiguous evidence.
To begin, we need a possibility proof: a demonstration of how ambiguous evidence can drive apart those who are trying to get to the truth. That’s what I’m going to do today.
I’m going to polarize you, my rational readers.
In my hand I hold a fair coin. I’m going to toss it twice (…done). From those two tosses, I picked one at random; call it the Random Toss. How confident are you that the Random Toss landed heads? 50-50, no doubt––it’s a fair coin, after all.
But I’m going to polarize you on this question. What I’ll do is split you into two groups—the Headsers and the Tailsers—and give those groups different evidence. What’s interesting about this evidence is that we all can predict that it’ll lead Headsers to (on average) end up more than 50% confident that the Random Toss landed heads, while Tailsers will end up (on average) less than 50% confident. That is: everyone (yourselves included) can predict that you’ll polarize.
The trick? I’m going to use ambiguous evidence.
First, to divide you. If you were born on an even day of the month, you’re a Headser; if you were born on an odd day, you’re a Tailser. Welcome to your team.
You’re going to get different evidence about how the coin-tosses landed. That evidence will come in the form of word-completion tasks. In such a task, you’re shown a string of letters and some blanks, and asked whether there’s an English word that completes the string. For instance, you might see a string like this:
And the answer is: yes, there is a word that completes that string. (Hint: what is Venus?) Or you might see a string like this:
And the answer is: no, there is no word that completes that string.
That’s the type of evidence you’ll get. You’ll be given two different word-completion tasks—one for each toss of the coin. However, Headsers and Tailser will be given different tasks. Which one they’ll see will depend on how the coin landed.
Here’s your job. Click on the appropriate link below to view a widget which will display the tasks for your group. You’ll view the first world-completion task, and then enter how confident you are (between 0–100%) that the string was completable. Enter “100” for “definitely completable”, “50” for “I have no idea”, “0” for “definitely not completable”, and so on.
If you’re a Headser, the number you enter is your confidence that the coin landed heads on the first toss. If you’re a Tailser, it’s your confidence that the coin landed tails—so the widget will subtract it from 100 to yield your confidence that the coin landed heads. (If you’re 60% confident that the coin landed tails, that means you’re 100–60 = 40% confident that it landed heads.)
You’ll do this whole procedure twice—once for each toss of the coin. Then the widget will tell you what your average confidence in heads was, across the two tosses. This is how confident you should be that the Random Toss landed heads, given your confidence in each individual toss. And this average is the number that will polarize across the two groups.
Enough set up; time to do the tasks.
Welcome back. You have now, I predict, been polarized.
This is a statistical process, so your individual experience may differ. But my guess is that if you’re a Headser, your average confidence in heads was greater than 50%; and if you’re Tailser, your average confidence in heads was less than 50%.
I’ve run this study. Participants were divided into Headsers and Tailsers. They each saw four word-completion tasks. Here’s how the two groups’ average confidence in heads (i.e. their confidence that the Random Toss landed heads) evolved as they saw more tasks:
Both groups started out 50% confident on average, but the more tasks they saw, the more this diverged. By the end, the average Headser was 58% confident that the Random Toss landed heads, while the average Tailser was 36% confident of it.
(That difference is statistically significant; the 95%-confidence interval for the mean difference between the groups’ final average confidence in heads is [16.02, 26.82]; the Cohen’s d effect size is 1.58—usually 0.8 is considered “large”. For a full statistical report, including comparison to a control group with unambiguous evidence, see the Technical Appendix.)
Upshot: the more word-completion tasks Headsers and Tailsers see, the more they polarize.
The crucial question: Why?
(800 words left)
Getting a complete answer to this—and to why such polarization should be considered rational—will take us a couple more weeks. But the basic idea is simple enough.
A word-completion task presents you with evidence that is asymmetrically ambiguous. It’s easier to know what to think if there is a completion than if there’s no completion. If there is a completion, all you have to do is find one, and you know what to think. But if there’s no completion, then you can’t find one; but nor can you be certain there is none—for you can’t rule out the possibility that there’s one you’ve missed.
Staring at `_E_RT’, you may be struck by a moment of epiphany—`HEART!’—and thereby get unambiguous evidence that the string is completable.
But staring at `ST_ _RE’, no such epiphany is forthcoming; the best you’ll get is a sense that it’s probably not completable, since you haven’t yet found one. Nevertheless, you should remain unsure whether you’ve made a mistake: “Maybe it does have a completion and I should know it; maybe in a second, I’ll think to myself, `It is completable—duh!’” This self-doubt is the sign of ambiguous evidence, and it prevents you from being too confident that it’s not completable.
The result? When you’re presented with a string that’s completable, you often get strong, unambiguous evidence that it’s completable; when you’re presented with a string that’s not completable, you can only get weak, ambiguous evidence that it’s not. Thus when the string is completable, you should often be quite confident that it is; when it’s not, you should never be very confident that it’s not.
I polarized you by exploiting this asymmetry. Headsers saw completable strings when the coin landed heads; Tailsers saw them when it landed tails. That means that Headsers were good at recognizing heads-cases and bad at recognizing tails-cases, while Tailsers were good at recognizing tails-cases and bad at recognizing heads-cases.
As a result, if you ask Headsers, they’ll say, “It’s landed heads a lot!”; and if you ask Tailsers, they’ll say, “It’s landed tails a lot!”. They polarize.
Here it’s worth emphasizing a subtle point but important point—one that we’ll return to. The ambiguous/unambiguous-evidence distinction is not the weak/strong-evidence distinction. Ambiguous evidence is evidence that you should be unsure how to react to; unambiguous evidence is evidence that you should be sure how to react to.
Ambiguous evidence is necessarily weak, but unambiguous evidence can be weak too. Example: if I tell you I’m about to toss a coin that’s 60% biased towards heads, that is weak but unambiguous evidence--weak because you shouldn’t be very confident it’ll land heads, but unambiguous because you know exactly how confident to be (namely, 60%).
The claim is that it is asymmetries in ambiguity—not asymmetries in strength—which drive polarization. We can test this by comparing our ambiguous-evidence Headsers and Tailsers to a control group that received evidence that was sometimes strong and sometimes weak, but always (relatively) unambiguous. (It came in the form of draws from an urn; see the Technical Appendix for details.)
Here is the evolution of the Headsers and Tailsers who got unambiguous evidence:
As you can see, there is some divergence (most liked a “response bias” because of the phrasing of the questions), but significantly less divergence than in our ambiguous-evidence case. (Again, see the technical appendix for a statistical comparison.)
Upshot: ambiguous evidence can be used to drive polarization.
That concludes my possibility proof. The rest of this project will try to figure out what it means. To do that, we need to look further into both the theoretical foundations and the real-world applications.
To preview the foundations: there is a clear sense which both Headsers and Tailsers are succeeding at getting to the truth of the matter—for each coin flip, they each tend to get more accurate about how it landed. The trouble is that this accuracy is asymmetric, and as a result they end up with very different overall pictures of the outcomes of the series of coin flips.
To preview the applications: these ambiguity-asymmetries can be exploited. Fox News can spin its coverage so that information that favors Trump is unambiguous, while that which disfavors him is ambiguous. MSNBC can do the opposite. So when we divide into those-who-watch-Fox and those-who-watch-MSNBC, we are, in effect, dividing ourselves into Headsers and Tailsers. As a result, although we are getting locally more informed whenever we tune into these programs, our global pictures of Trump are getting pulled further and further apart.
In fact, the same sort of ambiguity-asymmetry plays out in many different settings—helping explain why group discussions push people to extremes, why individuals favor news sources that agree with their views, and why partisans interpret shared information in radically different ways.
That’s where we’re headed. To get there, we need to get a few more basics on the table. Empirically: what mechanisms have led to the recent rise in polarization? And theoretically: what would it mean for this polarization—in our Headser/Tailser game, and in real life—to be “rational”?
We’ll tackle those two questions in the coming weeks. Then we’ll put all the pieces together, and examine the role that ambiguity-asymmetries in evidence play in the mechanisms that drive polarization.
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For a full statistical analysis of the experiment, see the technical appendix. No doubt those with more experimental expertise can find ways it could be improved––suggestions most welcome!
Coming up next week: I’ll sketch a story of the empirical mechanisms that drive polarization; with that on the table, we'll move on to evaluating them normatively.
PS. Thanks to Branden Fitelson and especially Joshua Knobe for much help with the experimental design and analysis.
- What this blog is about
- Reasonably Polarized series
- RP Technical Appendix
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