Paradigm Lost?

A somewhat long and rambling consideration of Kuhn's The Structure of Scientific Revolution

In college, I double majored Political Science and Management Science. I now work in a field called Data Science. Even though the last time I sat in a physics class was my junior year of high school, I am a scientist. One of my college buddies, an engineer, liked to poke fun at me, "if they have to put science in the name, I am pretty sure it's not science." Despite my degrees, I agreed with him. Seven years and one PhD later, my thoughts on science have changed. I no longer define science so much by the subject matter as by the process. Science is not limited to applications of physics, chemistry, or biology; human behavior, interactions between people, and society at large can also be topics of scientific inquiry.


Science isn't so much defined by subject matter as by a process. This process is, of course, the scientific method: generating hypothesis and testing the hypothesis by observation or experimentation. Unsurprisingly, there is a science of science; which is excellently described (and probably really began) in Thomas Kuhn's The Structure of Scientific Revolution. In this book, Kuhn proposes a theory of how large scientific breakthroughs,"revolutions", are arrived at. To do so, he also establishes a framework for how science functions outside periods of revolution. I found this framework both immensely useful for understanding the ebbs and flows of science, but also questioned whether "revolution" is as unique as Kuhn seems to imply.

16-0 and I feel fine

When I last posted after a Golden State Warrior's win, I was a little dismayed. Their win shed light on some fundamental flaws in my Bayesian updating model. Now, I have no such trepidation. Warriors moved to 16-0 (an unprecedented NBA record) and I spent my weekend coding and doing statistics (a very precedented Evan activity)

Podcast Power Ranks

I listen to a ton of Podcasts, something like three hours a day. It's a lot. I have a 45 minute commute each way. I work out for another 1 to 1.5 hours most days. Add in doing chores around the house (cooking, dishes, etc), and the moments before I fall asleep. Why not use every moment of that to suck in more information?

Here is a completely subjective ranking of my top 35 podcasts, based on no explicit criteria at all:

Robustness Check

The Warriors are 13-0. Normally, I would ecstatic but it revealed a big problem in my model.  My model now says the Warriors have a 0 percent chance of repeating their performance from last year, no matter your prior beliefs. Some people would say that Steph Curry is the ultimate heat check, but in this case he is the robustness check (I know, that was extremely lame... I'm just gonna own it).

Give Them Credit?

So there is thing about people, we're not entirely rational. Instead of always objectively weighing the costs and benefits of every decision, sometimes we are affected by our mood or compare ourselves to others. These irrationalities are well-documented by behavioral economist like Daniel Kahneman and Dan Ariely.

One of the ways we are irrational is our preference for instant gratification, something the behavioral economists refer to as hyperbolic discounting. This leads to all sorts of common human behaviors, such as procrastination. Doing some work now feels really high cost, but it seems like it will be less of a chore in the future. Of course, when that future time rolls around, it again feels high cost. So those of us that are irrational fall into a cycle of procrastination. Its probably fair to say we all procrastinate to some degree, so we are all irrational.  

Come Out and Play

The Learning Problem

The Golden State Warriors have started the NBA season 11-0. If the team keeps up this pace, they will win all 82 games, sweep every round of playoffs, and never ever lose again. Trust me, I'm a data scientist.

Something sounds fishy here, right? Obviously, I don't expect the team to win every single game just because they haven't lost one yet. Why? For one thing no team has ever gone 82-0, so I consider that pretty unlikely. For another thing, the Warriors were a great team (some would say historically great) last year and they "only" won 81.7 percent of their games. So can I really expect them to be that much better?

On the other hand, seeing how they have performed so far, I must have learned something about how amazingly awesome they are.  The question is, how do I balance the information obtained from the first few games, with whatever else I know (or at least believe) about the team.

Books I've Read In 2015

This is a somewhat boring post; simply listing the books I have read in 2015. They are listed in approximate order of totally subjective importance, interest, and general awesomeness. I will keep this post updated as I add more books. Over the next few months, I will probably write some posts discussing the more interesting of these books.

Thoughts on "Between the World and Me"

I recently finished the most powerful book I have read in years: "Between the World and Me" by Ta-Nehesi Coates. Frankly, it is not the type of book I often read; it is short and contains almost no numbers or statistics. Instead, the book is letter from the author to his son on the experience of being a black man in America today. The specifics of his story are powerful, the philosophizing is important, and the writing is quite beautiful (something which I rarely dwell on).

What is evanometrica?

Now that I have completed my PhD, I find myself with some excess free time. In this free time, I am rediscovering old hobbies, such as reading for fun, and identifying new hobbies, such as playing with computers. Because I find these activities so interesting, I figure others might also.

In my final year of graduate school, I spent hour after hour writing and rewriting. I hope I picked up a skill; one that I don't think I was very good at previously. In my current job, I don't have to write very much. In an attempt to make sure my skills don't degrade, I figured I would start a blog. 

So what exactly is the blog about? Well, its about evanometrica: the data, statistics, and economics of all things evan. I am still not exactly sure what this will mean, but I do have a couple of ideas.  I imagine posts will be broken down into roughly four frequently over-lapping categories: data, economics, books, and sports.