Mises’s Interventionism, A Recap

I suspect that Mises may have felt somewhat restless after writing Socialism. He had taken a very good stab at describing the socialist economy and its inadequacy for the promotion of human flourishing. By 1940 fascism had arisen in both Italy and in Germany, who Mises considered the clear antagonists of World War II. Further, the communist Soviets were allied with Germany at the time of writing Interventionism.

A communist-fascist alliance may seem strange to idealogues, but it appeared quite natural to Mises that the two distasteful versions of socialism should find cooperation convenient to achieve their own ends. In America, the revelations of German atrocities had yet to arrive and there were many sympathizers with both Russia and Germany. In Britain, union leaders were promoting the idea of socialism as a reward to the public who would be bearing the costs of the war.

Mises thought that the disfunction of socialism was adequate to describe its ultimate failure as an economic system. However, socialist tendencies were pervasive in the liberal market economies among both idealogues and demagogues enough to make the transition to socialism a very real threat. After all, while socialism may not be a stable regime in a dynamic world, certain features within specific market economies may nonetheless tend toward it. What is the cause of such tendencies?

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Book Review: Big Data Demystified

Last year, our economics department launched a data analytics minor program. The first class is a simple 2 credit course called Foundations of Data Analytics. Originally, the idea was that liberal arts majors would take it and that this class would be a soft, non-technical intro of terminology and history.

However, it turned out that liberal arts majors didn’t take the class and that the most popular feedback was that the class lacked technical challenge. I’m prepping to teach the class and it will have two components. A Python training component where students simply learn Python. We won’t do super complicated things, but they will use Python extensively in future classes. The 2nd component is still in the vein of the old version of the course.

I’ll have the students read and discuss “Big Data Demystified” by David Stephenson. He spends 12 brief chapters introducing the reader to the importance of modern big data management, analytics, and how it fits into an organization’s key performance indicators. It reads like it’s for business majors, but any type of medium-to-large organization would find it useful.

Davidson starts with some flashy stories that illustrate the potential of data-driven business strategies. For example, Target corporation used predictive analytics to advertise baby and pregnancy products to mothers who didn’t even know that they were pregnant yet. He wets the appetite of the reader by noting that the supercomputers that could play Chess or Go relied on fundamentally different technologies.

The first several chapters of the book excite the reader with thoughts of unexploited potentialities. This is what I want to impress upon the students. I want them to know the difference between artificial intelligence (AI) and machine learning (ML). I want them to recognize which tool is better for the challenges that they might face and to see clear applications (and limitations).

AI uses brute force, iterating through possible next steps. There are multiple online tic-tac-toe AI that keep track records. If a student can play the optimal set of strategies 8 games in a row, then they can get the general idea behind testing a large variety of statistical models and explanatory variables, then choosing the best.

But ML is responsive to new data, according to what worked best on previous training data. There are multiple YouTubers out there who have used ML to beat Super Mario Brothers. Programmers identify an objective function and the ML program is off to the races. It tries a few things on a level, and then uses the training rounds to perform quite well on new levels that it has never encountered before.

There are a couple of chapters in the middle of the book that didn’t appeal to me. They discuss the question of how big data should inform a firm’s strategy and how data projects should be implemented. These chapters read like they are written for MBAs or for management. They were boring for me. But that’s ok, given that Stephenson is trying to appeal to a broad audience.

The final chapters are great. They describe the limitations of big data endeavors. Big data is not a panacea and projects can fail for a variety of what are very human reasons.

Stephenson emphasizes the importance of transaction costs (though he doesn’t say it that way). Medium sized companies should outsource to experts who can achieve (or fail) quickly such that big capital investments or labor costs can be avoided. Or, if internals will be hired instead, he discusses the trade-offs between using open source software, getting locked in, and reinventing the wheel. These are a great few chapters that remind the reader that data scientists and analysts are not magicians. They are people who specialize and can waste their time just as well as anyone else.

Overall, I strongly recommend this book. I kinda sorta knew what machine learning and artificial intelligence were prior to reading, but this book provides a very accessible introduction to big data environments, their possible uses, and organizational features that matter for success. Mid and upper level managers should read this book so that they can interact with these ideas prudentially. Those with a passing interest in programming should read it for greater clarity and to get a better handle on the various sub-fields. Hopefully, my students will read it and feel inspired to be on one side or the other of the manager- data analyst divide with greater confidence, understanding, and a little less hubris.

How Zoning Affects Your Home, Your City, and Your Life (a book review)

As you drive, walk, or bike around your city, what do you think about as you see the various buildings and other structures? Perhaps you think about the lives of the people in them, or the architecture of the buildings themselves, or the products and services that the businesses offer for sale. For me, lately I’ve been thinking about one thing as I make my way around town: zoning. It’s not something I had thought about before very much, but after reading Nolan Gray’s new book Arbitrary Lines: How Zoning Broke the American City and How to Fix It, I’ve been thinking about zoning a lot more.

(Disclosure: I know the author of the book, but I paid for my own copy and got it in advance through the luck of the Amazon-pre-order draw.)

The book does a wonderful job of explaining what zoning is (and importantly, also what it is not), where zoning comes from historically (it’s a development of the early 20th century), and how zoning affects our cities. I really like the way that the book encourages the reader to be a part of the story of zoning. In Chapter 2, Gray encourages you to put down the book and locate your city’s zoning map to learn more about how zoning impacts your life.

I immediately did so and had no trouble finding zoning maps for the city I live in, Conway, Arkansas. Conveniently, my city provides both a simple PDF map and an interactive map, which provides a lot more detail. The interactive map even has embedded links with historical information on different pieces of property. For example, I found the ordinance for when my college, the University of Central Arkansas (previously Arkansas State Teachers College), was annexed by the City in 1958. Pretty cool!

Looking over the map, it’s pretty clear that most of the city that I live in is covered by R-1 and R-2 zoning. But what exactly do these designations mean? You can probably guess that “R” designates residential, but what does it proscribe about land use?

For that, you must dig into the zoning ordinances. And as Gray cautions in the book (somewhat tongue-in-cheek), you might not want to get in too deep with your zoning ordinances, since they can run hundreds or thousands of pages. But I was brave enough to do so, and located my zoning code online (the PDF runs a modest 253 pages).

What did I learn about the zoning that covers my city?

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Mises’s Bureaucracy, a Recap

My favorite two economists are Ludwig Von Mises and Milton Friedman. They might consider one another from very different schools of thought, though there is reason to think that they are not so different. As an undergraduate student, I liked them both, but I became more empirics-minded in graduate school and as a young assistant professor.

As I progressed through graduate school and conducted empirical research, my opinions and policy prescriptions changed and were refined from what they once were. In graduate school, I didn’t study Austrian Economics, though it was certainly in the water at George Mason University. Recently, as an assistant professor with a few years under my belt, I picked up Bureaucracy (1944) and read it as a matter of leisure.

One word:

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