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This is already not going over well among the people this “law” is aiming to “help” … not surprising:

Salary Threshold for Hourly Employees to Change

The U.S. Department of Labor has announced changes to how some employees are paid by announcing an increase to the salary threshold used to determine eligibility for overtime pay. Effective December 1, 2016, those who earn below $47,476 a year (with the exception of physicians, attorneys, and employees whose primary role is teaching) will be classified as non-exempt employees, who are paid on an hourly basis and are eligible for overtime. Non-exempt employees are required to use HRMS to track their time worked and also their paid leave time. Employers have been given six months to implement the change, and the University will use that time to assess the best ways forward to allow for full compliance. More information on the transition will be provided in a timely fashion. An FAQ will be made available through @Rochester within the next few days to explain the changes and how they will affect employees. Affected employees and their managers will receive further information directly from HR before the new pay scheme goes into effect later this year. Employees who have further questions should contact the appropriate HR Business Partner.

One in Ten Million!

Rare events … are NOT rare.

The modern internet has mated with the basic economics of risk and produced a frankensteinian beast. Suppose there is some awful thing that happens to people with a miniscule chance – say, once every 10 million times. It can be a car spontaneously combusting, the earth swallowing you up whole, or some awful disease striking you.

This is a rare event, and upon seeing it happen to someone your heart would break and your mind would shutter. However, in a huge world of 7 billion+ people, these miniscule events would actually happen quite regularly. In fact, we would expect to see these “rare” one-in-ten million events strike 700 times (per year, assuming an annual risk), or twice per day. With a good enough internet connection and decent enough access to modern cell phones, these twice per day awful events would hit our collective 7 billion phones quite regularly, leading everyone to think that these super-rare and horrible events are actually quite common. Where people go once they make that observation is a story for another day of course.

One reason I think some folks are suspicious of geoengineering solutions to climate change or biotechnology solutions to feeding people and clothing people is that these are not solutions where we “pay for our sins.” If humans perceive “easy” technical fixes to problems, that will not only make us less nervous about what future damages we may end up doing to the planet and ourselves, but it also undermines the calls for massive changes to our consumption patterns today and of course undermines the case for major political changes as well.

  1. The U.S. is 100% reliant on imports, from Canada, of Rubidium. Time to invade.
  2. Come to think of it … I AM a paid shill … for students.
  3. I wonder why private college tuition isn’t much lower?
  4. What is the right social cost of carbon to use for policy purposes?
  5. Acemoglu on automation and the future of labor markets and inequality.
  6. Is it cheap to mitigate CO2?

Seen in Dubai


Had to pick up a birthday present and card for one of the kids’ birthday parties this weekend. Used a self-service kiosk to checkout. On the way home, I fancied a quite cup of coffee. Ordered it via a kiosk. Then, headed to my gas station where I filled up my own gas. Finished up the trip by depositing cash and checks, with no envelope, directly into an ATM machine.

Russ R. has an interesting podcast this week with Pedro Domingos on Machine Learning. Part of their discussion surrounds how knowledge intensive companies develop algorithms that either send you advertisements or help you choose things to purchase. The most common examples are the Ads that run in Google that are instantly customized to the end user and the “recommended” book selections when you are browsing and buying books on Amazon.

I have long complained about buying books on Amazon, though I have spent thousands of dollars on books there in the last decade, because I can’t “browse the shelves” to see what else is around. Part of my favorite thing about bookstores (and beer stores!) is having the ability to wander around, see if something new or different piques my interest, or to compare what else is on the shelves in the categories I am looking at. There have been moments where I have started down the paths of entirely new disciplines by doing this sort of thing.

The downside of Amazon is that you can’t easily browse the stacks to see what is near the book you are looking at. They have computer algorithms that examine the books you have browsed and the ones you have bought and compares it to thousands of other customers and it makes all kinds of suggestions for things you might like. There are millions of books to possibly choose from, and since you cannot obviously self-browse this massive stack, something needs to help parse it.

I used to think this was good, then bad, and then good.

Compare this to an old bookstore. My former bias was that Amazon was inferior because I couldn’t see the other books, and to be honest, there are more books “in my line of sight” at a bookstore than grab my attention on a computer screen.

But is Amazon really inferior? I used to push back because “a computer” made selections for me, and did not allow me to see what the computer did not pick out. How silly? For sure, the computer did narrow down what came to my eyeballs, but if I start searching on other books, then the computer will start sending me different selections. But ultimately how is this any different than what is happening at the bookstore? Booksellers have to select which books to buy and stock, then where in the store to display them, and then how to display them in those particular location. And since there is obviously a dearth of actual physical space, the bookseller is not able to offer me very much in the way of options. At most, they may have 1,000 possible books that I might ever consider buying.

Is what Amazon does any different? Is the “computer” really “picking” what I look at? Of course not. The algorithms are developed based on what other real human beings actually like and do. And my best judgment is that physical booksellers are doing exactly the same thing. They do need to stay in business, right? So while I might wish that they pulled the little read classics and featured them on shelves for me, or that they offered in depth tracts on arcane subjects, those things just don’t sell. So the motivation of the bookseller does not seem to be very different than Amazon. The same is true for when we see “ratings” and “recommendations” posted in bookstores, and featured shelves – is that really any better than what Amazon offers?

My simple point(s) is that no matter how you shop, there is always going to be some institution that parses what is displayed to you. What styles a brewer likes to brew limits the available beer choices you have when you show up to a brewery. What pens are most popular limits the offerings when you head to the stationary stores. The same is true of media content. There never was a time when you had the world at your fingertips and had an easy time parsing through it. Walk into the Library of Congress and tell me if that experience is the best way to choose what to read. When you have everything available to you, in no particular order, that is not much better than having little available to you – you will quickly develop your own internal algorithm to help you navigate your way through the millions of volumes.

Now, can Amazon do better? I am sure of it. I would like it if I could tweak the algorithm better. Just because I want to buy book X does not mean that my tastes in books are like other people who like book X. We may have lots of different reasons for liking or wanting to read a particular book. With better computing power maybe algorithms could delve the depths of the thousands of books on our wish lists and compare them to purchases and also enable us to edit and discuss the features of works that are important to us. Maybe the algorithm already does that. And the potential for machine algorithms to do better than human algorithms is its advantage. The correct comparison is not whether the machines are going to get it perfect, but whether they do even a little bit better job than the people.

Of course, I am on my way to … the Fairport Library right now.

  1. Forcing people to vote … only … forces people to vote. No improvement in election outcomes. I find it hilarious that we can call it a “democracy” if you are forced to vote.
  2. Why Dodd-Frank will fail? The history of deposit insurance shows that it increases systemic risk, and of course, is instituted as a fairly well-hidden subsidy to certain classes of citizens. This is banking dog bites man stuff.
  3. Lee Ohanian in the next volley of academic ping-pong (i.e. supply vs. demand side shocks in explaining the GD and GR)
  4. Will John List be the next experimentalist to win a Nobel Prize?
  5. A “free” way to improve K12 performance?  The mechanisms are of interest.
  6. The value of a good teacher goes beyond test scores – glad to see someone actually trying to incorporate this more formally.

The hosts used at Catholic Mass, i.e. the Sacramental Bread, are made from wheat (and yeast, and water, and salt, and even some holy water!).

Quiz question: What percentage of the acreage of wheat planted around the world for commercial/consumption purposes is GMO wheat?

Answer here. Be nice to Mom today. And every day.

Serious Question

Does anyone actually store money under their mattress?

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