The recent (and in many respects ongoing) Brexit vote in the United Kingdom provides a superb example of poor use of mathematics. Regardless of your views on the desirability or otherwise of the UK remaining a member of the European Community (an issue on which this article is agnostic), for a democracy to base its decision on a complex issue based on a single number displays a woeful misunderstanding of numbers and mathematics.
Whenever there is an issue that divides a population more or less equally, making a decision based on a popular vote certainly provides an easy decision, but in terms of accurately reflecting "the will of the people", you might just as well save the effort and money it costs to run a national referendum and decide the issue by tossing a coin--or by means of a penalty shootout if you want to provide an illusion of human causality.
Politicians typically turn to popular votes to avoid the challenge of wrestling with complex issues and having to accept responsibility for the decision they make, or because they believe the vote will turn out in a way that supports a decision they have already made. Unfortunately, with a modicum of number sense, and a little more effort, it's possible to take advantage of the power that numbers and mathematics offer, and arrive at a decision that actually can be said to "reflect the will of the people".
The problem with reducing any vaguely complex situation to a single number is that you end up with some version of what my Stanford colleague Sam Savage has referred to as the Flaw of Averages. At the risk of over-simplifying a complex issue (and in this of all articles I am aware of the irony here), the problem is perhaps best illustrated by the old joke about the statistician whose head is in a hot oven and whose feet are in a bucket of ice who, when asked how she felt, replies, "On average I am fine."
Savage takes this ludicrous, but in actuality all-too-common, absurdity as the stepping-off point for using the power of modern computers to run large numbers of simulations to better understand a situation and see what the best options may be. (This kind of approach is used by the US Armed Forces, who run computer simulations of conflicts and possible future battles all the time.)
A simpler way to avoid the Flaw of Averages that is very common in the business world is the well-known SWOT analysis, where instead of relying on a single number, a team faced with making a decision lists issues in four categories: strengths, weaknesses, opportunities, and threats. To make sense of the resulting table, it is not uncommon to assign numbers to each category, which opens the door to the Flaw of Averages again, but with four numbers rather than just one, you get some insight into what the issues are.
Notice I said "insight" there; not "answer". For insight is what numbers can give you.
Applications of mathematics in the natural sciences and engineering can give outsiders a false sense of the power of numbers to decide issues. In science (particularly physics and astronomy) and engineering, (correctly computed) numbers essentially have to be obeyed. But that is almost never the case in the human or social realm.
When it comes to making human decisions, including political decisions, the power of numbers is even less reliable than the expensively computed numbers that go into producing the daily weather forecast. And surely, no politician would regard the weather forecast as being anything more than a guide--information to help make a decision.
Yet we now have the spectacle of a major nation reduced to a scene reminiscent of headless chickens running around in a farmyard, as a result of reducing a complex decision to a single number. Good grief, even the weather forecast comes in the form of a range of numbers.
One of mathematicians' favorite examples of how single numbers can mislead is known as Simpson's Paradox, in which an average can indicate the exact opposite of what the data actually says.
The paradox gets its name from the British statistician and civil servant Edward Simpson, who described it in a technical paper in 1951, though the issue had been observed earlier by the pioneering British statistician Karl Pearson in 1899. (Another irony in this story is that the British actually led the way in understanding how to make good use of statistics, obtaining insights the current UK government seems to have no knowledge of.)
A famous illustration of Simpson's Paradox arose in the 1970s, when there was an allegation of gender bias in graduate school admissions at the University of California at Berkeley. The fall 1973 figures showed that of the 9,442 men and 4,321 women who applied, 44% of men were admitted but only 35% of women. That difference is certainly too great to be due to chance. But was there gender bias? On the face of it, the answer is a clear "Yes".
In reality, however, when you drill down just one level into the data, from the school as a whole to the individual departments, you discover that, not only was there no gender bias in favor of men, there was in actuality a statistically significant bias in favor of women. The School was going out of its way to correct for an overall male bias in the student population. Here are the figures.
- Men applied 825, admitted 62%; women applied 108, admitted 82%
- Men applied 560, admitted 63%; women applied 25, admitted 68%
- Men applied 325, admitted 37%; women applied 593, admitted 34%
- Men applied 417, admitted 33%; women applied 375, admitted 35%
- Men applied 191, admitted 28%; women applied 393, admitted 24%
- Men applied 373, admitted 6%; women applied 341, admitted 7%
In all departments except 3, a higher proportion of women applicants was admitted, in Department 1 significantly so.
There was certainly a gender bias at play, but not on the part of University Admissions. Rather, as a result of problems in society as a whole, women tended to apply to very competitive departments with low rates of admission (such as English), whereas men tended to apply to less-competitive departments with high rates of admission (such as Engineering).
We see a similar phenomenon in the recent UK Brexit vote, though there the situation is much more complicated. British Citizens, politicians, and journalists who say that the recent referendum shows the "will of the people" are, either though numerically informed malice or basic innumeracy, plain wrong. Just as the UC Berkeley figures did not show an admissions bias against women (indeed, there was a bias in favor of women), so too the Brexit referendum does not show a national will for the UK to leave the EU.
Britain leaving the EU may or may not be their best option, but in making that decision the government would do well to drill down at least one level, as did the authorities at UC Berkeley. When you do that, you immediately find yourself with some much more meaningful numbers. Numbers that tell more of the real story. Numbers on which elected representatives of the people can base an informed discussion as how best to proceed--which is, after all, what democracies elect governments to do.
Much of that "one level down" data was collected by the BBC and published on its website. It makes for interesting reading.
For instance, it turned out that among 18-24 years old voters, a massive 73% voted to remain in the UK, as did over half of 25-49 years of age voters. (See the table "How different age groups voted" on that BBC page.) So, given that the decision was about the future of the UK, the result seems to provide a strong argument to remain in the EU. Indeed, it is only among voters 65 or older that you see significant numbers (61%) in favor of leaving. (Their voice matters, of course, but few of them will be alive by the time any benefits from an exit may appear.)
You see a similar Simpson's Paradox like effect when you break up the vote by geographic regions, with London, Scotland, and Northern Ireland strongly in favor of remaining in the UK (Scotland particularly so).
It's particularly interesting to scroll down through the long chart in the section headed "Full list of every voting area by Leave", which is ordered in order of decreasing Leave vote, with the highest Leave vote at the top. I would think that range of numbers is extremely valuable to anyone in government.
There is no doubt that the British people have a complex decision to make, one that will have a major impact on the nation's future for generations to come. Technically, I am one of the "British people," but having made the US my home thirty years ago, I long ago lost my UK voting rights, and my interest today is primarily that of a mathematician who has made something of a career arguing for improved public appreciation for the sensible use of my subject, and railing against misuse of numbers.
My emotional involvement today is in the upcoming US presidential election, where there is also an enormous amount of misuse of mathematics, and many lost opportunities where the citizenry could take advantage of the power numbers provide, in order to make better decisions.
But for what it's worth, I would urge the citizens of my birth nation to drill down one level in your referendum data. For what you have is a future-textbook example of the same phenomenon highlighted by Simpson's Paradox (albeit with many more dimensions of variation). To my mathematician's eye (trained as such in the UK, I might add), the referendum provides very clear numerical information that enables you to form a well-informed, reasoned decision as to how best to proceed.
Deciding between the "will of the older population" and the "will of the younger population" is a political decision. So too is deciding between "the will of London, Scotland, and Northern Ireland" and "the will of the remainder of the UK". What would be mathematically irresponsible, and to my mind politically and morally irresponsible as well, would be to make a decision based on a single number. Single numbers rarely make decisions for us. Indeed, single numbers are usually as likely to mislead as to help. A range of numbers, in contrast, can provide valuable data that can help us to better understand the complexities of modern life, and make better decisions.
We humans invented numbers and mathematics to understand our world (initially physical and later social), and to improve our lives. But to make good use of that powerful, valuable gift from our forbearers, we need to remember that numbers are there to serve us, not the other way round. Numbers are just tools. We are the ones with the capacity to make decisions.
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