Because Nino Said Yes, the Chief’s Saying No? To What Extent Does a Justice’s Vote Depend on the Others’ Votes?

The decisions of the U.S. Supreme Court often have important repercussions around the world, so it’s perhaps not surprising that is studied by lawyers and academics far beyond the Court’s jurisdiction. The results of such studies are often more useful for their insights into how others see us, than for any particular insights into how things work here. That’s because typically, the concepts and even the words we use to describe them just don’t translate all too well. Then again, they’re often misunderstood right here at home — the adjectives “liberal” and “conservative,” for example, have very different meanings depending on whether they’re modifying a political or jurisprudential noun, but try telling that to the average journalist.

Every now and then, however, you get a foreign study that — while still misinterpreting concepts and terms — nevertheless makes a nifty point.

One example was published just a couple of days ago: “Justice Blocks and Predictability of U.S. Supreme Court Votes,” (Nov. 9, 2011) by Spanish academics Roger Guimerà (of the Institució Catalana de Recerca i Estudis Avançats in Barcelona) and Marta Sales-Pardo (of the Departament d’Enginyeria Química, Universitat Rovira i Virgili in Tarragona). In their paper, Guimerà and Sales-Pardo tried to figure out how any given justice’s votes are affected by the votes of the other justices — not why or how, but whether the vote of Justice X depends on how each of the other justices are voting.

This is something that common wisdom claims happens all the time, such as the trope that Thomas tends to simply follow Scalia’s lead. There is usually some basis for the common wisdom, but it is never entirely accurate (in fact now there’s even talk of Thomas being a thought leader in his own right).

But all these comparisons tend to only compare one justice to another, or maybe blocs that tend to vote together on certain issues. The Supreme Court, however, is made up of nine justices, who all interact with each of the others in different ways. That’s 36 separate relationships. It’s even more complex when you try to figure how any relationship is affected by the other 35 relationships, and so on.

So enter Network Theory.

This is Guimerà and Sales-Pardo’s bailiwick. As NewScientist puts it, they “study complex systems, such as the metabolism of living cells, by considering them as networks of interacting components.” It’s often hard to tell what’s really going on in there, when there are many things interacting in often poorly-understood ways, and when you don’t have all the data you’d like to have. Network theory is a way to put the pieces together and figure out what the relationships probably are. Think of it as a sophisticated form of statistical analysis. It has been applied to hard sciences like biology and physics, to complex entities like the internet and the human brain, and even to the soft sciences of sociology, politics and economics.

Guimerà and Sales-Pardo determined that, if you look at how the other 8 justices voted in a particular case, you can usually predict how the 9th voted. It’s not prediction of future votes, but of what the interaction of these 9 justices will do in this particular case.

It may not seem surprising at first, but it becomes more surprising when you realize that this prediction has nothing to do with whether the justice is left- or right-leaning, or whether the president who appointed them was a Republican or a Democrat. The algorithm does not require any input with respect to the justice’s own ideology. The output is not affected by who appointed them. This would undermine much of the common wisdom, which holds these things to be the main drivers of how the justices influence each other’s votes. And indeed, the accuracy of the predictions exceeded that of forecasts by legal experts and of algorithms that took account of the issues in the cases.

Not so surprising was the finding that actual predictability was higher than what one would expect if all 9 were perfectly independent — they do interact, and so they debate and try to change each other’s minds, and they make concessions, and they form alliances and voting blocs. This leads to greater stability. And in fact, when cases were decided 5-4, the justices were more likely to vote with a bloc than to vote as they would have had they been entirely independent. It’s not ideal, perhaps, but it’s hardly surprising given human nature.

What was surprising was the observation that, during the last 50 years of the study (which only analyzed cases up to 2004), the aggregate predictability steadily decreased. It became less and less easy to predict what one justice would do based on what the others were doing. In other words, the justices were acting more and more as if they were not interacting.

And the greatest drops in predictability came about whenever the president was a Democrat.  “Aggregate court predictability,” they found, “has been significantly lower during Democratic presidencies.” This may be a correlation without causation, or it may reflect a digging-in-of-the-heels of individual justices and less inclination to be affected or influenced by the others. Why that would occur any more when one party is in office than another is hard to answer, though.

But the study didn’t look at why it happens or not, just whether it’s happening at all. It would make for an interesting discussion, though. What do you think?


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