He allows himself to be hired by anyone, for nearly any purpose — not involving physical contact — as long as they pay his hourly wage: a mere 1,000 yen (about US $9). And he loves it.
With gray hair, visible lines on his face and loss of youthful slimness, he is more like a free-spirited bohemian in a strange disguise.
Throughout an hourlong Skype interview, in which comments are tediously ferried back and forth through an interpreter, his energy and enthusiasm never flag, and his answers grow more expressive and thoughtful with each question.
It’s all part of his job as a rented “ossan,” the Japanese word for a middle-aged man.
“Forty percent of my ossan rental clients want something to do with the violin,” Sasaki said. “Another 40% are questions about IT work, and the other 20% are asking advice for their lives. These are mainly younger people.
“My profile on the ossan rental website has a very light-hearted atmosphere,” he said. Though he notes his occupation in IT, he bills himself as someone who plays the violin and shogi, or Japanese chess.
Sequestered capital is capital that is hidden or unseen by the market. R&D is often sequestered capitaI. New goods in production can be sequestered capital. Sequestered capital is special because it doesn’t inform price signals. In a series of papers, McClure and Thomas use the idea of sequestered capital to explain market anomalies. In this paper they look at sequestered capital and the Dutch tulipmania.
Framing tulipmania in terms of sequestered capital – capital whose quantities, usages and future yields are hidden from market participants – offers a richer and more straightforward explanation for this famous financial bubble than extant alternatives. Simply put, the underground planting of the tulip bulbs in 1636 blindfolded seventeenth-century Dutch speculators regarding the planted quantities and their development and future yields. The price boom began in mid November 1636, coinciding with the time of planting. The price collapse occurred in the first week of February 1637, coinciding with the time of bulb sprouting – signaling bulb quantities, development and future yields. Also consistent with our explanation is the initial price collapse location, in the Dutch city of Haarlem, where temperature and geography favored early sprouting and sprout visibility.
In a working paper (with Steve Horwitz) they look at sequestered capital and closed end funds. Sequestered capital is an interesting idea perhaps with many other applications.
These are originally derived from written notes, a basis for comments by somebody else, from a closed session on tech. I have added my own edits:
- Most tech leaders aren’t especially personable. Instead, they’re quirky introverts. Or worse.
- Most tech leaders don’t care much about the usual policy issues. They care about AI, self-driving cars, and space travel, none of which translate into positive political influence.
- Tech leaders are idealistic and don’t intuitively understand the grubby workings of WDC.
- People who could be “managers” in tech policy areas (for instance, they understand tech, are good at coalition building, etc.) will probably be pulled into a more lucrative area of tech. Therefore ther is an acute talent shortage in tech policy areas.
- The Robespierrean social justice terror blowing through Silicon Valley occupies most of tech leaders’ “political” mental energy. It is hard to find time to focus on more concrete policy issues.
- Of the policy issues that people in tech do care about—climate, gay/trans rights, abortion, Trump—they’re misaligned with Republican Party, to say the least. This same Republican party currently rules.
- While accusations of deliberate bias against Republicans are overstated, the tech rank-and-file is quite anti-Republican, and increasingly so. This limits the political degrees of freedom of tech leaders. (See the responses to Elon Musk’s Republican donation.)
- Several of the big tech companies are de facto monopolies or semi-monopolies. They must spend a lot of their political capital denying this or otherwise minimizing its import.
- The media increasingly hates tech. (In part because tech is such a threat, in part because of a deeper C.P. Snow-style cultural mismatch.)
- Not only does tech hate Trump… but Trump hates tech.
- By nature, tech leaders are disagreeable iconoclasts (with individualistic and believe it or not sometimes megalomaniacal tendencies). That makes them bad at uniting as a coalition.
- Major tech companies have meaningful presences in just a few states, which undermines their political influence. Of states where they have a presence — CA, WA, MA, NY — Democrats usually take them for granted, Republicans write them off. Might Austin, TX someday help here?
- US tech companies are increasingly unpopular among governments around the world. For instance, Facebook/WhatsApp struggles in India. Or Google and the EU. Or Visa and Russia. This distracts the companies from focusing on US and that makes them more isolated.
- The issues that are challenging for tech companies aren’t arcane questions directly in and of the tech industry (such as copyright mechanics for the music industry or procurement rules for defense). They’re broader and they also encounter very large coalitions coming from other directions: immigration laws, free speech issues on platforms, data privacy questions, and worker classification on marketplaces.
- Blockchain may well make the world “crazier” in the next five years. So tech will be seen as driving even more disruption.
- The industry is so successful that it’s not very popular among the rest of U.S. companies and it lacks allies. (90%+ of S&P 500 market cap appreciation this year has been driven by tech.) Many other parts of corporate America see tech as a major threat.
- Maybe it is hard to find prominent examples of the great good that big tech is doing. Instagram TV. iPhone X. Amazon Echo Dot. Microsoft Surface Pro. Are you impressed? Are these companies golden geese or have they simply appropriated all the gold?
You know the drill — where to go, what to do, and what to eat? I thank you in advance for your wisdom and advice.
Darth Vader Is in Demand at Summer Weddings
Forget flower girls. Couples want stormtroopers throwing petals, and Vader leading the congo line.
There is, however, a shortage. Note this:
Disney forbids the garrisons from participating in certain events without approval, such as gatherings that promote a local business or professional sporting events. Weddings are allowed because they’re considered “community service.”
The 501st has had to adopt an unofficial list of rules to narrow the number of wedding requests. That includes sufficient space to get dressed in costume and having drinking water available on hot days. The plastic and rubber costumes offer no ventilation. “They’re basically death traps,” said Mr. Johnson, who recently returned from a Star Wars event in Singapore where three people dressed as stormtroopers passed out from the heat.
That is a new paper by Mikko Packalen and Jay Bhattacharya, here is the abstract:
The National Institutes of Health (NIH) plays a critical role in funding scientific endeavors in biomedicine that would be difficult to finance via private sources. One important mandate of the NIH is to fund innovative science that tries out new ideas, but many have questioned the NIH’s ability to fulfill this aim. We examine whether the NIH succeeds in funding work that tries out novel ideas. We find that novel science is more often NIH funded than is less innovative science but this positive result comes with several caveats. First, despite the implementation of initiatives to support edge science, the preference for funding novel science is mostly limited to work that builds on novel basic science ideas; projects that build on novel clinical ideas are not favored by the NIH over projects that build on well-established clinical knowledge. Second, NIH’s general preference for funding work that builds on basic science ideas, regardless of its novelty or application area, is a large contributor to the overall positive link between novelty and NIH funding. If funding rates for work that builds on basic science ideas and work that builds on clinical ideas had been equal, NIH’s funding rates for novel and traditional science would have been the same. Third, NIH’s propensity to fund projects that build on the most recent advances has declined over the last several decades. Thus, in this regard NIH funding has become more conservative despite initiatives to increase funding for innovative projects.
In 2003, Johnson and Goldstein published what would become a famous paper in Science, Do Defaults Save Lives? The paper featured a graph which showed organ donor consent rates in opt-in countries versus those in opt-out countries. The graph is striking because it seems to suggest that a simple change in the default rule can create a massive change in organ donor rates and thus save thousands of lives.
The graph, however, does NOT show organ donor rates. It shows that in opt-in countries few people explicitly opt-in and in presumed consent countries few people explicitly opt-out. But when a potential organ donor dies the families of people in opt-in countries who did not opt-in are still asked whether they would like to donate their loved one’s organs and many of them say yes. Similarly, in the presumed consent countries the families of people who did not opt-out are still typically asked whether they would like to donate their loved one’s organs and some of them say no.
The actual difference in organ donation rates between opt-in and presumed consent countries is much smaller than the differences in the graph, as Johnson and Goldstein made clear later in their paper. Nevertheless, the simple story in the graph encouraged many people to put excess weight on presumed consent as the solution to low organ donor rates.
The best estimates of presumed consent suggested that switching to presumed consent might increase organ donor rates by 25%. 25% isn’t bad! But we don’t have many examples of countries that have switched from one system to another so that estimate should be taken with a grain of salt.
The latest evidence comes form Wales which switched to presumed-consent in 2013. Unfortunately, there has been no increase in donation rates.
The most significant analysis of the new system is the Impact Evaluation Report, released by the Welsh Government in November 2017. Whilst focusing on the positives, such as increased understanding among medical staff, the report cannot escape the donation statistics, which clearly show no improvement. Covering the period from January 2010 or January 2011 to September 2017, all donation data show no change since the legislation’s introduction. The 21-month period before the Act came into effect saw 101 deceased donors, whereas the same period after showed 104; an increase, but one that can be properly attributed to expected annual fluctuation.
I still favor presumed consent or better, mandated choice, but I don’t think the binding constraints on organ donation are default rules. More important are preferences and fears about donation, the existence of a professional system using people who are trained to ask for donations, an institutional organization that can use donations when they are available (minimizing waste), and, of course, incentives.
Hat tip: Frank McCormick.
Models developed for gross domestic product (GDP) growth forecasting tend to be extremely complex, relying on a large number of variables and parameters. Such complexity is not always to the benefit of the accuracy of the forecast. Economic complexity constitutes a framework that builds on methods developed for the study of complex systems to construct approaches that are less demanding than standard macroeconomic ones in terms of data requirements, but whose accuracy remains to be systematically benchmarked. Here we develop a forecasting scheme that is shown to outperform the accuracy of the five-year forecast issued by the International Monetary Fund (IMF) by more than 25% on the available data. The model is based on effectively representing economic growth as a two-dimensional dynamical system, defined by GDP per capita and ‘fitness’, a variable computed using only publicly available product-level export data. We show that forecasting errors produced by the method are generally predictable and are also uncorrelated to IMF errors, suggesting that our method is extracting information that is complementary to standard approaches. We believe that our findings are of a very general nature and we plan to extend our validations on larger datasets in future works.
That is from A. Tacchella, D. Mazzilli, and L Pietronero in Nature. Here is a Chris Lee story about the piece. Via John Chamberlin.
Here is the whole post, here is one excerpt:
If you’re 10–20: These are prime years!
- Go deep on things. Become an expert.
- In particular, try to go deep on multiple things. (To varying degrees, I tried to go deep on languages, programming, writing, physics, math. Some of those stuck more than others.) One of the main things you should try to achieve by age 20 is some sense for which kinds of things you enjoy doing. This probably won’t change a lot throughout your life and so you should try to discover the shape of that space as quickly as you can.
- Don’t stress out too much about how valuable the things you’re going deep on are… but don’t ignore it either. It should be a factor you weigh but not by itself dispositive.
- To the extent that you enjoy working hard, do. Subject to that constraint, it’s not clear that the returns to effort ever diminish substantially. If you’re lucky enough to enjoy it a lot, be grateful and take full advantage!
- Make friends over the internet with people who are great at things you’re interested in. The internet is one of the biggest advantages you have over prior generations. Leverage it.
- Aim to read a lot.
- If you think something is important but people older than you don’t hold it in high regard, there’s a decent chance that you’re right and they’re wrong. Status lags by a generation or more.
- Above all else, don’t make the mistake of judging your success based on your current peer group. By all means make friends but being weird as a teenager is generally good.
No, I am not kidding. His first roller coaster. Paul’s expression at the end is priceless. I haven’t seen him so ready to throw up since he debated Ron Paul. Personally, I’d prefer to do Economists in Cars Getting Coffee but the fact that this last only 2 minutes is probably for most people a bonus.
This paper explores the physics of the what-if question “what if the entire Earth was instantaneously replaced with an equal volume of closely packed, but uncompressed blueberries?”. While the assumption may be absurd, the consequences can be explored rigorously using elementary physics. The result is not entirely dissimilar to a small ocean-world exo-planet.
Here is the full analysis, via M.