Showing posts with label Statistics. Show all posts
Showing posts with label Statistics. Show all posts

Saturday, December 15, 2012

9 February 2012 – Where’s the Yorktown?


The most costly of all follies is to believe passionately in the palpably not true.  - H.L. Mencken
 

Colleagues,

The other day I was reading an article from the July 2011 edition of the Journal Of Military History - "Some Myths of World War II", by Gerhard Weinberg - when one of the passages leapt out at me.  I'll cite it in its entirety for you in a moment, but I wanted to explain first what it was that had caught my eye. 

As those of you who read a line or two into these missives before pressing the delete key have no doubt noticed, I'm a strong proponent of evidence as the core and foundation of science.  Evidence, I've argued in the past, plays the key role in every step in a rigorous scientific endeavour.  The first step in science - observation - involves noticing some sort of data, recording it, and wondering what caused it.  The second step, hypothesization, requires coming up with a thesis that explains and is consistent with one's initial observations.  The third step, experimentation, is an organized search for more evidence - especially evidence that tends to undermine your hypothesis; and the fourth, synthesis, requires the scientist to adapt his hypothesis so that it explains and is consistent with not only the original observed evidence, but also all of the evidence garnered through research and experimentation.  This last criterion is key to the scientific method: if one's hypothesis is not consistent with observed data, it must be changed; and if it cannot be changed to account for observations, it must be discarded.  Science is like evolution in that regard; only the fittest hypotheses survive the remorseless winnowing process, and progress requires enormous amounts of time, and enormous amounts of death.  The graveyards of science are littered with extinct theories.

But there's a fifth stage to science, one that tends to be overlooked because it's technically not part of the "holy quaternity" I've outlined above.  The fifth stage is communicating one's conclusions - not only to one's fellow seekers-of-fact, but also to those on whose behalf one's efforts are engaged.  This stage requires telling the whole story of the endeavour, from start to finish, including all of the errors and pitfalls.  Publicizing one's misapprehensions and mistakes is a scientific duty, for two reasons: first, because it improves the efficiency of science by helping one's colleagues avoid the errors of observation and analysis that you've already made (in army terms, "There's no substitute for recce"); but more importantly, because total disclosure of data and methods, and frank, transparent discussion of errors of fact and judgement, are what give science its reliability and rigour.  Transparency and disclosure are the keystone of the scientific method, and are the reason that science works - and the reason, I might add, that science has produced steady progress since the Enlightenment, and has come to be trusted as the only reliable means of understanding the natural world.

The thing is, disclosure of data and methods, and frank discussion of errors of fact and judgement, might be strengths in the scientific field, but in other fields of endeavour, they are often interpreted as weaknesses.  In a competitive business environment, for example, disclosure of data and methods makes it easier for competitors to appropriate or undermine your ideas.  In such a cut-throat atmosphere, the individual who conceals his activities, who refuses to discuss with colleagues (whom he likely views as competitors) what he is doing, and how, and why, will enjoy a comparative advantage vis-à -vis those who are more open about their data, methods and conclusions.  As for discussion of errors of fact and judgement, in a non-scientific atmosphere, the admission that one has made errors in experimental design or in data collection, or the confession that one may have misinterpreted observations or the results of an experiment, may be taken as a sign of incompetence or lack of ability, rather than as an indication of the type of honest transparency that is the hallmark of good science.

Acknowledgement of error, of course, is also key to correcting an erring course of action - and this is true in every area of activity in life, not merely in science.  The first step, as they say, is admitting that you have a problem - and this leads me back to the citation from the article that prompted this line of thought in the first place.  In the article I mentioned above, Weinberg - a distinguished scholar who has been writing about the Second World War for the better part of 70 years - discusses a wide variety of some of the "myths" about some of the well-known leaders of World War II.  When he comes to Admiral Isoroku Yamamoto, one of his more trenchant questions pertains to why the famous naval commander, in preparing for the pre-emptive attack on Pearl Harbour, was "so absolutely insistent on a project with defects that were so readily foreseeable."  Why, for example, would you attack a fleet in a shallow, mud-bottomed harbour, where damaged vessels could be so easily raised and repaired (18 of the 21 ships "severely damaged" at Pearl Harbour eventually returned to service)?  Weinberg suggests that it may have been just as well for Yamamoto's peace of mind that he was dead by the time of the battle of Surigao Strait in October 1944, "when a substantial portion of the Japanese Navy was demolished by six American battleships of which two had allegedly been sunk and three had been badly damaged" three years earlier at Pearl Harbour.  Weinberg speculates that Yamamoto may have been so "personally invested" in his tactical plan for the Pearl Harbour attack that he was "simply unable and unwilling to analyze the matter objectively":

There is an intriguing aspect of the last paper exercise of the plan that he conducted in September 1941, about a month before the naval staff in Tokyo finally agreed to his demand that his plan replace theirs.  In that exercise, it was determined that among other American ships sunk at their moorings would be the aircraft carriers including the Yorktown.  Not a single officer in the room had the moral courage to say, “But your Excellency, how can we sink the ‘Yorktown’ in Pearl Harbor when we know that it is with the American fleet in the Atlantic?”  None of these officers lacked physical courage: they were all prepared to die for the Emperor, and many of them did.  But none had the backbone to challenge a commander whose mind was so firmly made up that none dared prize it open with a touch of reality.[Note A]

Where do we lay the blame for this error of fact?  On Yamamoto, for coming up with a scenario whose fundamental assumptions were known to be wrong?  On his subordinates, who accepted those assumptions, knowing them to be wrong?  Remember, this was not a matter of a difference of opinion between experts; it wasn't a question of differing interpretations of equivocal results.  Nor was this a notional, peace-time exercise; this was an exercise that took place in the work-up stage of what was intended to be the very definition of a critical strategic operation - a knock-out blow that would curtail the ability of the US to interfere with Japan's plans for the Pacific.  In such circumstances, one would think that accuracy of data would be at a premium, and that fundamental errors of fact would be corrected as soon as they were noticed.  But this didn't happen.  This is a very telling anecdote, and Weinberg distils from it a devastatingly important observation: that men who had no fear of death, and who would (and did) fight to their last breath, nonetheless somehow lacked the moral courage to disagree with their superior on a matter of indisputable fact: that the exercise scenario did not reflect the real world.  Where the USS Yorktown actually was (it was conducting neutrality patrols between Bermuda and Newfoundland throughout the summer and autumn of 1941) was in fact very important - and what is more, Japanese intelligence had that information.  Yet no one stepped up to correct the scenario.  No one challenged the planning assumptions when actual data showed those assumptions to be wrong. 

Yorktown's location should have mattered to Yamamoto.  When the Japanese Navy struck Pearl Harbour on 7 December 1941, USS Yorktown was in port in Norfolk, Virginia.  She left Norfolk on 16 December, and was in San Diego by 30 December.  She was later involved in launching the Smiley raids against the Marshall Islands.  Her air wing accounted for numerous Japanese warships and landing craft at the Battle of the Coral Sea. 
 
 
She was also at Midway, where her air wing destroyed the Japanese carriers Soryu and Hiryu, the latter while flying from USS Enterprise after Yorktown was disabled. 
 
 
In less than six months, that one ship accounted for 25% of Japan's carrier strength. 
 
 
Where she was in the fall of 1941 mattered, because none of these things would have happened if Yorktown had been badly damaged or sunk at Pearl Harbour.
 


Why did Yamamoto's exercise plan assume that Yorktown would be among the ships tied up in Battleship Row, when he knew it was on the other side of the world?  Why did no one challenge the scenario?  It's easy, as many historians have done (and not without justification) to write off staff subservience to a commander as the inevitable consequence of centuries of deification of the Emperor, the warrior philosophy of bushido, and decades of Imperial Japanese militarism.  But how can we simultaneously accept that such an organizational entity is at once both a dangerously innovative and adaptive enemy, and yet so rigidly hierarchical that the thought processes of the senior leadership are utterly impervious even to objective fact?  The two qualities, it seems to me, are mutually exclusive.  One cannot be both intellectually flexible and intellectually hidebound.

Yamamoto's refusal to incorporate known facts into his exercise plan, and his subordinates' refusal to insist on their incorporation, resulted in flawed planning assumptions.  Over time, the impact of these flawed assumptions came back to bite them in their collective posteriors, in some cases personally.  But they bit the Empire, too.  In her 1984 treatise on folly, Barbara Tuchman spends most of her time castigating governmental elites for long-term, dogged pursuit of policy contrary to the interests of the polity being governed; but she also investigates the sources of folly.  One of these, upon which she elaborates with characteristic rhetorical verve, is the refusal of those in positions of authority to incorporate new information, including changes in objective facts, into their plans:

Wooden-headedness, the source of self-deception, is a factor that plays a remarkably large role in government.  It consists in assessing a situation in terms of preconceived fixed notions while ignoring or rejecting any contrary signs.  It is acting according to wish while not allowing oneself to be deflected by the facts.  It is epitomized in a historian’s statement about Phillip II of Spain, the surpassing wooden-head of all sovereigns: ‘No experience of the failure of his policy could shake his belief in its essential excellence’. [Note B]

Or to put it more simply, as economist John Maynard Keynes is reputed to have said when he was criticized for changing his position on monetary policy during the Great Depression, "When the facts change, I change my mind.  What do you do, sir?"

I could go on about this at great length, citing innumerable examples (as I expect we all could) but I thought that just one would do - surprisingly, from the field of climate science.  In its 4th Assessment Report, issued in 2007, the IPCC claimed that Himalayan glaciers were receding faster than glaciers anywhere else in the world, and that they were likely to "disappear altogether by 2035 if not sooner."  In 2010, the IPCC researchers who wrote that part of the report confessed that they had based it on a news story in the New Scientist, published in 1999 and derived from an interview with an Indian researcher - who, upon being contacted, stated that his glacial melt estimates were based on speculation, rather than formal research.  Now, according to new, peer-reviewed research based on the results of satellite interferometry - i.e., measured data rather than speculation - the actual measured ice loss from glaciers worldwide is "much less" than has previously been estimated. 

As for the Himalayas - well, it turns out they haven't lost any ice mass at all for the past ten years.(Note C)  As one of the researchers put it, the ice loss from high mountain Asia was found to be "not significantly different from zero." 

Amongst other things, this might explain why satellite measurements show that sea levels, far from rising (as the IPCC said they would), have been stable for a decade, and now appear to be declining.  After all, the amount of Aitch-Two-Oh on Earth is finite, so if the glaciers aren't melting, that would explain why the sea levels aren't rising, non?

Remember, according to the IPCC AR4, under the most optimistic scenario for GHG reductions (i.e., massive reductions in GHG emissions, which certainly haven't happened), sea level would increase by 18-38 cm by the year 2099, which equates to an absolute minimum increase of 1.8 mm per year (see the AR4 SPM, pp 7-8, Figure SPM.5 and Table SPM.1).  According to the IPCC's model projections, over the past decade sea levels should have risen by at least 1.8 cm (on the above chart, that's half the Y-axis, which is 35 mm from top to bottom.  In other words, the right hand end of the curve should be well above the top of the chart).  But according to the most sensitive instruments we've yet devised, sea levels haven't risen at all over the past decade, and for the past three years have been steadily falling.  This isn't a model output; it's not a theory.  Like the non-melting of Himalayan glacier ice, it's observed data.

Why point this out?  Well, our current suite of planning assumptions state that, amongst other things, Himalayan glaciers (along with glaciers all over the world) are melting, and that as a consequence, sea levels are rising.  "Rising sea levels and melting glaciers are expected to increase the possibility of land loss and, owing to saline incursions and contamination, reduce access to fresh water resources, which will in turn affect agricultural productivity." (Note D)  Those assumptions were based on secondary source analysis - basically, the IPCC reports, which were themselves largely based on secondary source analysis (and, as shown above, occasionally on sources with no basis in empirical data) - but when verified against primary research and measured data, they are demonstrably wrong.  In essence, stating that defence planning and operations are likely to be impacted by "rising sea levels" or "melting glaciers" is precisely as accurate as Yamamoto planning to sink the USS Yorktown at Pearl Harbour when everyone knew that it was cruising the Atlantic.

A big part of folly consists of ignoring facts that don't conform to one's preconceptions.  Actual planning or policy decisions may be above our pay grade as mere scientists - but what isn't above our pay grade is our moral and professional obligation to point out the facts, and to remind those who pay us that when facts conflict with theories or assumptions, it's the theories or assumptions that have to change. 

So when we see people basing important decisions on inflammatory rhetoric from news reports – for example, a CBC piece hyping an already-hyped “scientific” study arguing that sea level rises have been “underestimated”...


Some scientists at an international symposium in Vancouver warn most estimates for a rise in the sea level are too conservative and several B.C. communities will be vulnerable to flooding unless drastic action is taken.



The gathering of the American Association for the Advancement of Science heard Sunday the sea level could rise by as little as 30 centimetres or as much as one metre in the next century.



But SFU geology professor John Clague, who studies the effect of the rising sea on the B.C. coast, says a rise of about one metre is more likely.

...it behooves us, as scientists, to look at the data.  And what do the data say in this case? Well, they say that over the past century, the sea level at Vancouver hasn’t risen at all.
 

The average sea level at Vancouver for the past century has been 7100 mm.  If, as the above-cited report claims, Vancouver's sea level had risen by 30 cm, then the average at the right-hand end of the screen should be 7400 mm, close to the top of that chart.  But it's not; it's still 7100 mm. The trend is flat. Absolutely flat.

So as it turns out, according to one of the supposed "key indicators" of anthropogenic climate change is concerned, nothing is happening. If sea levels are the USS Yorktown, the Yorktown isn't at Pearl Harbour at all. She's nowhere near Hawaii. She's not even in the Pacific; she's on the other side of the world, off cruising the Atlantic. But nobody has the moral courage to say so. All of the planning assumptions in use today are firmly fixated on the idea that the Yorktown is tied up at Pearl.

Well, assume away, folks. But no matter how many dive bombers you send to Battleship Row, you are simply not going to find and sink the Yorktown, because the facts don't give a tinker's damn about your assumptions. She is just...not...there.

Of course, finding out the facts – the observed data – is only the first part of our duty as scientists. The next and even more important part is...what do we do with them?  Unlike Yamamoto's juniors, whose ethical code condemned them to obedience, silence and eventually death, our ethical code requires us to sound the tocsin - to speak up when decisions are being made on a basis of assumptions that are provably flawed. And more than that; we shouldn't just be reporting contrarian evidence when we stumble across it, we should be actively looking for it.  A scientific theory can never be conclusively proved; it can only be disproved, and it only takes a single inexplicable fact to disprove it.  Competent scientists, therefore, hunt for facts to disprove their theories - because identifying and reconciling potential disproofs is the only legitimate way to strengthen an hypothesis.  We have to look for things that tend to undermine the assumptions that are being used as the foundation for so much time, effort, and expenditure, because that's the only way to test whether the assumptions are still valid. If we don't do that - if we simply accept the assumptions that we are handed instead of examining them to see whether they correspond to known data, and challenging them if they don't - then we're not doing the one thing that we're being paid to do that makes us different from the vast swaths of non-scientists that surround us. 

Playing the stoic samurai when important, enduring, expensive and potentially life-threatening decisions are being based on demonstrably flawed assumptions is fine if your career plan involves slamming your modified Zero into the side of a battleship and calling it a day.  But if you'd prefer to see your team come out on top, sometimes you've got to call BS.  That should be easier for scientists than it is for most folks, because it's not just what we're paid to do; it's our professional and ethical obligation to speak out for the results of rigorous analysis based on empirical fact.  
 
So when you think about it, "Where's the Yorktown?" is in many ways the defining question that we've all got to ask ourselves: is she where our assumptions, our doctrine, last year's exercise plan, the conventional wisdom, the pseudo-scientific flavour of the month, or "the boss" says she ought to be? 

Or is she where, based on observed data, we know her to be?
How we answer that question when it's posed to us determines whether we are scientists...or something else.

Cheers,

//Don//

Post Scriptum
Today, the USS Yorktown is in 3 miles of water off of Midway Island.  She was rediscovered by Robert Ballard on 19 May 1998, 56 years after she was lost.


For the record, the average sea level at Midway Atoll has increased by about 5 cm since 1947 (65 years). This amounts to 0.74 mm per year, which is less than 1/4 the alleged minimum rate of sea level increase expected due to "climate change", according to the IPCC. And even that tiny increase is due not to "anthropogenic climate change", but rather to isostatic adjustment as the weight of the coral atoll depresses the Earth's crust, and - according to this recent study - to tidal surges associated with storminess that correlates with the increasing Pacific Decadal Oscillation over the past sixty years.

It's all about the evidence, friends. You either have it or you don't. And if you have it, you either try to explain it - like a real scientist - or you ignore it. Ignoring the evidence didn't turn out well at all for Yamamoto, the Imperial Japanese Navy, or in the long run, Imperial Japan.

Notes
Photos courtesy the
US Navy History and Heritage Command.

A) Gerhard L. Weinberg, “Some Myths of World War II – The 2011 George C. Marshall Lecture in Military History”, Journal of Military History 75 (July 2011), 716.

B) Barbara Tuchman, The March of Folly from Troy to Vietnam (New York: Ballantine Books, 1984), 7.

C)
http://www.guardian.co.uk/environment/2012/feb/08/glaciers-mountains

D) The Future Security Environment 2008-2030 - Part 1: Current and Emerging Trends, 36.

Monday, December 10, 2012

Bad Gas

Colleagues,

Last year, when the EPA announced its new fuel efficiency standards for automobiles, I penned this little analysis. I thought I'd post it now, seeing as how Canada, courtesy the irrepressible bureaucrats at Environment Canada, has decided to follow Obama down the rabbit hole.

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Colleagues,

Just a short note today to signal Wednesday's announcement by the White House of new EPA regulations that will require automobiles, by 2025, to achieve a fuel efficiency standard of 54.5 miles per gallon (Note A).

…yeah, okay then.  For the record, the auto industry, and the internal combustion engine that is it's principal component, is over 100 years old.  Automakers have been attempting to improve fuel efficiency for pretty much the whole of that period.  How far, you ask, have they gotten?  Well, according to Ford, the top fuel efficiency of the Model-T - the Flivver, the infamous Tin Lizzie, mass production of which began in September of 1908, a little over 104 years ago - was 25 MPG.  The highway fuel efficiency rating of a 2012 4WD Ford Fusion is…25 MPG.  The latter is naturally a little more comfortable, what with air conditioning, a CD player, and cushioned as opposed to wooden seats, but the fuel efficiency is pretty much the same.

Think I'm joking?  Let's go to the data.  In 1978, a 6-cylinder Jeep CJ got 18.1 MPG on the highway.  In 2011, a Jeep Patriot 2WD, after 33 years of gas crunches, climbing gas prices, and EPA efficiency targets, got 29 MPG on the highway.  Not interested in SUVs?  In 1978, the top fuel efficiency for a production car was 35.1 MPG by the Chevy Chevette (it beat the famously fulminatory Pinto by half-a-MPG) with 35.1 MPG on the highway.  The Chevette, for those of you who never got to see Star Wars in a theatre, was a two-door hatchback weighing in at less than a ton.  A comparable vehicle today?  How about a Honda Civic?  It gets 36 MPG.

I expect you see the pattern.  Let's make it graphical.  Here's US Government data on the MPG characteristics of the current suite of 2012 production vehicles on offer.  Along the Y-axis we have highway fuel efficiency in MPG; along the bottom, vehicular class, as follows: 1=2-seaters, 2=minicompact, 3=subcompact, 4=compact, 5=midsize cars, 6=large cars, 7=small station wagons, 8=midsize station wagons, 9=large station wagons, 10=small 2WD pickups, 11=small 4WD pickups, 12=standard 2WD pickups, 13=standard 4WD pickups, 14=cargo vans, 15=passenger vans, 17=2WD special purpose vehicles, 20=2WD minivans, 21=4WD minivans, 22=2WD SUVs, and 23=4WD SUVs.




2015 is three years away.  Which cars meet the EPA's mandated fuel efficiency targets for that year?  Well, using composite city-highway milage, there are 8 that do (they don't all show up because of overlap on the graph).  Here they are:  The Toyota Prius, the Prius wagon, the Honda Civic Hybrid, the Toyota CT200H, the Ford Fusion Hybrid, the Ford Lincoln MKZ hybrid, the Chevy Volt, and the Toyota Scion iQ.

Guess what they all have in common?  That's right.  They're all hybrids.  Notice something else?  They're all small.  Even the one that nominally rates as a wagon - the Prius v - isn't exactly a battleship.  In other words, it's impossible for today's automakers to make a car that meets the EPA's 2015 fuel efficiency standard without making it both small and a hybrid.

What if we look just UNDER the EPA standard?  Well, the Volkswagon Passat gets 35 MPG combined (it gets 43 MPG on the highway, which is better than every hybrid vehicle except the Prius and the Civic hybrid).  Moreover, it's the same size as the Prius (bigger than the Volt or the Civic).  So what's the difference?  It's in the MSRP, amigos.

 

2012 Volkswagen Passat Sedan - base price $19,995

2012 Chevy Volt - base price $41,545

 

With that kind of price differential, who'd buy a Volt?  Well, would it change your mind if you knew that Dalton McGuinty would give you back $10,000 if you did? (Note B)  That still leaves you paying a $12,000 premium for a smaller vehicle.  At a difference in fuel efficiency of only 5.4% (combined; the Passat is actually 7% more fuel-efficient than the smaller Volt in highway driving), it's going to take a long time to make up $12K.

Now, bearing in mind that the fuel efficiency of the Model-T 100 years ago was 25 MPG, take a look at how many production vehicles currently meet the EPA fuel efficiency target for 2025.  The answer is "none".  The only one that even comes close is the Prius, and it's still 10% shy of the gold standard.  In fact, the only vehicles that presently meet that target are the all-electric Volt and the Tesla - all-electric vehicles that don't burn any fuel at all.

NOW do you get the picture?  The purpose of the EPA's 2025 target is to eliminate the internal combustion engine.

Here's my question: just how likely do you think that is?
 
//Don//
 
Notes

A) [http://www.nationalreview.com/planet-gore/283363/epa-2025-pigs-will-fly-henry-payne]
B) [http://gm-volt.com/2009/07/17/canadian-government-gives-10000-chevy-volt-subsidy-angers-toyota/]

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Just to drive the point home (so to speak), the average increase in fuel efficiency for cars over the past century has been zero. The Ford Fusion gets the same MPG as a Model T. But Obama's EPA - and now Environment Canada - expect auto manufacturers to achieve a 100% increase in fuel efficiency by 2025. That's thirteen years away. The laws of physics haven't changed. The only possible conclusion - the ONLY conclusion - is that this is an ideologically-driven attempt to regulate the internal combustion engine out of existence.

And why? As an auto-da-fe; an act of faith to propitiate the Gods of Carbon Dioxide.  As a little reminder, there has been no statistically significant warming for 16 years despite a 10% increase in atmospheric carbon dioxide concentrations. There is no justification in science or reason for the anthropogenic global warming thesis - and thus no justification in science or reason for costly, pointless government regulations aimed at outlawing one of the fundamental technologies that drive the western world.

A few more words on the Canadian take on this little endeavour. Here's how the government plans to sell the standards change to Canadians.

"These new regulations improve fuel efficiency so that by 2025 new cars will consume 50% less fuel and emit 50% less GHGs than a similar 2008 model, leading to significant savings at the pump," said Environment Minister Peter Kent. "At today's gas prices, a Canadian driving a model year 2025 vehicle would pay, on average, around $900 less per year compared to driving today's new vehicles."
 
Awesome. So at a savings of $900 per annum, it'll only take the average Canadian family thirteen and a half years to pay off the extra $12,000 that their Volt will cost them. Assuming, of course, that they didn't finance the difference, and assuming that the Ontario government can afford to continue subsidizing every Volt that's sold to the tune of $10,000 a pop.

Ontarians buy 45,000-50,000 new passenger cars per month. That's 540,000 - 600,000 new cars per year. If everyone buys Volts - and once these new regulations are in force, that's all that anyone in Ontario will be allowed to buy - then the Ontario government is going to be on the hook for $10,000 x 550,000 = $5,500,000,000 in hybrid car subsidies every year.

Ontario's budget deficit in 2012 was already $15.2 billion. These subsidies would bump that up by a third.  All to support sales of a car that can go at most 80 km on a 10-hour charge, provided it's not too cold.

Can't politicians do arithmetic?

Thursday, December 6, 2012

Doha, dear (A repeat from December 2011)


Colleagues,

In honour of the UN's annual climate alarmapalooza at Doha last week, I thought I'd reprise a little piece I wrote a year ago, after the 17th Climate Kerfuffle at Durban, South Africa - because since then, the only thing that's changed is that there is more empirical evidence than ever that the Sun controls terrestrial climate, and still no empirical evidence whatsoever that carbon dioxide - let alone the 5% of atmospheric CO2 attributable to human activities - plays any role whatsoever in twisting the planetary thermostat.

Enjoy.

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The UN's annual climate conference (17th Conference of Parties, or COP) at Durban wrapped up this past weekend.  I'll spare you all the gory details; basically, delegates kicked the can down the road, agreeing to hammer out a new binding framework agreement by 2015 with provisions for greenhouse gas reductions that would kick in around 2020.  Negotiators painted this as a victory for diplomacy, climate skeptics as a victory for skepticism, and environmental crusaders as a guarantee of apocalypse.

I'm not going to rant and rail about this; I'm simply going to offer 3000 words on it in the form of three pictures.  Charts, actually.  Together, these explain the outcome at Durban.

The first one explains the problem with climate science:

 
The AGW thesis asserts that temperature responses will scale linearly with forcings, the most important of which, according to the IPCC, is anthropogenic carbon dioxide emissions.  However, anthropogenic carbon dioxide emissions account for less than 5% of atmospheric carbon dioxide - and as this chart shows, according to measured data, there is no correlation over the course of the past decade between atmospheric carbon dioxide concentration and average global temperature.  All of the arguments underlying the Kyoto Protocol, and all conceivable successor agreements, are based on linking anthropogenic CO2 to temperature - and yet temperature does not appear to respond to CO2 at all.  That's the first problem.

Here's the second problem:



The data are from British Petroleum.  That's a chart showing the CO2 emissions of the world's top 14 emitters - every country that currently puts out more than 400 million tonnes of CO2 per year.  Only three of them matter: the US (dark blue), whose emissions are declining; China (scarlet), whose emissions are skyrocketing; and India (beige), whose emissions are climbing slowly but accelerating.  Every other major emitter is lower than these three, and is showing stagnation or decline.  Accordingly, any emissions control or reduction agreement that requires reductions from the US or any other western state, but does not require significant (i.e. massively disproportionate) reductions from China and India, is utterly pointless - as is talk of any "carbon debt" owed to the "Third World".

Incidentally, activists at Durban made much of Canada's alleged "delinquency" on the carbon file.  Take a look at the data and explain to me why Canada (a delightful fuschia) is a bigger problem for the environment than, say, China.  Or India.  Or the US.  Or Japan.  Or Germany.  Or for that matter South Africa, whose emissions are at the same level as ours.  And don't be fooled by arguments about "per capita" emissions; that matters to us, but Gaia doesn't care how many people live in your country; she only cares how much CO2 you pump out.

Speaking of "carbon debts", here's the third and last chart:



This is data from Japan's Ibuki satellite, which measures carbon dioxide flux.  What is shows is the daily rate of change in CO2 flux over various parts of the planet, in the four different seasons.  As we all know, most of the world's human-produced CO2 is produced by northern hemisphere countries, because that's where most of the industry is (the only state in the above list that isn't in the northern hemisphere is South Africa).  And we see from this data that the CO2 flux changes massively from season to season.  In high summer, the northern hemisphere is a massive carbon sink.  In autumn, with the exception of the northereastern US states and parts of Siberia, the northern hemisphere is a net CO2 producer.  In the depths of winter, the northern hemisphere - except for the Middle East, the Subcontinent, and Central Asia - is a net CO2 producer; and in the spring, the northern hemisphere, except for Europe and Northeastern Canada, is a net CO2 producer.  Looks pretty simple, doesn't it?  After all, we burn more fuel to stay warm in the autumn, winter, and spring, don't we?

Well, it's not that simple. Take a look at China.  It's the only country that doesn't change colour.  Throughout the year, China is a net CO2 producer.  The oceanic bands are interesting, too; in the winter, when the northern hemisphere is producing a lot of CO2, the North Atlantic is absorbing CO2 like crazy.  This is because cold water can hold more dissolved gas than warm water.  It's also worth noting that the temperate southern hemisphere oceanic bands seem to be absorbing a lot of CO2.

Why does this matter?  Well, take another look at Canada.  We're supposed to be climate criminals - and yet it's the seasons, not human activity, that are the key determinant of our status as a net carbon emitter or consumer.  It's tempting to blame the change on the use of heating fuel, but that explanation just doesn't hold water; if you look at fuel consumption patterns for the US, fuel oil consumption peaks in the winter, when gasoline consumption troughs, and vice-versa - except that US consumers use twice as many barrels of gasoline as they do of fuel oil at any given time.  In other words, you would expect CO2 emissions to be higher in the summer time, when gasoline consumption is high, and fuel oil consumption is low; but summer is when CO2 flux over North America is at its lowest. 

The answer lies in the forests, and in photosynthesis.  When it's summer, the northern hemisphere - the boreal and temperate forest regions - becomes biologically more active and consumes more CO2 than it produces.  In the winter, photosynthetic activity declines, and CO2 emissions climb.  CO2 concentrations, in other words, seem to respond less to fuel consumption patterns than to seasonal variation leading to change in the metabolic rates of plants.

Bottom line is this: as these satellite observations illustrate, we understand an awful lot less about where CO2 comes from and where it goes than we purport to understand. Maybe the caliphs of carbon at Durban [and Doha! -ed.] ought to consider taxing states that don't grow enough trees per capita.

Yeah - can you imagine anyone from Qatar agreeing to that?

Cheers,

//Don//

Thursday, October 18, 2012

11 January 2011 – The impossibility of data management

Colleagues,

Empirical science has four steps: observation, hypothesis, experimentation and synthesis.  Two of those - observation, which requires noting a previously unexplained phenomenon, and experimentation, which is designed to produce evidence - are based, respectively, on collecting and generating data for further analysis.  Data is the bedrock of the scientific method, because it is only through data that established, often cherished, assumptions about how the world works may legitimately be challenged.  So you’d think there couldn’t be any such thing as ‘too much’ data, right?

“Wrong,” says David Weinberger.  In a new book with the wonderfully expressive title Too Big to Know: Rethinking Knowledge Now That the Facts Aren’t the Facts, Experts are Everywhere, and the Smartest Person in the Room is the Room, Weinberger argues that our ability to generate data is outstripping not only our ability to organize it in such a way that we can draw useful conclusions from it, but even our cognitive ability to grasp the complexities of the phenomena we are trying to understand.

This isn’t a new phenomenon.  As Weinberger notes, in 1963 - right around the same time that Edward Lorenz formulated chaos theory to explain the inherent impossibility of predicting the long-term behaviour of non-linear systems like weather - Bernard Forscher of the Mayo Clinic published a letter in Science entitled “Chaos in the Brickyard”, in which he complained that scientists were generating - of all things - too many facts.  According to Weinberger,

...the letter warned that the new generation of scientists was too busy churning out bricks — facts — without regard to how they go together. Brickmaking, Forscher feared, had become an end in itself. “And so it happened that the land became flooded with bricks. … It became difficult to find the proper bricks for a task because one had to hunt among so many. … It became difficult to complete a useful edifice because, as soon as the foundations were discernible, they were buried under an avalanche of random bricks.” [Note A]

The situation today, Weinberger argues, is astronomically - and I use the word “astronomically” quite deliberately, in the sense of “several orders of magnitude” - worse than it has ever been.  In an article introducing his book, he puts the problem thus:

There are three basic reasons scientific data has increased to the point that the brickyard metaphor now looks 19th century. First, the economics of deletion have changed. We used to throw out most of the photos we took with our pathetic old film cameras because, even though they were far more expensive to create than today’s digital images, photo albums were expensive, took up space, and required us to invest considerable time in deciding which photos would make the cut. Now, it’s often less expensive to store them all on our hard drive (or at some website) than it is to weed through them.

Second, the economics of sharing have changed. The Library of Congress has tens of millions of items in storage because physics makes it hard to display and preserve, much less to share, physical objects. The Internet makes it far easier to share what’s in our digital basements. When the datasets are so large that they become unwieldy even for the Internet, innovators are spurred to invent new forms of sharing.  The ability to access and share over the Net further enhances the new economics of deletion; data that otherwise would not have been worth storing have new potential value because people can find and share them.

Third, computers have become exponentially smarter. John Wilbanks, vice president for Science at Creative Commons (formerly called Science Commons), notes that “[i]t used to take a year to map a gene. Now you can do thirty thousand on your desktop computer in a day. [Note A]

The result, Weinberger argues, is actually worse than Forscher predicted.  We are not merely awash in “bricks/facts” and suffering from a severe shortage of “theory-edifices” to organize them with; the profusion of data has revealed to us systems of such massive intricacy and interdependence that we are incapable of visualizing, let alone formulating, comprehensive theories to reduce them to manageable - by which is meant, predictable - rules of behaviour.  Scientists facing “data galaxies” are being forced down one of two paths.  The first path - reductionism - attempts to derive overarching, general principles that seem to work well enough to account for the bulk of the data.  Historically, searching for “universals” amid the vast sea of “particulars” has been the preferred approach of empirical science, largely because there tend to be many fewer universals than there are particulars in the physical world, and also because if you know the universals - for example, Newton’s laws of motion and the fact that gravitational attraction between two objects varies inversely as the cube of the distance separating them - then you can often deduce the particulars, for example, where you should aim your Saturn V rocket in order to ensure that the command and service module achieves Lunar orbit instead of ending up headed for the heliopause.

The second path - modelling - involves the construction of tunable mathematical models that can be tweaked to produce outputs that mimic, as closely as possible, observational data without attempting to derive an overarching theory of how the system that produced the data actually functions.  This has been the preferred approach for dealing with complex phenomena that do not easily lend themselves to reduction to “universals”.  When it comes to complex systems, however, both paths exhibit crippling flaws.  Overarching theories that explain some data, but not all of it, can help us to approach a solution, but they cannot produce “settled science” because unexplained data are by definition the Achilles Heel of any scientific theory.  Meanwhile, modelling, even if it can come close to reproducing observed data, cannot replace observed data, and is never more than a simulation - often a grossly inaccurate one - of how the real world works, because if we cannot visualize all of the complexities and interdependencies of a given problem set, we certainly cannot design mathematical formulae to simulate them.

As if these two unacceptable paths were not bad enough, perhaps the most alarming deduction that Weinberger draws from his analysis is that these data-driven trends are forcing us towards a “new way of knowing” that is, from a human perspective, almost entirely “virtual.”  According to Weinberger, only computers can generate the vast quantities of data necessary to investigate a given phenomenon; only computers have the capacity to organize and store so much data; and only computers can perform the unthinkable number of calculations necessary to process the data and create modelled outputs.  Our role, such as it is, has been to create the mathematical models for the computers - but even this last redoubt of human involvement is collapsing as we become increasingly incapable of visualizing the scope and interrelationships of the problems we are trying to solve.  In other words, the future of research into complex interdependent phenomena is gradually departing the human domain, simply because we lack the cognitive wherewithal needed to cope with it.

Does this mean that scientific inquiry is doomed to leave the human sphere entirely?  Or that it’s destined to grind to a halt unless we can come up with an AI that mimics human cognition to the point of being able to visualize and craft investigative solutions to huge, complex problems?  I certainly don’t dispute that science has developed the ability to drown itself in data; but there are ways of dealing with preposterous quantities of information.  As I believe I’ve pointed out before, historians are accustomed to being drowned in data.  From the point of view of the science of historiography, this is not a new problem.  Take, for example, a relatively straightforward historical event - the Battle of Waterloo.  Working from basic numbers, Napoleon had about 72,000 troops, and the Allies under Wellington had about 118,000.  Reducing the affair to nothing more than a series of iterative diarchic interactions (which I am the first to admit is a wholly ludicrous proposition, but which frankly is no more ridiculously inappropriate than some of the assumptions made in climate modelling - for example, the assumption that clouds warm the Earth, when observed data suggest that they cool it) suggests that there were about 8.5 billion potential diarchic interactions in the first iteration alone.  And that’s only the individuals.  Nothing is too insignificant to be eliminated from your model, and each increase in the fidelity of the data you input ought to help refine the accuracy of your output (unless you’re trying to model a non-linear system, in which case the fidelity of your input doesn’t matter because inherent instabilities will quickly overwhelm the system (see “Nonlinearity andthe indispensability of data”, 15 June 2011)).  In the quest for greater fidelity/realism, you would need to model the characteristics and behaviour not just of each individual but also of their weapons, their clothing, their boots or shoes, their health and physical condition, their horses, their guns, their limbers, their ammunition, the terrain, the obstacles, the weather, the psychological vagaries of individual soldiers and commanders...how many details are we talking about here?  Are we getting to the level of a “non-visualizable” problem yet?

Is it even possible to model something this complex?  Well, it’s sort of already been done.  I’m sure many of you have seen The Lord of the Rings: The Two Towers.  The final battle at Helm’s Deep comprised hundreds of Rohirrim and Elves, and ten thousand Uruk-Hai - and yet there were no more than a hundred live actors in any of the shots.  The final digital effects were created by Weta Digital using a programme called “Massive”.  According to Stephen Regelous, the chap who programmed the battle sequences, he didn’t really “program” them at all, or at least, not in the sense of crafting specific movements for every digital effect in the scenes.  That would have been an impossibly daunting task, given the number of individual digital actors involved.

Instead, he made the digital actors into people.  “The most important thing about making realistic crowds”, Regelous explains, “is making realistic individuals.”  To do that, the programme creates “agents”, each of which is in essence an individual with individual characteristics, traits, and most important of all, volition:

In Massive, agents’ brains - which look like intricate flow charts - define how they see and hear, how fast they run and how slowly they die. For the films, stunt actors’ movements were recorded in the studio to enable the agents to wield weapons realistically, duck to avoid a sword, charge an enemy and fall off tower walls, flailing.

Like real people, agents’ body types, clothing and the weather influence their capabilities. Agents aren’t robots, though. Each makes subtle responses to its surroundings with fuzzy logic rather than yes-no, on-off decisions. And every agent has thousands of brain nodes, such as their combat setting, which has rules for their level of aggression. When an animator places agents into a simulation, they are released to do what they will. It’s not crowd control, but anarchy. Each agent makes decisions from its point of view. [Note B]

In other words, outcomes aren’t predetermined; the Agents are designed with a range of options built into their makeup, and a degree of choice about what to do in response to given stimuli.  Kind of like people.  While it’s possible to predict likely responses to certain stimuli, it’s not possible to be certain what a given Agent will do in response to a given event.  “It’s possible to rig fights, but it hasn’t been done,” Regelous says. “In the first test fight we had 1,000 silver guys and 1,000 golden guys. We set off the simulation, and in the distance you could see several guys running for the hills.”

If you happen to own the full-up Uber-GeekPathetic Basement-Dwelling Fan-Boy LOTR collection (I admit nothing!), you can watch the “Making Of” DVDs for Two Towers, and listen to a much more in-depth explanation of how chaotic, complex and unpredictable the behaviour of the Massive-generated battle sequences were.  The programmers ran the Helm’s Deep sequence many, many times, using the same starting conditions, and always getting different results.  If that sounds familiar, it’s because it’s exactly what Lorenz described as the behaviour of a non-linear system.  It’s chaos in a nutshell. 

According to the DVD explanation, the results of individual volition were potentially so unpredictable that the Agents’ artificial intelligence needed a little bit of tweaking to ensure that the battle scenes unfolded more or less in line with the script.  Early on, for example, the designers had given each Agent a very small probability of panicking and fleeing the battle, along with a slight increase to that probability if a neighbouring agent went down hard, and a slightly greater probability if a neighbouring agent panicked and fled.  Reasonable, right?  We all know that panic on the battlefield is contagious.  The problem is that, depending on how the traits of the Agents were programmed, it could be too contagious.  In one of the simulation runs for the battle, one of the Uruk-Hai Agents near the front lines apparently panicked and fled at exactly the same time as a couple of nearby Agents were shot and killed.  This sparked a massive, rapidly propagating wave of panic that resulted in Saruman’s elite, genetically-engineered army dropping their weapons and heading for the hills.  I’m sure King Theoden, Aragorn, and hundreds of unnamed horse-vikings and elf-archers would’ve preferred it if Jackson had used that particular model outcome for the film, but it probably wouldn’t have leant itself to dramatic tension. And it would've been something of a disappointement when Gandalf, Eomer and the Rohirrim showed up as dawn broke across the Riddermark on the fifth day, only to discover that there was no one left for them to fight because Uruk Spearman #9551 had gone wobbly a couple of hours before.

What’s the point of all this?  Well, the link between Forscher’s complaint about the profusion of data-bricks and Weinberger’s conclusion that computers are taking over the process of generating, storing, and figuring out what to make of data is obvious.  The problem of deriving “universals” from the colossal mass of “particulars” that make up modern scientific (and historical) inquiry ought to be obvious, too, and it’s demonstrated by one of the key weaknesses of history: experimental repeatability.  Steve Regelous could re-run the Battle of Helm’s Deep as many times as he liked until he got the result that Peter Jackson wanted - but that’s fiction, and when you’re writing fiction you can do whatever you like because you’re not tied to any objective standard other than some degree of plausibility (which in turn depends on the expectations, experience and gullibility of your audience).  But we can’t re-run the Battle of Waterloo, changing one variable here or there to see what might have happened differently, because there’s no way to account for all possible variables, there’s no way to find out whether some seemingly insignificant variable might have been overwhelmingly important (butterfly wings, or “for want of a nail”, and all that), and finally there’s no way to scientifically validate model outputs other than to compare them to observed data, because historical events are one-time things.

What we can do, though, is try to develop rules that help us winnow the “universals” from the chaff that makes up most of history.  There were 190,000 “Agents” at Waterloo - but do we really need to know everything about them, from their shoe size to how many were suffering from dysentery on the big day?  Do we need all of the details that had to be programmed into Weta’s “Massive” Agents?  Or are there some general principles that we can use to guide our understanding of history so that we can pull out what’s important from the gigantic heap of what isn’t?

This is where a good reading - or hopefully re-reading - of E.H. Carr’s What is History? comes in handy.  In Chapter One, “The Historian and His Facts”, Carr disputes the ages-old adage that “the facts speak for themselves”, arguing instead that the facts speak only when the historian calls on them.  Figuring out which facts to call on is the historian’s métier.  Separating the gold - the “significant” as opposed to the “accidental” facts of a given phenomenon - from the dross of historical data is the historian’s challenge, and Carr’s criteria for judgement of what is “gold” is generalizability.  The key word here, of course, is “judgement” - informed judgement as a function of human cognition.  This is something that can’t be done by computers. 
 
Not yet, anyway. [Notes C, D] 

So for the time being, at least, there’s still a need for a meat plug somewhere in the analysis chain, if only to provide the informed judgement that our eventual silicon replacements haven’t quite learned to mimic.  That’s something of a relief.

Cheers,

//Don// (Meat plug)

Notes:


C) Historians looking for a lesson in humility need only read Heinlein’s The Moon is a Harsh Mistress, in which a computer that accidentally becomes artificially intelligent finds itself having to run a Lunar uprising - and in order to design the best possible plan for doing so, reads every published history book in a matter of minutes, analyzes them, and then not only plots a revolution from soup to nuts, but actually calculates the odds of victory, and recalculates them periodically in response to changing events.  Now that’s what I call “future security analysis.”  If only.

D) Some computer programmes are getting pretty good at matching human cognition.  Fortunately, we still hold an edge in important areas.  As long as we can still beat our electronic overlords at “Rock-Paper-Scissors”, we might, as the following chart from the genius webcartoon xkcd.com suggests, be able to hold the Singularity off for a little longer.
 


Friday, October 5, 2012

16 December 2011 – Seeing the light

Colleagues,

There's been an illuminating development in the battle in the US Congress to avoid a federal shutdown.  The $1 trillion spending bill, agreed last night, runs to more than 1200 pages and includes all manner of provisions inserted at the last minute in order to garner sufficient bipartisan support to ensure passage.(Note A)

One of those last-minute changes was a rider defunding the Department of Energy's standards for light bulb efficiency that would have gradually outlawed traditional incandescent bulbs.  The result of an energy law signed in 2007 by then-President George W. Bush, the DOE standards were to have been phased in gradually; the first victims would have been 100W incandescent bulbs, which would have been outlawed as of 1 January 2012, a fortnight hence.  Grass-roots Republican supporters had seized on the ban, making it a cause-celebre, and the issue has been at the forefront of talk radio and Tea Party-type discussions for months.  Democrats railed against the rider, but managed to eke out what they consider a win by including provisions requiring recipients of federal grants in amounts of $1 million or more to certify that they will upgrade the efficiency of facilities to the 2007 lighting standards.(Note B)

The light bulb ban is one of those rare issues that seems to galvanize the disparate ends of the political spectrum.  Democrats justified the ban by citing the impending extinction of polar bears and the drowning of coastal cities; Republicans countered with charges of war-against-the-poor and historical treason against the sainted memory of Thomas Alva Edison.  Lost in all the rhetoric, as usual, was the bulk of empirical fact, to wit, that while incandescent bulbs consume more electricity to operate, the lowest-cost alternative - compact fluorescent bulbs - are far more expensive than incandescent bulbs, contain liquid mercury, rarely last as long as advertised, must be disposed of as toxic waste, and are virtually all made in China (and thus must be shipped across the ocean on oil-powered cargo ships).  One reason for the manufacturing location is that China is virtually the world's only source of the rare earth elements used in the phosphors coating the interior of the CFL tubes.  The next generation of candidate illumination, LED lights, are even more expensive (due primarily to the need for a heavy and costly heat sink) and unless of very high quality, do not come close to mimicking the characteristics of incandescent bulbs.

As an exercise in cost-effectiveness, I ran the numbers on these back in March (see "Are governments smarter than a5th-grader?, 8 March 2011"), and concluded that the payback period for LED bulbs is about 6 years.  I'm six months [17 months, now! - ed.] into that on my office light bulbs right now and they're all still working; let's see how that goes.

Of course, the cost-effectiveness calculation is only one part of the equation.  The unit cost of the spotlamps I bought was $35, about 30 times the cost of a comparable incandescent spotlamp.  The average wage-earner isn't in a position to engage in costly experimentation with novel lighting technologies; if you're pulling down $35K a year as a general labourer, $35 for a single light bulb might seem a little outrageous.  That, in my opinion, is what fuelled much of the discontent with the DOE standards that would have led to the demise of the incandescents about two weeks from now.  Of course, the visceral response by many Americans to the government telling them what light bulbs they can and can't buy probably had something to do with it, too; live free or die, and all that.  You can have my incandescents when you pry them from my cold, dead hands...

What I find especially interesting from a strategic analysis perspective is how some in the environmental lobby responded to the news that the bill effectively lifted the impending ban.  Jim DePeso, the policy director of the group "Republicans for Environment Protection", was quoted as saying:

"In the real world, outside talk radio's echo chamber, lighting manufacturers such as GE, Philips and Sylvania have tooled up to produce new incandescent light bulbs that look and operate exactly the same as old incandescent bulbs, and give off just as much warm light...The only difference is they produce less excess heat and are therefore 30 percent more efficient. Same light, lower energy bills. What's not to like?"

Well, Jim, as I pointed out above, there's a lot not to like.  But what DePeso is referring to is a new suite of bulbs like the Osram Sylvania, a halogen bulb that reportedly replicates 100-W performance while consuming only 72W of current.(Note C)  
 
The Osram Sylvania 72W halogen bulb

Unlike the CFLs, Sylvania makes the Osram, which doesn't contain mercury, in Mexico.  But there's another strategic problem, you see - unlike traditional incandescents, these bulbs require rare earth phosphors.  This prompted Sylvania to issue a consumer notice earlier this year:

Rare earth metals  are elements vital to the manufacturing of our energy-efficient fluorescent lamps as they are a crucial component of the light-producing tri-phosphors inside the lamps. Currently, 95% of the world’s rare earth metal mining and oxide production comes from China where the manufacture and export of these compounds is heavily taxed and controlled.

The Chinese government has implemented new tariffs and mining regulations on rare earth materials. These actions, coupled with increasingly strict export quotas, have caused the price of these compounds to substantially increase – as much as 3500% since January of 2010 in some cases.

Due to regulation, exports of rare earth materials were reduced 40% from 2009 to 2010 and another 35% during the first half of 2011 compared with prior year. It is clear that the Chinease [sic] policies regarding rare earth materials must be addressed with multiple strategies in order to stabilize pricing and supply of these critical minerals.

OSRAM SYLVANIA has and will continue to take steps to combat these rising costs. We have continued to strengthen our supply chain while spurring our research and development teams into action. We are investigating the ability to reduce the weight of powders used in fluorescent lamps while evaluating alternatives to the current phosphor combinations. However, despite our best efforts, the rising cost of rare earth oxides has outpaced our ability to absorb them.

In order to combat the steep rising costs of these raw materials, OSRAM SYLVANIA will increase pricing on our fluorescent lamps, including our T8, T5, Deluxe T12 and all CFL pin and self-ballasted lamps. These increases will go into effect July 1, 2011; August 1, 2011; and then monthly until the cost of rare earth materials are stabilized.(Note D)

What does that mean for the bulbs that Mr. DePose thinks are the next big thing, infinitely preferable to traditional incandescents?  Well, it means that they're going to get more expensive.  And they're not exactly cheap right now.  The 72W Osram Sylvania goes for $8.20, which is more than ten times the price of a 100W Phillips Duramax, which retails for $2.98 per four-pack at Home Depot.  But the Osram isn't ten times as efficient (it uses 72% of the power of the 100W Duramax); and it isn't ten times as long-lived (it's rated for 3500 hrs, compared to 1500 hrs for the Duramax).  So it's already not much of a deal.  Furthermore, as the above statement indicates, that $8.20-per-bulb price is subject to change without notice due to the impact of China's near-total rare earths monopoly on REE supplies needed by the Mexican manufacturer.(E)
As for Mr. DePose's comment about GE, Sylvania, Philips and so forth gearing up to make the new light bulbs...well, is that a consequence of consumer demand for vastly more expensive light bulbs?  Or is it a consequence of the 2007 Bush Energy law outlawing incandescent bulbs that was supposed to come into effect on New Year's Day?
The Great Light Bulb Purge - narrowly averted by Congress, at least for now - is an excellent example of the chicken-and-egg relationship of government regulation with consumer preference in the modern "free" market.  Apart from moral pressure (like the David Suzuki Foundation's recent and ludicrously hyperbolic "Drowning Santa" campaign), there are essentially two ways that governments can force consumers to buy things they wouldn't otherwise buy.  One is subsidies - governments giving taxpayer money to industries to make otherwise non-competitive products cheap enough for consumers to consider buying them (q.v. wind power, solar power, and hybrid and electric vehicles - I've got sources if you want'em) - and the other is regulating technologies deemed 'undesirable' out of business.  This is the source of the philosophy reflected in Obama’s campaign promise to bankrupt coal-fired electrical generating concerns (Note F).
Both approaches constitute interference in the marketplace.  And as I've mentioned before in previous missives, both of these techniques seem to keep popping up, time and time again, in the political drive towards the 'green energy' economy.
But for the moment, at least insofar as the venerable incandescent bulb is concerned, it seems as though someone just might have "seen the light".
(Exclamation of joy at sudden observation of luminescence expurgated)

Let us therefore give thanks.

Cheers,

//Don//

P.S.  A back-of-the-envelope calculation tells us a little bit about the cost comparison.  The 72W Osram Sylvania bulb sells for $8.20 online.  A 4-pack of Phillips Duramax 100W bulbs costs $2.98 at Home Depot, or $0.75 apiece.  The Duramax has a rated lifespan of 1500 hrs; the Osram 72W, 3500 hrs.  To get 21,000 hours of light, you'd need 6 Osrams or 14 Duramaxes.  21,000 hrs of Osrams would consume 1512 kWh of electricity.  21,000 hrs of Duramaxes would consume 2100 kWh of electricity.  At roughly $0.10 per kWh, the Osrams would cost you $151.2; the Duramaxes, $210.  So you're saving $58.80 over 21,000 hrs, or $0.0028 per hour of use.  Those 6 Osrams would cost you $49.20, whereas 16 Duramaxes would cost you $12.00.  So you're paying $37.20 more for the bulbs, but saving $58.80 on electricity, for a total savings of $21.60 over 21,000 hrs.  Unless the Osrams don't last as long as advertised, or their price goes up due to the rare earths shortage, of course.

Notes
A) http://www.politico.com/news/stories/1211/70530.html

B) http://www.politico.com/news/stories/1211/70534_Page2.html

C) http://assets.sylvania.com/assets/Documents/Consumer_SocketSurvey_FINAL.6f999370-cb88-479f-94e1-a6483531b81f.pdf

D) http://www.sylvania.com/Phosphors/

E) http://www.bulbtronics.com/Search-The-Warehouse/ProductDetail.aspx?sid=0000984&pid=OS72MBCAP120V&AspxAutoDetectCookieSupport=1

F) http://frontporchpolitics.com/2012/07/obama-keeps-campaign-promise-patriot-coal-bankrupt/