As an AI researcher, I think I am required to have an opinion about this. Here's what I have to say to the various tribes.
AI-pessimists: please remember that the Luddites have been wrong about technology causing economic cataclysm every time so far. We're talking about several consecutive centuries of wrongness.1 Please revise your confidence estimates downwards.
AI-optimists: please remember that just because the pessimists have always been wrong in the past does not mean that they must always be wrong in the future. It is not a natural law that the optimists must be right. That labor markets have adapted in the long term does not mean that they must adapt, to say nothing of short-term dislocations. Please revise your confidence estimates downwards.
Everyone: many forms of technology are substitutes for labor. Many forms of technology are complements to labor. Often a single form of technology is both simultaneously. It is impossible to determine a priori which effect will dominate.2 This is true of everything from the mouldboard plough to a convolutional neural network. Don't casually assert AI/ML/robots are qualitatively different. (For example, why does Bill Gates think we need a special tax on robots that is distinct from a tax on any other capital equipment?)
As always, please exercise cognitive and epistemic humility.
I am aware of the work of Gregory Clark and others related to Industrial Revolution era wage and consumption stagnation. If a disaster requires complicated statistical models to provide evidence it exists, I say its scale can not have been that disastrous. [↩]
Who correctly predicted that the introduction of ATMs would coincide with an increase in employment of bank tellers? Anyone? Anyone? Beuller? [↩]
Here is a question to think about. If religions help to create social capital by allowing people to signal conscientiousness, conformity, and trustworthiness [as Norenzayan claims], how does this relate to Bryan Caplan’s view that obtaining a college degree performs that function?
That might explain why the credentialist societies of Han China were relatively irreligious. Kling likes to use the Vickies/Thetes metaphor from Neal Stephenson's Diamond Age, and I think this dichotomy could play well with that. Wouldn't the tests required by the Reformed Distributed Republic fill this role, for instance?
This is by far the best, simplest explanation of Coase's insights that I have read. Having read plenty of Landsburg, that should not — indeed does not — surprise me.
His final 'graph is a digression, but a good point:
Coase’s Nobel Prize winning paper is surely one of the landmark papers of 20th century economics. It’s also entirely non-technical (which is fine), and (in my opinion) ridiculously verbose (which is annoying). It’s littered with numerical examples intended to illustrate several different but related points, but the points and the examples are so jumbled together that it’s often difficult to tell what point is being illustrated... Pioneering work is rarely presented cleanly, and Coase was a true pioneer.
And this is why I put little stock in "primary sources" when it comes to STEM. The intersection between people/publications who originate profound ideas and people/publications which explain profound ideas well is a narrow one. If what you want is the latter, don't automatically mistake it for the former. The best researchers are not the best teachers, and this is true as much for papers as it is for people.
Start a font by tweaking all glyphs at once. With more than twenty parameters, design custom classical or experimental shapes. Once prototyping of the font is done, each point and curve of a glyph can be easily modified. Explore, modify, compare, export with infinite variations.
(Okay, so technically this may not belong on a "reading list.") Duncan previously created The History of Rome podcast, which is one of my favorites. Revolutions is his new project, and it just launched. Get on board now.
This would be a great starting place for high-school or freshmen STEM curricula. As a bonus, it has this nice epigraph from Richard Hamming:
"In science, if you know what you are doing, you should not be doing it. In engineering, if you do not know what you are doing, you should not be doing it. Of course, you seldom, if ever, see either pure state."
I'm at the tail end of a doctoral program and going on the job market. This is good advice. What's disappointing is that this would have been equally good and applicable advice for people going on the job market back when I started grad school. The fact that we're five years (!!) down the road and we still have need of these sorts of "surviving in horrid job markets" pieces is bleak.
All programming blogs need at least one post unofficially titled “Indisputable Proof That I Am Awesome.” These are usually my favorite kind of read, as the protagonist starts out with a head full of hubris, becomes mired in self-doubt, struggles on when others would have quit, and then ultimately triumphs over evil (that is to say, slow or buggy computer code), often at the expense of personal hygiene and/or sanity.
I'm a fan of the debugging narrative, and this is a fine example of the genre. I've been wrestling with code for mapping projections recently, so I feel Miller's pain specifically. In my opinion the Winkel Tripel is mathematically gross, but aesthetically unsurpassed. Hopefully I'll find some time in the next week or so to put up a post about my mapping project.
Social cohesion can be thought of as a manifestation of how "iterated" people feel their interactions are, how likely they are to interact with the same people again and again and have to deal with long term consequences of locally optimal choices, or whether they feel they can "opt out" of consequences of interacting with some set of people in a poor way.
Munger links to some very good analysis but it occurs to me that what is really needed is the variance of grades over time and not just the mean. (Obviously these two things are related since the distribution is bounded by [0, 4]. A mean which has gone from 2.25 to 3.44 will almost certainly result in less variance here.)
I don't much care where the distribution is centered. I care how wide the distribution is — that's what lets observers distinguish one student from another. Rankings need inequality. Without it they convey no information.
I share Graur's and Tabarrok's wariness over "high impact false positives" in science. This is a big problem with no clear solutions.
The Graur et al. paper that Tabarrok discusses is entertaining in its incivility. Sometimes civility is not the correct response to falsehoods. It's refreshing to see scientists being so brutally honest with their opinions. Some might say they are too brutal, but at least they've got the honest part.
McCaffrey is completely right. But good luck to him reasoning people out of an opinion they were never reasoned into in the first place.
I do like the neologism "sustainable pricing" that he introduces. Bravo for that.
I would add a sixth reason to his list: accusations of "price gouging" are one rhetorical prong in an inescapable triple bind. A seller has three MECE choices: price goods higher than is common, the same as is common, or lower than is common. These choices will result in accusations of price gouging, collusion, and anti-competitive pricing, respectively. Since there is no way to win when dealing with people who level accusations of gouging, the only sensible thing to do is ignore them.
"Betting Therapy" should be a thing. You go to a betting therapist and describe your fears — everything you're afraid will happen if you do X — and then the therapist offers to bet money on whether it actually happens to you or not. After you lose enough money, you stop being afraid.
This is a good compendium. Nothing too ground-breaking here, but Vanderbilt does cover a lot of ground.
I especially liked that Vanderbilt addressed self-driving cars. Traffic was published in 2009; I didn't expect then that producers would have made as much progress towards autonomous vehicles as they have in the last four years. I am more optimistic about overcoming regulatory hurdles than I was then, but I still believe those will be bigger obstacles than any technological difficulties.
I find any serious discussion of congestion, mass transit, electric vehicles, hybrids, land use, urban planning, fuel usage, carbon emissions, etc. pretty pointless if it doesn't consider the transformative effects of autonomous vehicles. Planning a new highway or commuter rail line that's supposed to be useful for the next fifty years without considering robo-cars feels like some 1897 Jules Verne-esque proto-steampunk fantasy that predicts the next century will look just like the last one except it will have more telegraphs and longer steam trains. You might as well be sitting around in a top hat and frock coat micromanaging where you'll be putting all the stables and coal bunkers for the next five generations, oblivious to Messrs Benz, Daimler, Peugeot et al. motoring around on your lawn.
I think you can wrap most of the problems of traffic congestion up into several short, unimpeachable statements:
Costs can take the form of both money and time.
Lowering the cost of something means people will do more of it, ceteris paribus.
Reducing traffic congestion reduces the time-cost of driving.
The reduced cost of driving causes people to want to drive more, raising traffic congestion again.
Unless someone can show me one of those four statements is incorrect, I'm comfortable concluding that traffic is here to stay for the foreseeable future.
Plenty people think they have the cure for congestion: roundabouts, light rail, "livable communities," bike sharing, HOV lanes, high-density residences, abolishing free parking, mileage fees, congestion fees, etc. Some of these are good ideas, and some aren't. But I'm not taking anyone who claims to solve (or even alleviate) the traffic problem seriously unless they can address how their solution interacts with #1-4 above.
For some of the proposals the resolution is simple: they lower the time-cost but explicitly raise the monetary cost (e.g. congestion pricing, market-based rates for parking). Others don't have such an easy time of it. But either way, I'd like people to at least be able to address how they would break out of this feedback loop.
PS I once sat through an hour-long keynote by an eminent professor from MIT Sloan on modeling market penetration of alternative fuel vehicles. Half of his talk ended up being about gas shortages, both in the 1970s and after Hurricane Sandy. At no point in those thirty minutes did he once mention the word "price"! Everything I had heard about the distinction between freshwater and saltwater economics snapped into focus.
Don Boudreaux discussing Armen Alchian's preference for clear prose over "mathematical pyrotechnics" reminded me of a few neural networks researchers I know. I won't name names, because it wasn't a favorable comparison. There's far too much equation-based whizz-bangery going on in some papers.
I use to think the problem was insufficient sophistication in my own math background, but I've recently heard independently from two very smart people in our Applied Math/Scientific Computing program that they also find the math in a lot of these papers to be more of an obfuscating smoke screen than a clarifying explication. If they find it hard to follow I've got good reason to believe the problem isn't just me.