Tag: autonomous cars

A Tweet, A Reply, And So A Blogpost

It goes without saying that I do not know enough about the details, but I certainly treat this tweet as good news. And in case you missed reading about it, there’s also this from last month.

It is remarkable how much progress we’re making in the medical field, and based on what little I understand of the developments over the last two to three years, we’re only getting started.
But it was a reply to this tweet that had my EFE antennae really and truly perk up:

There’s so much to analyse in that short little tweet!

  • Autonomous cars are coming – they’ve “been coming” for a long time, it is true. But whenever they do, y’know, actually come, will they make the world a better place or not?
  • We can (and do) worry about what impact this will have on employment, car ownership patterns, parking lots within cities and lots of other things. But what about fatalities?
  • Do I mean fatalities caused by having autonomous cars, or fatalities avoided because we have autonomous cars? Well, the net effect, of course.
  • This tweet makes the claim that fatalities will, on net, go down because of autonomous cars. Maybe you agree, maybe you don’t. But especially if you do not, I would argue that you should focus on not just newspaper reports about deaths caused by autonomous cars, but also check to see if fatality statistics drop as autonomous cars become more prevalent. This is where a carefully designed econometric analysis can be truly useful. Counterfactuals really and truly matter!
  • But let’s assume, for the moment, that fatality statistics will actually come down. If they do, surely that’s a good and wonderful thing?
  • But ah, TANSTAAFL! What this tweet is really getting at is the opportunity cost of a reduction in fatalities as a consequence of greater deployment of autonomous cars. That is, the author of the tweet assumes that fatalities will come down with autonomous cars… but then asks about some of the second order effects.
  • And one second order effect, he says, is that we simply will not have as many organs up for donation as we used to earlier. Fewer fatalities by definition means fewer deaths (which is awesome), but it also means lesser organs up for donation (which is not so awesome)
  • And so we need to get a move on in biomedical sciences, and make sure we figure out how to grow organs suitable for human transplants.
  • Have fun going further out on this limb if you are a student of economics. Imagine, for example, what a world with abundant organs for transplants might look like. Will people end up being less careful about their health? Is that a good thing or a bad thing?
  • You might be tempted to say it is a bad thing. But consider this: will not this cavalier attitude towards health lead to greater demand for better quality of transplants and at lower prices?
  • Note that I have no clue what the “correct” answer is! I’m simply trying to point out that simple applications of simple economic concepts can help you frame better and more thought-provoking questions.

A (surprising) profile, a surprising result,a Maharaja(h) in the Yorkshire Dales, Driverless Cars and (non)ergodicity

Can you guess what this article is about, who has written it, and when?

The dirty little secret on Wall Street is that the men responsible for its current reputation were not exceptionally bad. They were just ordinary people placed in unusual circumstances.

“Knowing somebody” to “get the job done” is older than you thought, is applicable in more places than you’d expect, and last across a longer time horizon than you’d have expected. Well, I don’t know about you, but each of these was true in my case.

The main empirical analysis of this article compares a snapshot of the location of mission stations in Africa in 1903 to the precise locations of projects funded by the World Bank in 1995–2014. The unit of analysis is derived from a grid of 55km×55km square cells covering the African mainland and Madagascar. The results imply that the presence of (at least) one mission station increases the probability that an area is allocated a development project by approximately 50 percent.

A rather macabre excerpt, but to me a revealing one. On “The Maharajah of the Yorkshire Dales”

The first ethnically Indian minister in Britain was Parmjit Dhanda. He too found a rural seat, out in the West Country, in Gloucestershire. People were almost always polite and pleasant to him, but one morning he came out and found a severed pig’s head on the bonnet of his car.

The year was 2010.

Vox explains the current state of affairs when it comes to driverless cars, and how long that might take (short answer? A little bit longer, but no idea exactly how long. Sorry.)

There are two core statistics useful for evaluating how advanced a self-driving car program is. One is how many miles it has driven. That’s a proxy for how much training data the company has, and how much investment it has poured into getting its cars on the road.

The other is disengagements — moments when a human driver has to take over because the computer couldn’t handle a situation — per mile driven. Most companies don’t share these statistics, but the state of California requires that they be reported, and so California’s statistics are the best peek into how various companies are doing.

On both fronts, Google’s sister company Waymo is the clear leader. Waymo just announced 20 million miles driven overall, most of those not in California. In 2018, Waymo drove 1.2 million miles in California, with 0.09 disengagements every 1,000 miles. Coming in second is General Motors’ Cruise, with about half a million miles and 0.19 disengagements per 1,000 miles. (Cruise argues that since it tests its cars on San Francisco’s difficult streets, these numbers are even more impressive than they look.)

A topic that more students of economics should know about: (non)-ergodicity.

First is a very micro level concern: behavioural biases. The whole idea of endowment effects and loses aversion make sense in a world dominated by non-ergodic processes. We hate losing what we have because it decreases our ability to make future gains. Mathematics tells us we should avoid being on one of the many losing trajectories in a non-ergodic process.