Submitted by Sal Arnuk and Joe Saluzzi via Themis Trading blog,
This is really the best paragraph I have read so far in 2017:
The world is awash in bullshit. Politicians are unconstrained by facts. Science is conducted by press release. So-called higher education often rewards bullshit over analytic thought. Startup culture has elevated bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit, then take advantage of our lowered guard to bombard us with second-order bullshit. The majority of administrative activity, whether in private business or the public sphere, often seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit.
It’s from The Bull$hit Syllabus, which was created by University of Washington Professors Carl Bergstrom and Jevin West, who are trying to combat The Bull$hit. The syllabus includes questions and standards for data scientists to think about and use.
They believe that with the advent of “Big Data” and tools to deal with it, the amount of BS in the world has really risen too much. It has become too easy for BS to be taken out of context, and to be spread and made to go “viral.” Big Data has given us ginormous datasets to study and manipulate. While we might not be quick to draw conclusions from a smaller data set, we have become very comfortable putting credence to implications and patterns in big data sets. Bergstrom explains:
Before big data became a primary research tool, testing a nonsensical hypothesis with a small dataset wouldn’t necessarily lead you anywhere. But with an enormous dataset, he says, there will always be some kind of pattern.
“It’s much easier for people to accidentally or deliberately dredge pattern out of all the data,” he says. “I think that’s a bit of a new risk.”
He is also skeptical of machine learning algorithms. They often give very strong results, but the data they analyze and draw from is not questioned often enough:
Can an algorithm really look at a person’s facial features and determine their preponderance for criminality? Yeah, maybe not. But that was the argument of a paper actually published just a few months ago (Automated Inference on Criminality Using Facial Images)
“If you look deeper, you find some spurious things driven mostly by how that person was dressed, if they’re frowning or not,” West says. “There’s manipulation of the way the results are reported.” Not to mention that human bias and existing structural inequalities can make algorithms just as flawed as the humans that make them.
Feel free to read this article called Data Can Lie – A Guide to Calling Out Bull$hit., and feel free to look at the actual Bull$hit Syllabus linked to atop this note.
While this topic may not specifically appear to be trading related, in my opinion it really is relevant. Algorithms are constantly tested, and in place in the markets. Development of machine -learning trading algos are on the rise. They all use Big Data. Your data. Collected by stock exchanges and sold.
The guys who collect it and sell it want you to use the heck out of it, become addicted to it, and keep increasing the price of it.
But maybe a lot of the Big Data is really bull$hit. And maybe all that data can cause more harm than good.
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