From the Shifting baseline’s project a strong video on the emptying of the ocean of fish:
via Dot Earth
See also World’s Fair on the Great Pacific Trash Vortex.
Archive for September, 2008
Richard Selley an Emeritus Professor at Imperial College London has written a book The Winelands of Britain: past, present and prospective, that describes how climate, geology, and culture have shaped wine growing in the UK.
He projects that climate change will destroy the wine producing potential of current wine producing areas of the UK, such as the Thames Valley, and the Severn valley. From an Imperial college press release:
…if the climate changes in line with predictions by the Met Office’s Hadley Centre, by 2080 vast areas of the UK including Yorkshire and Lancashire will be able to grow vines for wines like Merlot and Cabernet Sauvignon which are currently only cultivated in warmer climates like the south of France and Chile.
Different grape varieties flourish in different temperatures, and are grouped into cool, intermediate, warm and hot grape groups. For the last 100 years ‘cool’ Germanic grape varieties have been planted in British vineyards to produce wines like Reisling. In the last 20 years some ‘intermediate’ French grape varieties have been successfully planted in southeast England, producing internationally prize-winning sparkling white wines made from Pinot Noir, Pinot Meunier and Chardonnay.
Combining temperature predictions from the IPCC and the Met Office’s Hadley Centre with his own research on UK vineyards throughout history, Professor Selley predicts that these cool and intermediate grape varieties will be confined to the far north of England, Scotland and Wales by 2080, with ‘warm’ and ‘hot’ varieties seen throughout the midlands and south of England.
Explaining the significance of his new study, Emeritus Professor Selley from Imperial’s Department of Earth Science and Engineering, said: “My previous research has shown how the northernmost limit of UK wine-production has advanced and retreated up and down the country in direct relation to climatic changes since Roman times.
“Now, with models suggesting the average annual summer temperature in the south of England could increase by up to five degrees centigrade by 2080, I have been able to map how British viticulture could change beyond recognition in the coming years. Grapes that currently thrive in the south east of England could become limited to the cooler slopes of Snowdonia and the Peak District.”
Some recent reflections on systemic risk and the financial markets - ranging from details to the big picture.
First, Gretchen Morgenson in the New York Times writes Behind Insurer’s Crisis, Blind Eye to a Web of Risk:
“It is hard for us, without being flippant, to even see a scenario within any kind of realm of reason that would see us losing one dollar in any of those transactions.”— Joseph J. Cassano, a former A.I.G. executive, August 2007
…Although America’s housing collapse is often cited as having caused the crisis, the system was vulnerable because of intricate financial contracts known as credit derivatives, which insure debt holders against default. They are fashioned privately and beyond the ken of regulators — sometimes even beyond the understanding of executives peddling them.
Originally intended to diminish risk and spread prosperity, these inventions instead magnified the impact of bad mortgages like the ones that felled Bear Stearns and Lehman and now threaten the entire economy.
In the case of A.I.G., the virus exploded from a freewheeling little 377-person unit in London, and flourished in a climate of opulent pay, lax oversight and blind faith in financial risk models. It nearly decimated one of the world’s most admired companies, a seemingly sturdy insurer with a trillion-dollar balance sheet, 116,000 employees and operations in 130 countries.
Second, America’s National Public Radio’s Planet Money has a lot of recent indepth coverage of the financial crisis in this vein available of podcasts. Including a recent one called the day America’s economy almost died.
Looking a more the big economic picture, Predicting Crisis in the United States Economy a profile of Nouriel Roubini discusses the selective vision of models and the biases against discontinuities or nonlinear change.
Recessions are signal events in any modern economy. And yet remarkably, the profession of economics is quite bad at predicting them. A recent study looked at “consensus forecasts” (the predictions of large groups of economists) that were made in advance of 60 different national recessions that hit around the world in the ’90s: in 97 percent of the cases, the study found, the economists failed to predict the coming contraction a year in advance. On those rare occasions when economists did successfully predict recessions, they significantly underestimated the severity of the downturns. Worse, many of the economists failed to anticipate recessions that occurred as soon as two months later.
The dismal science, it seems, is an optimistic profession. Many economists, Roubini among them, argue that some of the optimism is built into the very machinery, the mathematics, of modern economic theory. Econometric models typically rely on the assumption that the near future is likely to be similar to the recent past, and thus it is rare that the models anticipate breaks in the economy. And if the models can’t foresee a relatively minor break like a recession, they have even more trouble modeling and predicting a major rupture like a full-blown financial crisis. Only a handful of 20th-century economists have even bothered to study financial panics. (The most notable example is probably the late economist Hyman Minksy, of whom Roubini is an avid reader.) “These are things most economists barely understand,” Roubini told me. “We’re in uncharted territory where standard economic theory isn’t helpful.”
Finally, Science Fiction writer Charlie Stross writes about the increasing difficulty of projecting the near future at all:
We are living in interesting times; in fact, they’re so interesting that it is not currently possible to write near-future SF.
… Put yourself in the shoes of an SF author trying to construct an accurate (or at least believable) scenario for the USA in 2019. Imagine you are constructing your future-USA in 2006, then again in 2007, and finally now, with talk of $700Bn bailouts and nationalization of banks in the background. Each of those projections is going to come out looking different. Back in 2006 the sub-prime crisis wasn’t even on the horizon but the big scandal was FEMA’s response (or lack thereof) to Hurricane Katrina. In 2007, the sub-prime ARM bubble began to burst and the markets were beginning to turn bearish. (Oh, and it looked as if the 2008 presidential election would probably be down to a fight between Hilary Clinton and Rudy Giuliani.) Now, in late 2008 the fiscal sky is falling; things may not end as badly as they did for the USSR, but it’s definitely an epochal, historic crisis.
Now extend the thought-experiment back to 1996 and 1986. Your future-USA in the 1986 scenario almost certainly faced a strong USSR in 2019, because the idea that a 70 year old Adversary could fall apart in a matter of months, like a paper tiger left out in a rain storm, simply boggles the mind. It’s preposterous; it doesn’t fit with our outlook on the way history works. (And besides, we SF writers are lazy and we find it convenient to rely on clichés — for example, good guys in white hats facing off against bad guys in black hats. Which is silly — in their own head, nobody is a bad guy — but it makes life easy for lazy writers.) The future-USA you dreamed up in 1996 probably had the internet (it had been around in 1986, in embryonic form, the stomping ground of academics and computer industry specialists, but few SF writers had even heard of it, much less used it) and no cold war; it would in many ways be more accurate than the future-USA predicted in 1986. But would it have a monumental fiscal collapse, on the same scale as 1929? Would it have Taikonauts space-walking overhead while the chairman of the Federal Reserve is on his knees? Would it have more mobile phones than people, a revenant remilitarized Russia, and global warming?
There’s a graph I’d love to plot, but I don’t have the tools for. The X-axis would plot years since, say, 1950. The Y-axis would be a scatter plot with error bars showing the deviation from observed outcomes of a series of rolling ten-year projections modeling the near future. Think of it as a meta-analysis of the accuracy of projections spanning a fixed period, to determine whether the future is becoming easier or harder to get right. I’m pretty sure that the error bars grow over time, so that the closer to our present you get, the wider the deviation from the projected future would be. Right now the error bars are gigantic.
Philosopher Peter Singer writes a newspaper editorial Tuberculosis or Hair Loss? Refocusing Medical Research:
… the diseases that cause nine-tenths of what the World Health Organization refers to as “the global burden of disease” are getting only one-tenth of the world’s medical research effort. As a result, millions of people die every year from diseases for which no new drugs are in the pipeline, while drug companies pour billions into developing cures for erectile dysfunction and baldness.
…If drug companies target diseases that affect only people who are unable to pay high prices for drugs, they cannot expect to cover their research costs, let alone make a profit. No matter how much their directors may want to focus on the diseases that kill the most people, the current system of financial incentives means that if they did so, their shareholders would remove them, or their companies would soon be out of business. That would help no one. The problem is with the system, not with the individuals who make their choices within it.
At a meeting in Oslo in August, Incentives for Global Health, a nonprofit organization directed by Aidan Hollis, professor of economics at the University of Calgary, and Thomas Pogge, professor of philosophy and international affairs at Yale, launched a radical new proposal to change the incentives under which corporations are rewarded for developing new medicines. They suggest that governments contribute to a Health Impact Fund that would pay pharmaceutical companies in proportion to the extent to which their products reduce the global burden of disease.
…Hollis and Pogge estimate that about $6 billion a year would be required to enable the Fund to provide a sufficient incentive for drug companies to register products that target the diseases of the poor. That figure would be reached if countries accounting for one-third of the global economy – say, most European nations, or the United States and one or two small affluent nations – contributed 0.03 % of their gross national income, or three cents for every $100 they earn. That’s not a trivial sum, but it isn’t out of reach, especially considering that affluent nations would also benefit from cheaper drugs and from medical research that was focused on reducing disease rather than on maximizing profits.
Renzo Piano’s California Academy of Sciences Blooms and Grows, Balancing Man and Nature
Mr. Piano’s vision avoids arrogance. The ethereality of the academy’s structure suggests a form of reparations for the great harm humans have done to the natural world. It is best to tread lightly in moving forward, he seems to say. This is not a way of avoiding hard truths; he means to shake us out of our indolence.
See a slide show of the Academy and from Flickr photos.
Also, from Pruned:
The New York Times visits Alan Berger and gets a tour of his reclamation project in the Pontine Marshes. Says Berger, “The solution has to be as artificial as the place. We are trying to invent an ecosystem in the midst of an entirely engineered, polluted landscape.” Much earlier, The New York Times tagged along with the landscape architect and his class to a severely polluted mining area in Colorado.
Agriculture increases the supply of food supplied by an ecosystem, but often decreases its ability to supply other services. The same appears to be true for salmon aquaculture. In the Toronto Globe and Mail, Vancouver journalist Mark Hume reports Declining salmon runs blamed for wilderness tourism slump:
All along the B.C. Coast, wilderness tourism operators who run bear-viewing, whale-watching and sport-fishing resorts are reporting tough times because of declining salmon runs.
But the biggest impact may be occurring in the Broughton Archipelago, where Mr. MacKay operates, and where pink salmon runs have all but vanished, sending a shock wave through the region’s ecosystem.
“Some of the northern pods are just not here,” Mr. MacKay said yesterday. “And we’ve had three occasions [this summer] when we did not see any orcas at all. That’s pretty weird.”
He said northern killer whales visit the area during the summer months, collecting in big social gatherings where breeding takes place.
“When they get together like that it’s called Super Pod Day, and we will see over 100 dorsal fins out there at a time,” Mr. MacKay said. “That didn’t happen this year, for the first time since we’ve been collecting data, which is almost 30 years.”
Mr. MacKay said it’s not coincidental that the whales have vanished along with the salmon.
“It’s pretty simple. …What do you think these orcas eat?” he said.
Surveys by the Department of Fisheries and Oceans indicate pink salmon stocks have fallen to extremely low levels in the Broughton Archipelago. In Glendale Creek, a key indicator stream, there have been only 19,000 spawners counted this year, compared with 264,000 last year.
Pink salmon, which usually spawn in prodigious numbers, are a keystone species on the West Coast. Chinook salmon, the mainstay of the orca diet, feed on young pinks, while grizzly and black bears depend on spawning adult pink salmon to bulk up for hibernation.
…
Brian Gunn, president of the Wilderness Tourism Association, said the collapse of salmon stocks is threatening the survival of ecotourism businesses.
“The bear-viewing businesses, the whale-watching operations, they built up a lot of equity showing people these wild animals. Now the fish aren’t there and they are seeing their equity drain away. …If the salmon go, so does the wildlife, and so does the business.”
Mr. Gunn blamed the fish-farming business, saying a heavy concentration of net pens in the Broughton Archipelago has created sea-lice epidemics which kill young salmon.
The New York Times presents an interactive visualization of US financial institution losses (about $1 trillion dollars over the past year).
Nassim “Black Swan” Taleb writes on Edge about an unwillingness or consider and remember extreme events leads to financial disaster in The Fourth Quadrant: A map of the limits of statistics.
Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let’s face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).
The current subprime crisis has been doing wonders for the reception of any ideas about probability-driven claims in science, particularly in social science, economics, and “econometrics” (quantitative economics). Clearly, with current International Monetary Fund estimates of the costs of the 2007-2008 subprime crisis, the banking system seems to have lost more on risk taking (from the failures of quantitative risk management) than every penny banks ever earned taking risks. But it was easy to see from the past that the pilot did not have the qualifications to fly the plane and was using the wrong navigation tools: The same happened in 1983 with money center banks losing cumulatively every penny ever made, and in 1991-1992 when the Savings and Loans industry became history.
It appears that financial institutions earn money on transactions (say fees on your mother-in-law’s checking account) and lose everything taking risks they don’t understand. I want this to stop, and stop now— the current patching by the banking establishment worldwide is akin to using the same doctor to cure the patient when the doctor has a track record of systematically killing them. And this is not limited to banking—I generalize to an entire class of random variables that do not have the structure we thing they have, in which we can be suckers.
And we are beyond suckers: not only, for socio-economic and other nonlinear, complicated variables, we are riding in a bus driven a blindfolded driver, but we refuse to acknowledge it in spite of the evidence, which to me is a pathological problem with academia. After 1998, when a “Nobel-crowned” collection of people (and the crème de la crème of the financial economics establishment) blew up Long Term Capital Management, a hedge fund, because the “scientific” methods they used misestimated the role of the rare event, such methodologies and such claims on understanding risks of rare events should have been discredited. Yet the Fed helped their bailout and exposure to rare events (and model error) patently increased exponentially (as we can see from banks’ swelling portfolios of derivatives that we do not understand).
Are we using models of uncertainty to produce certainties?
…So the good news is that we can identify where the danger zone is located, which I call “the fourth quadrant”, and show it on a map with more or less clear boundaries. A map is a useful thing because you know where you are safe and where your knowledge is questionable. So I drew for the Edge readers a tableau showing the boundaries where statistics works well and where it is questionable or unreliable. Now once you identify where the danger zone is, where your knowledge is no longer valid, you can easily make some policy rules: how to conduct yourself in that fourth quadrant; what to avoid.
…
Now it lets see where the traps are:
First Quadrant: Simple binary decisions, in Mediocristan: Statistics does wonders. These situations are, unfortunately, more common in academia, laboratories, and games than real life—what I call the “ludic fallacy”. In other words, these are the situations in casinos, games, dice, and we tend to study them because we are successful in modeling them.
Second Quadrant: Simple decisions, in Extremistan: some well known problem studied in the literature. Except of course that there are not many simple decisions in Extremistan.
Third Quadrant: Complex decisions in Mediocristan: Statistical methods work surprisingly well.
Fourth Quadrant: Complex decisions in Extremistan: Welcome to the Black Swan domain. Here is where your limits are. Do not base your decisions on statistically based claims. Or, alternatively, try to move your exposure type to make it third-quadrant style (”clipping tails”).
Below I’ve redrawn Taleb’s figure. His article provides a fuller picture.
Similarly, Scientific American reprints Benoit Mandelbrot’s 1999 How Fractals Can Explain What’s Wrong with Wall Street:
Individual investors and professional stock and currency traders know better than ever that prices quoted in any financial market often change with heart-stopping swiftness. Fortunes are made and lost in sudden bursts of activity when the market seems to speed up and the volatility soars. Last September, for instance, the stock for Alcatel, a French telecommunications equipment manufacturer, dropped about 40 percent one day and fell another 6 percent over the next few days. In a reversal, the stock shot up 10 percent on the fourth day.
The classical financial models used for most of this century predict that such precipitous events should never happen. A cornerstone of finance is modern portfolio theory, which tries to maximize returns for a given level of risk. The mathematics underlying portfolio theory handles extreme situations with benign neglect: it regards large market shifts as too unlikely to matter or as impossible to take into account. It is true that portfolio theory may account for what occurs 95 percent of the time in the market. But the picture it presents does not reflect reality, if one agrees that major events are part of the remaining 5 percent. An inescapable analogy is that of a sailor at sea. If the weather is moderate 95 percent of the time, can the mariner afford to ignore the possibility of a typhoon?
The 2007/2008 UN Human Development Report Fighting climate change: Human solidarity in a divided world focuses on the inequalities of climate change, as well as providing its usual indicators of human development.
According to the report, in 2007 the most developed countries are Iceland, Norway, Australia, Canada, and Ireland while the least developed (of the countries ranked) are Mali, Niger, Guinea-Bissau, Burkina Faso, and Sierra Leone).
The high developed countries are responsible most of the accumulation of greenhouse gases driving climate change, while the low development countries are most vulnerable to the impacts of climate change (see also previous post on climate inequalities).
Now the report has inspired an exhibit at the UN - One planet, one chance. Magnum photos produced a video for the exhibit, which uses gripping photos to describes the basic inequalities of climate change. The video is below the break.
Continue reading ‘A climate change and development slide show’
Many species of bamboo flower and then die in synchronized cycles. These cycles have substantial ecological impacts, and in post cyclone Burma appear to be contributing to famine. From the Guardian Cyclone, starvation - now plague of rats devastates Burmese villages
Four months after Cyclone Nargis devastated Burma, another natural disaster has struck the country. This time the ruling military regime has had 50 years to prepare for it, yet it has still proved unable and unwilling to respond.
The disaster, known in Burma as maudam, is caused by a cruel twist of nature. Once every 50 years or so the region’s bamboo flowers, producing a fruit. The fruit attracts hordes of rats, which feed on its seeds. Some believe the rich nutrients in the seeds cause the rodents to multiply quickly, creating an infestation. After devouring the seeds, the rats turn on the villagers’ crops, destroying rice and corn. In a country once known as the rice bowl of Asia, thousands of villagers are on the brink of starvation.
The last three cycles of flowering occurred in 1862, 1911 and 1958, and each time they were followed by a devastating famine. The current maudam is proving just as disastrous.
The same sequence of events - bamboo flowering, fruit, rats, and then famine - occurred earlier this year in the mountains of Bangladesh.