You Brought It Up, Not Me…

Summary

Recently this plea went out to cyclists in the Chicagoland Area:

Active Transportation Alliance

Active Transportation Alliance

TELL GOV. QUINN: Don’t put the brakes on protected bike lanes and safer streets!
Posted by Active Transportation Alliance on February 11, 2013 at 1:14pm

Whether you’re an 8-year-old child or 80-year-old grandmother, you should be able to ride a bike on your community’s streets without fearing for your safety. Barrier protected bike lanes are designed with all kinds of people in mind to make biking a safe and easy option for everyone.

But Streetsblog Chicago and the Chicago Tribune have revealed that the Illinois Department of Transportation (IDOT) has put the brakes on barrier protected bike lanes and safer streets. This will impact plans for safer streets in both the City of Chicago and the suburbs. Please sign this petition telling Gov. Quinn that IDOT must cooperate with local communities to create safer streets for biking!

TAKE ACTION TODAY!
Tell Gov. Quinn: Don’t put the brakes on protected bike lanes and s…
– Lee Crandell, Active Trans

And with it came numerous replies. But one that I remembered was this:

Reply by Anne Alt 2-10 1 hour ago
Thank you for creating this petition. There’s more than enough data from other states regarding protected bike lanes.

So when I saw Anne’s reference to an opinion piece by David Brooks titled, “What Data Can’t Do” I was intrigued enough to read it through. After all the data that Anne is referring to was collected by folks (presumably in the DOT of each state) who understand how to both collect and massage the data to divulge the validity of protected bike lanes.

But here is a piece that calls into question the use of computers when the human brain is a better arbiter of the meaning of data. David Brooks writes:

Therefore, when making decisions about social relationships, it’s foolish to swap the amazing machine in your skull for the crude machine on your desk.

Data struggles with context. Human decisions are not discrete events. They are embedded in sequences and contexts. The human brain has evolved to account for this reality. People are really good at telling stories that weave together multiple causes and multiple contexts. Data analysis is pretty bad at narrative and emergent thinking, and it cannot match the explanatory suppleness of even a mediocre novel.

Data creates bigger haystacks. This is a point Nassim Taleb, the author of “Antifragile,” has made. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation. Falsity grows exponentially the more data we collect. The haystack gets bigger, but the needle we are looking for is still buried deep inside.

One of the features of the era of big data is the number of “significant” findings that don’t replicate the expansion, as Nate Silver would say, of noise to signal.

Big data has trouble with big problems. If you are trying to figure out which e-mail produces the most campaign contributions, you can do a randomized control experiment. But let’s say you are trying to stimulate an economy in a recession. You don’t have an alternate society to use as a control group. For example, we’ve had huge debates over the best economic stimulus, with mountains of data, and as far as I know not a single major player in this debate has been persuaded by data to switch sides.

Data favors memes over masterpieces. Data analysis can detect when large numbers of people take an instant liking to some cultural product. But many important (and profitable) products are hated initially because they are unfamiliar.

Data obscures values. I recently saw an academic book with the excellent title, “ ‘Raw Data’ Is an Oxymoron.” One of the points was that data is never raw; it’s always structured according to somebody’s predispositions and values. The end result looks disinterested, but, in reality, there are value choices all the way through, from construction to interpretation.

This is not to argue that big data isn’t a great tool. It’s just that, like any tool, it’s good at some things and not at others. As the Yale professor Edward Tufte has said, “The world is much more interesting than any one discipline.”

When the data from New York came back this fall showing that the number of new bike lanes had not created a safer infrastructure the folks in City Hall were scrambling for a reason to explain away what they had told everyone would  not occur. Increasing infrastructure is supposed to make all segments of the traffic pie safer. The cause was laid at the feet of motorists who were supposedly “text and driving”.

Clearly adding more bike lanes did nothing to prevent that sort of activity. So why then should Chicago not want more data to help ferret out the reality of the safety situation here as a result of a finite number of miles of new infrastructure? And why too would anyone point the way to an article like this one by David Brooks, knowing that it cuts both ways?

Inquiring minds want to know.