Author: David Wright

Another perspective on the big market turn in the early 00s:

consider what happened to the European insurance industry in 2002. European insurers are allowed to invest much more in equities than their U.S. counterparts can. (Berkshire Hathaway (BRK.A:NYSE) is an interesting exception here.) As the bull market of the 1990s came to an end, European insurers found themselves flush with surplus from years of excellent stock-market returns, and adequate, if declining, underwriting performance. The fat years had led to sloppiness in underwriting from 1997 to 2001.

During the bull market, many of the European insurers let their bets ride and did not significantly rebalance away from equities. Running asset policies that were, in hindsight, very aggressive, they came into a period from 2000 to 2002 that would qualify as the perfect storm: large underwriting losses, losses in the equity and corporate bond markets and rating agencies on the warpath, downgrading newly weak companies at a time when higher ratings would have helped cash flow. In mid-2002, their regulators delivered the coup de grace, ordering the European insurers to sell their now-depressed stocks and bonds into a falling market. Sell they did, buying safer bonds with the proceeds. Their forced selling put in the bottom of the stock and corporate bond markets in September and October of 2002. Investors with sufficient financial slack, like Warren Buffett, were able to wave in assets at bargain prices.

From this awesome piece by David Merkel.

When discussing that turn, everyone points to the claims costs ticking up and 9/11. But it takes more than that to turn a market.

That’s the result of this bombing that will affect your lives and mine.

Wait for the regulations to come out that say a minimum of 5 police officers and k-9 unit for every mile of race distance, or something similar.

Somebody pays for that. The cost of marathons just became prohibitive.

If we all came to see our political opponents not as nonsensical fools but as basically reasonable speakers of another language, we would not elect the demagogues we do now, or watch the same clowns on cable news.

From this review of Arnold Kling’s new book, which (sigh) I hope to read.

The biggest problem with maintaining such ancient computer systems is that the original technicians who knew how to configure and maintain them have long since retired or passed away, so no one is left with the knowledge required to fix them if they break.

That’s from this amusing article on ancient computer systems still in use today (how about the company in Texas using punchcards!).

In Asimov’s Foundation series we’re introduced to the idea of technology beyond the understanding of anyone that uses it. Its use spawns a religion around the ‘magic’ it performs.  God forbid it breaks!

I’m also reminded of Kevin Kelly’s book on technology:

I told him it would take me a half hour to find a tool, an invention that is no longer being made anywhere by anybody.

Go ahead, he said. Try.

If you listen to our Morning Edition debate, I tried carbon paper (still being made), steam powered car engine parts (still being made), Paleolithic hammers (still being made), 6 pages of agricultural tools from an 1895 Montgomery Ward & Co. Catalogue (every one of them still being made), and to my utter astonishment, I couldn’t find a provable example of an technology that has disappeared completely.

As much as 96% of software in China is pirated.

More here and here, on the topic of the hopeful eradication of Internet Explorer 6, perhaps the worst program ever created.

Ten years ago I was in Guangzhou, China, and next in line for a bus ticket. As the teller came free some lady comes out of nowhere, brushes past me and glides up to the wicket. Like I wasn’t even there.

I was astonished. And infuriated, but what could I do? I turned to my guide (from Hong Kong) who didn’t even wait for the question: “they don’t queue, here,” his lip curled in disgust. Queuing being the height of civilization, this only confirmed to my HK Chinese friend that mainlanders are a backward bunch.

I think about what annoys me about this episode and three things come to mind:

1. Someone broke the rules and I got effed over
2. There was nothing I could do about it
3. As an uncommunicative foreigner, I couldn’t even shame the perpetrator for breaking the rule. I was helpless.

To me this is exactly what road rage is. As your avatar on road, a car can’t tell someone to go f$%# himself. Or, more likely, a car can’t shoot someone a dirty look after they cut you off. Better still, the other person’s car can’t exactly look back, realize it cut you off, blush and sheepishly shuffle its feet.

We live in an incredibly complex social world with incredibly rich communication technology (body language, facial expression, language, etc) for enforcing its norms. And the urge to keep your neighbors in line is powerful.

Road rage is when that urge is frustrated and compounded with a feeling of helplessness.

File under “Holy Cow”.

Visual image reconstruction allows us to reconstruct images that a person sees, displaying complex perceptual contents as they are represented in the brain….

In this study, we developed a method to reconstruct visual images by combining local image decoders that predict local image contrast at multiple spatial scales, using brain activity patterns measured by fMRI scanners. Using this method, we can reconstruct arbitrary visual images, including geometric figures and alphabet letters, which are not used to train the decoders.

More here with an astonishing video of what a person is seeing.

Online learning is quickly gaining in importance in U.S. higher education, but little rigorous evidence exists as to its effect on student learning outcomes. In “Interactive Learning Online at Public Universities: Evidence from Randomized Trials,” we measure the effect on learning outcomes of a prototypical interactive learning online (ILO) statistics course by randomly assigning students on six public university campuses to take the course in a hybrid format (with machine-guided instruction accompanied by one hour of face-to-face instruction each week) or a traditional format (as it is usually offered by their campus, typically with 3-4 hours of face-to-face instruction each week).

We find that learning outcomes are essentially the same—that students in the hybrid format “pay no price” for this mode of instruction in terms of pass rates, final exam scores, and performance on a standardized assessment of statistical literacy. These zero-difference coefficients are precisely estimated. We also conduct speculative cost simulations and find that adopting hybrid models of instruction in large introductory courses have the potential to significantly reduce instructor compensation costs in the long run.

From this study. My bias here is that learning is, above all, linked to motivation to learn and that is rarely affected by the classroom environment.

Learning requires engagement, requires presence, which mostly comes from within. To me this is what great teachers do differently: they grab students’ attention and direct it to the material.

Without that gift, much of teaching is replaceable.

Great piece on LeBron’s transformation after the 2011 Finals.

First you need humiliation.

According to Spoelstra, “It took the ultimate failure in the Finals to view LeBron and our offense with a different lens. He was the most versatile player in the league. We had to figure out a way to use him in the most versatile of ways — in unconventional ways. It seems like a ‘duh’ moment now, but we had to go through the experiences and failures together.”

…That loss, and maybe some of those demeaning characterizations, fueled one of the greatest and most important transformations in recent sports history. James was distraught, but somehow channeled that into ferocious dedication to his craft. Spoelstra was perplexed and desperate to correct course; he told me, “Shortly after our loss to Dallas in the Finals, LeBron and I met. He mentioned that he was going to work on his game relentlessly during the offseason, and specifically on his post-up game. This absolutely made sense for us. We had to improve offensively, and one of the best ways would be to be able to play inside-out with a post-up attack.”

Then you need a plan:

It’s no secret where and when James first worked on his low-post game. Fueled by that loss to the Mavs, he went to Houston in the summer of 2011 to learn from a master: Hakeem Olajuwon.

“I wanted to get better,” James said of his decision to work with Olajuwon. “I wanted to improve and I sought out someone who I thought was one of the greatest low-post players to ever play this game. I was grateful and happy that he welcomed me with open arms; I was able to go down to Houston for four and a half days; I worked out twice a day; he taught me a lot about the low post and being able to gain an advantage on your opponent.

Then you need to grind and grind and grind:

I used that the rest of the offseason, when I went back to my hometown. Every day in the gym I worked on one thing or I worked on two things and tried to improve each and every day.”

Here’s a good David Brooks column on forecasting:

Tetlock and company gathered 3,000 participants. Some got put into teams with training, some got put into teams without. Some worked alone. Some worked in prediction markets. Some did probabilistic thinking and some did more narrative thinking. The teams with training that engaged in probabilistic thinking performed best. The training involved learning some of the lessons included in Daniel Kahneman’s great work, “Thinking, Fast and Slow.” For example, they were taught to alternate between taking the inside view and the outside view.

Suppose you’re asked to predict whether the government of Egypt will fall. You can try to learn everything you can about Egypt. That’s the inside view. Or you can ask about the category. Of all Middle Eastern authoritarian governments, what percentage fall in a given year? That outside view is essential.

…In the second year of the tournament, Tetlock and collaborators skimmed off the top 2 percent of forecasters across experimental conditions, identifying 60 top performers and randomly assigning them into five teams of 12 each. These “super forecasters” also delivered a far-above-average performance in Year 2. Apparently, forecasting skill cannot only be taught, it can be replicated.

There’s a lot there and do read it in conjunction with the supremely impressive *Thinking Fast and Slow*. In that book, Kahneman tells a story about how he and a team of academics were going to write a new psychology curriculum.

After doing a bit of preparatory work on the textbook, Kahneman decided to poll the team members’ forecasts for how long it might take to finish the thing. Two years or so was the central estimate with a range between 1.5 and 2.5.

Then I had another idea. I turned to Seymour [who was surveyed in the original group that said two years, remember -DW], our curriculum expert, and asked whether he could think of other teams similar to our that had developed a curriculum from scratch. Seymour said he he could think of quite a few. I then asked whether he knew the history of these teams in some detail, and it turned out that he was familiar with several. I asked him to think of these teams when they had made as much progress as we had. How long, from that point, did it take them to finish their textbook projects?

He fell silent. When he finally spoke, it seemed to me that he was blushing, embarrassed by his own answer: “you know, I never realized this before, but in fact not all the teams at a stage comparable to ours ever did complete their task. A substantial fraction of the teams ended up failing to finish the job.”

…My anxiety risking, I asked how large he estimated that fraction was: “about 40%”, he answered… “Those who finished, I asked, “How long did it take them?” “I cannot think of any group that finished in less than seven years”.

“When you compare our skills and resources to those of other groups, how good are we?” “We’re below average”, he said, “but not by much.”

Forecasting is subject to a hurricane of cognitive bias.

Much of it comes down, I think, to the fact that the benefit of a good forecast, being right, is normally not realized for a while. The short term cost of unpopular or awkward or ‘stupid’ forecasts, on the other hand, is real and immediate and painful.

Humans have high discount rates. Unless the long term payoff is awesome, forecasting will always be about something other than being right.