The "thread" is that there is a mess, and main cause is gumment messing up the situation.
369 karma · joined January 21, 2011
But the start of this was not computing but math, physical science, and engineering during the sudden high demands in such fields in the 1960s due to both the Cold War and the Space Race. There, too, the old power structure was surprised and angry. Technical people were called 'The New Mandarins'. The old power structure, say, Ivy League history majors, were torqued.
Part of the situation is somewhat general: The 'suits' still want to be like in a Ford plant, say, 80 years ago when the suits knew more and the subordinates were there just to add labor, muscle, and sweat to the work of the suits. Then in the technical fields, suddenly the subordinates knew more than the suits, e.g., about Maxwell's equations for battlefield or satellite communications, about the fast Fourier transform and digital filtering, about orbit determination for navigation satellites, and about computers.
Yes, people in law and medicine also know more, but they are in recognized professions set apart from the hierarchy of an old Ford plant, but computing was not, was, say, in the CFO's group or the manufacturing group. Bummer.
So, some suit had a bright idea, "To keep people from wasting so much time learning higher level languages, we will use only assembler and, thus, save lots of money.". Right! "Also, programmers get paid much more than typists. So, we will hire typists for the typing and won't let programmers type.". With the ROFL, soon the suits saw that they were in deep, fuming, smelly, sticky stuff.
So, how can a suit survive? Sure: For each technical job, hire about three technical people. Then none of the technical people can have 'leverage' over the suit. Of course, for this, need MANY more technical people.
Second, the US DoD actually believed that for national security, the US should increase the supply of labor in technical fields and got Congress to agree. Then the NSF started throwing money around to this end and with considerable success.
By the 1970s, US citizens began to see that there was no pot of gold at the end of the picture of a rainbow drawn by the NSF and heavily quit going for those technical fields.
Third, but the NSF kept trying. Their next semi-bright idea was to write into academic research grant contracts that students must be supported. When US citizens wouldn't come, the universities got the students from other countries, at first, heavily Taiwan and India.
Fourth, when the computer industry caught wind of all this, they pushed for the H1-B visa program and got a big supply of essentially 'indentured labor' they could, and did, exploit.
Net, fields such as law, medicine, pharmacy, roofing, carpentry, pizza making, machine tool making, auto repair, accounting, etc. don't get the attention from all of the DoD, big employers, Congress, the NSF, and the universities.
So, net, computing is heavily 'targeted' by all of the DoD, ..., the universities.
E.g,, the targeting pushes for more in, say, electronic engineering. Thus, often there is a better career as an electrician than with a Ph.D. in electronic engineering:
At least in some states, the electrician needs a license and, likely, has liability, and the Ph.D. nearly never has either. So, the electrician is closer to having a 'profession'.
As an employee in industry, the Ph.D. will likely discover that before 40 he has to move into management or get fired. Yes, Virginia, they fire Ph.D. EEs. So, by age 40, only about 1 in 100 is in management.
Fired, the Ph.D. will discover that the electrician of the same age can have a nice business, several employees, a nice house, and take off Friday-Sunday, even if he doesn't bother to have his name in the Yellow Pages, really can't be fired, and isn't vulneable to age discrimination, office politics, industry M&A, Toshiba beating GE, etc.
Really, more generally, with 'globalization', the US citizens who get rich are okay but most of the others need a geographical barrier to entry. E.g., an electrician is not in competition with anyone more than, say, 100 miles away. So, if he does okay in a radius of 100 miles, then he can do okay.
In business, the Ph.D. hss only a very narrow list of candidate employers, heavily the US 'military-industrial complex', while the electrician has a huge range of candidate clients and can do okay as long as the whole economy is not in the tank. E.g., if there is new construction, then he does that. If not, then he does renovations.
Seeing such things, many US citizens are avoiding the fields targeted by the Federal government and, also, fields vulnerable to globalization. Net, at present, for nearly everyone in high school now, they better plan on a career as a Main Street sole proprietor.
Thank you NSF for 'targeting' technical fields and driving out US citizens and Foggy Bottom for 'globalization' as a source economic carrots to try to make nasty foreign countries 'behave' -- e.g,, give away much of the US bath towel market to Pukistan to make them 'behave'. How well are they 'behaving'?
Mo big gumment, Ma!
The less big gumment does, the fewer really big mistakes they make.
But the flip side of this disaster is an historic opportunity and right in the center of HN: Be an entrepreneur much as for a Main Street business but also technical. Maybe get venture funding and maybe not, but in any event be a technical CEO. Then can beat the pants off any competitors run by non-technical suits.
The supply of labor for software is a very different issue.
David J. Marchette, 'Computer Intrusion Detection: A Statistical Viewpoint', ISBN 0-387-95281-0, Springer-Verlag, New York, 2001.
Yes, my work became peer reviewed, original research published in one of the better Elsevier journals of computer science,
And I have a long background from Yorktown Heights and in DoD work.
Still I discovered that I was absolutely, positively, permanently unemployable in anything having anything at all to do with computing. Period. In business, on Wall Street, near DC for national security, for anything. Why? I was over 45.
So, I'm starting my own business. My target customers won't care that I'm over 45.
For a physician or lawyer, being over 45 is a great advantage -- they know more, and the target customers want the gray hairs. The knowledge is, in principal and can be in practice, e.g., my work in computer security, a big advantage. Still in computing gray hairs are worse than a felony conviction, literally.
This issue of age discrimination is a big reason you see so many immigrants in computing. Then, seeing so many immigrants, US citizens commonly sense that there's something wrong in that field and stay out.
So, why so many immigrants? Sure, it's easy, just as in, say,
http://www.wired.com/dangerroom/2010/01/darpa-us-geek-shorta...
where the drum beat (as recently from Mayor Bloomberg, on AVC.com, in some banker before a committee of Congress, etc.) is for a big 'shortage'. The same was true during The Great Depression: Growers in California circulated posters in the rest of the country claiming a big 'shortage' of farm workers in California. The sheep came and got fleeced.
Then, with the drum beat for 'shortage', as you notice, the drum beat will be for more immigration to meet the shortage.
This got started when the NSF decided to flood computing with immigrants and did this by writing into university research grant contracts that so many students had to be supported. Then the H1-B situation came along and filled whole departments with immigrants and, often, implicit signs "No US citizens need apply".
Computing? The US Federal government 'targets' the field and tries hard to manipulate the supply and demand. So, well informed US citizens stay the hell out.
For my business, what the US Federal government is doing to computing does not hurt. Actually I will have opportunities to exploit immigrants but will refuse to do so. Instead, I can hire some gray hairs! Okay by me!
But generally, young US citizens should stay the hell out of computing unless they can see their way clear to owning their own, successful business with a wide, deep "moat" (see Buffett).
The only way to be sure gumment doesn't make a mess out of our economy is to be sure our gumment stays out of our economy. E.g., The Great Recession, started by what some selected members of Congress told Fannie and Freddy -- back any junk paper. So, bubble, crash, wipe out the ability of the US banks to play their role in the US economy, bring on The Great Recession, and run up the national debt by a few trillion dollars. Yup, gumment in action again.
Computing and gumment? Drive US citizens out of computing.
Semi-, pseudo-, quasi-great: Computing is an 'essential' field especially for US national security, so drive out US citizens. Yup, gumment's best again!
Lawyers? They have a cute professional rule that a lawyer working as a lawyer must be supervised by a lawyer. So, no stuffed suit, business middle manager types need apply to supervise lawyers in an organization.
It's possible to do some good work in 'climate science', and maybe there is some by more than just the one guy at MIT or the one in Georgia. If they do good science, then maybe Nature would publish it. Here I mean they can do some good science which, however, is a very long way from really answering the main question, are we about to cook the planet? E.g., the MIT guy did some diffusion in a column calculation. Looks okay, but it's a very, very long way from an answer to the main question.
But generally my view would be as you put it that on 'AGW' -- anthropomorphic global warming? -- science is broken.
Why don't I get rich and famous debunking the alarmist nonsense? Because the alaremist 'science' is making various approximations. Maybe with their assumptions, their arithmetic is correct. E.g., if do the energy balance arithmetic carefully and get it right, then okay. Nice work. If it says no changes, good. If it says big changes, then, sadly, the work is good only until the changes start to become significant. If they do everything correctly on energy balance, then my objection would be, in the case of big changes, using that analysis to say what the climate will be in 10, 50, 100 years. I could say that, and maybe it would get published, but it would hardly make me rich or famous.
Broadly Al Guru picked a 'good' problem, that is, like the Mayan priests who scared people without enough solid information do debunk. Again, the solid science we want is not available. With such science we could debunk the alarmists by saying in rock solid terms what the climate would be year by year for the next, say, 500 years. I can't do that. No one can do that.
So, why is science broken for AGW? The fundamental reason is that no one can really get a solid answer to what the climate will be decades into the future under various scenarios of human activity. The shorter term political reason is that the 'climate science' community was started heavily by VP Gore's direction of the funding and, then, the interests of the IPCC to find a way to send money from rich countries to poor ones. So, there are lots of vested interests and not much science solid enough to settle the issue.
So, again, note (1) the temperature is, as far as we can tell, now just where it was before the start of the Little Ice Age; (2) from the good temperature data we have now from satellites, apparently the planet is not getting warmer now. So, what to do? Just keep watching the data and the science, If there is any that is very important, and then readdress the question once it begins to look important.
But there's a big HUGE thing NOT to do now: Wreck the world economy over very inconclusive science.
If I were an executive at the head of one of those, then I'd likely go ahead and upchuck and put out the same total BS they do now. Why? Because it's about 99 44/100% light enterainment instead of information. They have an absolute phobia at getting at any very serious information. In particular, they have to stay way below the average level of HN. As I wrote above in this thread, apparently hated by several people, the contents has to be at the 4th grade except math at the 2nd grade and sex at the 10th grade.
You want to blame this low grade nonsense on "the consumes". Well, with more channels, e.g., via the Internet, we can get, e.g., HN, and that's MUCH better, more technical, more advanced, more thoughtful, and MUCH less just formula fiction entertainment.
With still more development of the Internet, we will be able to get some really solid information. Some such information leaks out in places now via university material, some quite specialized Web sites, some industry sites, and more. E.g., there's a Web site that wanted to talk about electric cars. So, I got into a big debate with someone. We had to get into capacitor math. So, I got out my college E&M text, read up on capacitor math, and typed in the math to support my position. The other guy didn't like my math. Finally the site moderator found a good expert on capacitor math, etc., had my post 'reviewed' and pronounced correct. Got'a tell you, won't see any capacitor math on ABC, CBS, ..., not even PBS.
The media WILL "reflect the nature of the consumers" when we can have enough channels and bandwidth to partition the consumers into many thousands of categories. Then in some of the categories we will be able to get some really good stuff. Actually we have the channels and bandwidth now, but the exploitation of the Internet is not nearly complete yet.
You didn't get the memo: "All content is to be at the level of a not very good student in the fourth grade. There are two exceptions: Math is to be at the second grade level or below. Sex is to be at the tenth grade level or above."
Now you have the memo and know not to expect anything as far out, absurdly advanced, totally unrealistic, super genius level as high school physics!!!!!!!
Yes, you may want to rush back to your home planet. Have any extra space on your ship?
My main explaination was "the medium is the message", and old media found that pushing emotional content, drama, formula fiction, etc. got them the best ad revenue. I suspect it did. So, in particular, the 'medium' led to emotional, superficial articles such as in this thread, That was my explanation for the article.
The 'radiative forcing' term was not well defined and, as I recall, nothing like what Ramaswami explained in the main, relevant IPCC document. Ramaswami's stuff was junk, but the usage at the Web page seems better but a long way from good.
That a 30% increase in CO2 concentration leads to an increase of 3 degrees C is tough to swallow: CO2 absorbs such a small amount of energy, especially after what would be absorbed by water vapor, methane, etc. Also, I'd want to see that CO2 calculation in detail, e.g., in terms of the CO2 spectral lines and the radiation from the surface of the earth. Also the page mentioned 'saturation': They didn't say just what they meant, but a guess is that some gas is asborbing all it can. They didn't say how close CO2 is to 'saturation'. My guess is that CO2 is close to saturation now. I'd want to see some details.
At one point they want to refer to 'sophisticated global climate models': That's easy -- it's the empty set.
In places their writing tries to be a snow job, e.g., using undefined acronyms.
They reference Hansen -- TILT! He used to be a big global cooling guy.
Generally, though, the stuff from Al Guru and the IPCC (Tom Friedman is MUCH worse) is so bad that I lost patience with this stuff: The whole thing is at least 99 44/100% flim flam fraud deceptive manipulation,
It's from a 'culture' that is ingrained and self-perpetuating: In college, they majored not in math, physical science, or engineering but in the 'humanities', especially English literature. There 'truth' is 'compelling' and from emotions or beauty, is in the eye of the beholder, personal, relative, etc.
In particular the most desired form of the emotions is 'drama' especially as in formula fiction with good and evil, etc.
The foundation is 'art' as in communication, intrepretation of human experience, emotions. Or 'it feels good'.
This 'culture' is solidly in control of 'old media'. There are two big reasons:
First, old media goes way back, is sitll close to the old morality plays, and goes way back before the revolution in information safety and efficacy starting with, say, J. Maxwell and with grand examples in math, physics, chemistry, biology, engineering, technology, medical science, and medicine of the 20th century. Old media is still locked up well before 1900, mostly 1800. The college humanities majors naturally gravitated to that culture and still do. There are more details in C. P. Snow's 'The Two Cultures'.
Second, "The medium is the message" has long held true. In particular, before the Internet, the 'medium' was print, radio, or TV, and there the number of 'channels' and the 'bandwidth' of each channel were so small that the audience had to be very broad and the room for details was very small. So, the 'message' was to low grade emotions and very short. And that's what the article of this thread is. Useful? Rational? No. Emotional? Trivial? Yes.
So, obviously 'new media' can exploit more 'channels', would you believe over 100 million blogs, and more bandwidth, how ahout over 5 Mbps download bandwidth? Then we can have 'streams of focused content for focused interests', over 100 million 'streams'.
Sure, anyone with anything like an education in math, science, or engineering good enough actually to make things work pays close attention to details, say, efficiency, cost, durability, power levels, etc. Else, computers would snap, crackle, and pop, airplanes would never get off the ground, bridges and buildings would fall, etc. But the English majors in the culture of old media don't care.
Old media is dying, and not just because Craig's List is taking their classified ads.
HN and your remarks are right on target for how old media is being killed and where new media will be better.
My view is that the biggest problem in civilization and our country now is the brain-dead, all-emotions all the time, dysfunctional, self-destructive nonsense of old media instead of the solid information we need to be responsible citizens and direct our government to a better future. E.g., only now, slowly, are we learning the real anatomy of The Great Recession. So, old media never got the word out. Cry about the pains after the disaster? Sure. Have the solid, crucial information to avoid the disaster before hand? NOT a chance. Old media is helpless, full of tears, devoid of rationality or responsibility.
With old media, it's surprising we haven't blown up the planet by now. Old media, I have a question: "Now, how does that make you feel?".
But this discussion does not come up to the level of seriousness for me to go to my directory of global warming information and pull up references and details.
The 50 US cents per KWh at the plant is okay: That's ballpark what the total cost has been in Germany that tried hard with subsidies to get farmers to install solar panels.
Sure, people can keep working on solar, wind, low head hydro, etc. power and hope to get the cost down to 20 cents or so, etc.
Still solar and wind are total made up nonsense, wind likely for centuries, and solar at least for decades. A big, huge problem is that those sources are unpredictable so need storage, and the cheap storage is not available. So, basically have to pay the CapEx twice. That is, have to pay for a coal plant to use when the wind is not blowing. Then the coal plant operator will have to raise his rates to cover his CapEx for the time his plant is not operating. Bummer.
On average in the US, at the plant, nukes have been under 2 cents per KWh and coal has been under 3 cents. In comparison, the dreams of the people pushing 'clean, renewable' energy would shoot the US economy in the gut.
For the probability, that's a silly issue: The Mayan priests who killed people to pour blood on a rock to keep the sun moving across the sky (I have a reference to a scholarly book on the Mayans, and Google books or Amazon shows the page with the specifics) picked a good problem: The Mayans didn't know enough to debunk the priests.
Al Guru did the same: Solid science for what the climate will be in 50 years under various scenarios for human activity does not exist. In particular, about the only way we know to calculate the 'climate' is from 'first principles' of physics and chemistry, and that involves actually predicting the weather, each cubic millimeter or so, INCLUDING the oceans, all over the planet for each millisecond or so for 50 years, and we can't do that computing. [Note: All simplifications are not from first principles and are approximations of unknown accuracy. So, can't actually calculate all the clouds in detail, put in a simple model for the clouds; for the oceans, do something still simpler.]
Then for the 'climate', need to do the full calculation, do that computing for a narrow distribution of initial conditions around the conditions now. Big problem: We don't even know the initial conditions, e.g., in the depths of the oceans and their currents, accurately. Anyway, run the weather prediction a few million times and then take empirical distributions and find the climate. Then change the scenario of human activity, do it again, and compare.
Tough to do. Hasn't been done.
Similarly for a gamma ray burst blowing the atmosphere off the earth, Yellowstone wiping out much of the US, a moving black hole sucking up the earth, some microbe we don't know how to kill wiping out humanity, etc.
So, what do we do? Well, we do NOT fall for superstition and gurus like the Mayans did. We don't go for morality plays about evil humans with transgression from evil, retribution from an angry god, and redemption from sacrifice. We don't sacrifice lambs or virgins. And we don't sacrifice our coal plants, half of our electric power.
For 'global warming', first, we look at the arguments of Al Guru, Guru Ramaswamy, etc., move forward to their first absurd, outrageous, egregious, grotesquely incompetent and/or dishonest point and then flush their arguments and reject those gurus. For Al Guru, the time between the high temperatures and the high CO2 levels he didn't show in the Vostok data is enough to flush his stuff. For the IPCC and Guru Ramaswami, his 'radiative forcing' is enough to flush.
So, for the alarmists, we have nothing but superstition.
So, we have to proceed on our own.
So, we start: Is there any empirical evidence that the earth is getting warmer now? We can measure temperature with astounding accuracy, and now with satellites we can get good data for the whole planet, day by day. May I have the envelope, please? "Nope, there's no evidence."
For a little more, as far as we can tell (I'm sure you have the NAS report), the temperature now is exactly the same as it was before the start of the Little Ice Age caused, maybe, by missing sun spots for some decades. So, since then we've been pulling out of the Little Ice Age and, net, since just before the Little Ice Age, all of human activity has had zip, zilch, zero effect on temperature. Net, human caused global warming doesn't pass the sniff test.
Next, what about the science? Do we worry about CO2, methane, water vapor, chlorinated hydrocarbons, aerosols? First- cut, changes in any of these seem to be at most small and trivial.
Next, what is the record on climate variability? Well, there's been a lot of variability. So, that we see nothing going on now means, first-cut, no worries, mate.
Next, if there we do begin to see some changes, then we will address the issue again.
The idea that the climate is wildly 'unstable' (got'a stop those evil butterflies from flapping their wings) and that we are at a tipping point and point of no return, with no good evidence, we have to pass off as superstitious nonsense like the Mayans.
Net, we're not Mayans. We don't wreck our society for superstitious fears. We just don't do that.
No sale. Ain't buying.
Done.
For the $500 K per year per prof in field and experimental science, that's not their "compensation". The $120 K a year you mentioned is closer. My $10 million was within the ballpark, within a factor of only 2 from the data you found. Next, the CEO salaries have nothing to do with global warming or climate change.
An great example of bad data is Al Guru's movie. Again, he blew the Vostok ice core data and neglected to note that the CO2 increases were hundreds of years after the temperature increases. So, clearly CO2 did not cause the temperature increases.
Al Guru's pictures of polar bears and glaciers are meaningless and not "good" data. Similarly for his observation of snows on Kilimanjaro. So, Guru wanted to bring in lots of anecdotal this and that instead of what is clearly the crucial measure for global warming -- temperature, just temperature.
The bad data and analysis goes on and on. Guru is trying to make money (he has), be famous (he is), and push his favorite project, scaring people about global warming. It's likely a religious thing with him, considering his background. Whatever his motives are, his evidence and arguments are BS.
For the IPCC, it's really no better. 'Radiative forcing' is total made up crack pot BS. Really the IPCC is about getting 'carbon credit' transfer payments from wealthy countries to poor ones.
Yet the global warming people want us to go to electric power at the plant from about 2 cents per KWH to about 50 cents, convert to electric cars for which there are no feasible batteries for how the vast majority of cars are used, even to convert long haul trucks to batteries which is absurd, and on and on. It's the same as the Mayan priests killing people to pour their blood on rocks to keep the sun moving across the sky.
Just what is it about total BS crapola you find so attractive? Dump it. Flush it. F'get about it.
Your claim that three energy industry executives make more money than all the climate science research is likely false: A research grant in science to a research professor goes for about $500,000 a year. The university grabs about 60% for 'overhead' (help support the English department, etc.). The rest covers or helps cover the prof, travel, photocopying, lab equipment, grad students, etc. Add up 100 such profs and have $50 million a year.
But nearly all the Fortune 500 CEOs are just hired managers and, surprisingly, are not so well paid if only because typically they don't last more then 5 years as CEO. So, maybe they make $10 million a year, for a few years; then they don't go start again in the mail room and, instead, just retire. After the taxes, considering how few years they were CEO and long they might continue to live, a guy who owns, say, 10 McDonald's can do better if only because he can keep his business for decades.
So, for your three CEOs you are up to maybe $30 million which is less than the $50 million for the profs.
Sure, a given CEO might some year cash in some stock options and make $50 million, but that's a one-time thing.
Instead of such salaries, the big bucks are from owning something not worth much and then making it valuable. Why? Because only a tiny fraction of the people can evaluate the tree that might grow from a seed or help such a seed grow to a tree. E.g., relevant here on HN, there's hardly a single venture partner anywhere in the US who will even try to evaluate the chances of a particular project becoming another Google. Instead, for any investment amount enough to support a few people for a few years, say, over $1 million, they will invest in something simple such as 'traction' and do so on the basis if the track record of 'traction'. For 'another Google', they will be happy if it happened but will make no effort toward that goal. So, entrepreneurs such as discussed on HN some hedge fund managers, especially J. Simons, can make much more. On all of Wall Street, there is exactly one person who understands what Renaissance Technologies does and has good reason to know why it makes money: Simons. Period.
But, that some oil company executives are making money says nothing about global warming or really that Exxon is funding FUD.
A lot of the global warming hysteria is from stimulating the desire of people to have a religion. With the major organized religions in decline, Communism, global warming, etc. can find some fertile ground.
There is also general paranoia such as made the morality plays popular going way back in the history of 'story telling'.
"All this said, though, perhaps the bigger question is: what would convince you that global warming is a big problem and that it's being caused by people?"
Right, that's the bigger question. The answer is simple and the same for global warming, climate change, dying of anthrax, having Yellowstone blow again and put a layer of ash 10 feet thick over much of the US, have the earth hit by an asteroid, etc. Same for all of them: Look for good data and good arguments, especially good science.
E.g., for the asteroids, yes, the various belts of asteroids are unstable so at any time a bump can cause an asteroid to leave the belt and head for earth. Right. So, for a fairly good first cut, dangerous asteroids will arrive like a Poisson process. But we have a very good estimate of the arrival time: The rate is less than once per 65 million years. So, for the next few years, the probability is tiny. And that's why I don't worry about asteroids.
For anthrax, we have good public health data and we understand anthrax. So, for my life in the burbs, no worries, mate.
Etc.
For global warming and climate change, the data is total BS, and the science is worse. So, flush it. No worries, mate.
Easy enough.
Done.
For you, again, relax.
There's nothing significant against Exxon at that Web site, certainly nothing like your "organized disinformation campaign".
Maybe Exxon funded some 'climate science', although the Web site gave no evidence.
Well, how'd the whole 'global warming' thing get started anyway? Sure: VP Al Guru told the NSF, etc. to fund his buddies for 'climate alarmism'. That's how the 'closed community' of 'climate science' alarmists got started: They all reviewed each other's research papers and grant proposals, and they all knew that there was only one source of money, Al Guru, and that that source wanted only one answer -- human CO2 raising temperatures. Now that they are on the back of that tiger, and could not have a career in science anywhere else, they have to just keep riding that tiger.
But your URL has alarmist nonsense about extreme weather -- droughts heat waves, etc. There's essentially no connection between such things and the arguments about global warming, at least not for decades or centuries.
So, the increases in temperature over the past few decades are so small that they are tough to measure or to show an increase, but even without showing an increase in temperature we are supposed to believe that the increase, too small to measure, is causing massive climate 'change' now, as with the tornadoes this spring? Total reeking BS.
But when the predictions for global warming clearly didn't happen, there was a memo to talk about climate 'change'. While global 'warming' can be characterized by just average temperature, say, over the planet, over a year, measured by, say, satellite, and while some temperature records go back hundreds of years and some temperature evidence, say, ice core data, goes back hundreds of thousands of years, climate 'change' is MUCH more difficult to evaluate, e.g., compare with the past. While the past temperature record is nothing like what we can measure today, the climate 'change' record is much worse. So, the climate change screamers can keep screaming, as for the tornadoes this spring, without much comparison with the past.
For the funding corrupting science, f'get about Exxon and concentrate on Al Guru.
That is, the LESS data they have, the MORE screaming they are free to do. We just should refuse to listen.
Besides, the usual media, ABC, CBS, NBC, MSNBC, CNN, PBS, just LOVE the morality play of evil humans doing transgressions and causing retribution and the drama of tornadoes. Grab'm by the gut, and their eyeballs will be sure to follow, and then get the ad revenue. Those media outlets are in the ad business and will push anything at all, any sewage -- crime, scandal, blood, danger -- that can be turned into drama to grab people by the heart, the gut, and below the belt.
E.g., what was the frequency of tornadoes in the area of the present state of Kansas each year over the past 400 years? Not a chance of getting good data. And, heck, the media won't even report such frequency data over the past, say, decade. Why not? The data would show no significant change, have no drama, and would kill the made up story with drama.
So, the tornadoes this spring can be screamed to be from 'climate change' from evil humans, as we have heard.
You are spouting just alarmist nonsense.
The data for human caused significant global warming is BS; for climate change, much worse. You have no serious evidence.
You didn't take my advice just to relax and f'get about Al Guru.
How much are YOU getting paid?
Global warming is real to the present depending on when one starts to count from. In the past the earth has been at times warmer than now and cooler than now.
Humans responsible? Human are clearly responsible for some global warming: E.g., light a match and warm the earth. But humans clearly are not responsible for all the quite wild changes in climate in the geological record before humans even existed. And it is tough to say that humans had much to do with the earth falling into the Little Ice Age or starting to come out of it.
Is current human activity significantly warming the earth? I have looked hard yet seen no credible reports that it is. No, I do not count Guru Ramaswami's 'radiative forcing' crapola at the center of the IPCC documents. For 'climate science', that is nearly all just a flim flam fraud scam by a closed group of people pushing their orthodoxy to push their own careers.
I know; I know: One of Al Guru's Yale profs liked to take vacations in Hawaii so put a CO2 sampling station there and has CO2 concentration data for some decades. Then the screaming started: "CO2 is a greenhouse gas, will 'trap' heat and warm the planet, and we will DESTROY THE EARTH." BS. CO2 only absorbs in three narrow bands, one for each of bending, twisting, and stretching of the molecule, and all three bands are out in the infrared. Net, CO2 doesn't absorb much energy and is trivial, a nit, compared with water vapor, methane, clouds, etc. The CO2 is just something to scream about.
My understanding is that the predictions of rapid warming by the 'climate scientists' over the past few decades never happened thus seriously hurting their credibility. Heck, the 'leading climate scientist' back in 1970 or so (I'll save the time to look up his name, claims, and date -- but you remember, it was a 'Newsweek' or 'Time' cover story) about 'global cooling'.
For the article, it seems to have gotten the memo: Don't talk about 'global warming' and, instead, talk about 'climate change'. So, jumping in with the media, the article seems to go along with the suggestion that human activity has caused climate change has caused extreme climate this spring caused many more than the usual spring tornadoes in the US and, in particular, ones aimed at population centers, all without any serious, numerical, historical data on tornadoes in the US. Sure: ABC, CBS, NBC, MSNBC, CNN, and PBS can get people up on their hind legs this way, but not me. So, the screaming is just flim flam to stir up hysteria.
I have seen no "organized disinformation campaign", and you provided no evidence of one.
It's not about a "campaign". Instead it's about the climate and solid evidence and science about the climate. So far, net, 'no worries, mate'. For the flim flam fraudsters, ROFL.
I used to debate all this nonsense on Reddit but gave up on Reddit and saw that mostly everyone saw that the global warming crowd was just pushing a fraud and quit listening. So, I quit arguing. So, I'm not going to dig out all my details on the 'radiative forcing' crapola at the center of the IPCC garbage, the NASA guy who makes so much noise, the good work done by the guy at MIT, the Hadley e-mail data, the 50 cent per KWH solar power in Germany, the wacko Al Guru graph that failed to notice that in the geological record from the Vostok ice core data the CO2 increased hundreds of years AFTER the temperature increased, etc. The whole thing is dishonest science and a flim flam fraud scam.
I've got good news for you: Relax. F'get about Al Guru, Cap and Trade, shutting down coal plants and, thus, wrecking the US economy, the polar bears, the whales, the rising sea levels, etc. Let the EPA enviro-wackos get real jobs and save the tax money. Let Icemelt Immelt find something useful to do. Let Al Guru find some more acolyte chicks to bed. Tell the UN IPCC to go back to railroad engineering. F'get about the fraud.
Give up on the morality play of human sin and evil and the classic dramatic trilogy of transgression, retribution, and redemption. If you like that trilogy, then listen to Wagner's 'Ring' or 'Parsifal' or Tolkien's 'Ring' or 'Star Wars'. Relax. After all, it's just a movie.
(1) Humans are evil. They are sinful, greedy, duplicitous, violent, irrational, and destructive.
The claims that the concerns about human caused significant global warming are just a flim flam fraud are just deceptions of the Devil.
(2) Evil humans are destroying the planet, the 100% all-natural, delicate, sensitive, pure, pristine, precious environment.
The weather was never like this before evil humans started working with the bow and arrow.
(3) For this evil transgression, humans will be made to suffer terrible retribution of extreme weather, failed crops, farms and forests turned into deserts, lowlands flooded, cute, cuddly, sweet, pure white baby polar bears drowning in the ice-free Arctic, penguins starving, and worse. The whales will die, and when the whales die, the oceans will die and then we will die, and the whales are starting to die.
(4) For this retribution, humans need redemption or death from their sin, evil, and transgressions. The only possible redemption is sacrifice. We have to start by giving up computers, telephones, airplanes, plastics, TV, yes, even including the soaps, electric power, cars, frozen foods, and McDonald's French fries. Then we must give up synthetic fabrics, permanent press, washing machines, paper plates, deodorant, and women's bras and panties.
Humans must abstain from sex.
But this will not be enough, not nearly enough: The sun is about to stop moving across the sky, and the only solution is to have a holy Mayan priest hold ceremonies pouring the blood of evil humans on a sacred rock. The only qualified priest is Saint Laureate Al Guru aided by the dedicated, devoted Guru Acolytes lead by Sister Laurie.
The blood will come from sacrificing virgins (when they first arrived at the ceremony with Saint Guru).
Only in this way, along with Cap and Trade and the EPA, can the planet be saved.
For what I read, those were the course notes.
That's not at all what I wrote. You really don't even know how to read a definition in math, do you? Do you know any math at all?
You are wrong again; a counterexample is trivial to construct.
Here you have no need to defend your knowledge of math in general, just on one point, the definition of a random variable.
You are seriously, flatly wrong mathematically. Name calling and refusing to read won't make your nonsense correct.
Enjoy looking like a fool before the world of computing, forever.
This statement is an unfortunate juxtaposition: Glad probability is one of your "favorite topics", but you make serious mistakes right at the beginning and show that you don't know anything significant at all about the subject, not at any level from freshman to the texts I listed.
That you "agree" with that "part of the course's treatment" is absurd, especially after what I wrote. That "treatment" is a total upchuck. You, the professor, and CMU should all be humiliated and ashamed. It would be tough to get worse even from a storefront for-profit 'college'. For probability at CMU, I listed an excellent source, Steve Shreve.
"If we really wanted to do probability the right way, we'd tell them that a random variable is a structure-preserving map between measure spaces."
There you go again. You are digging your hole longer, wider, and deeper. You are spouting nonsense.
A "random variable" is definitely not a "structure-preserving map between measure spaces". Not a chance. You won't find "structure-preserving" mentioned anywhere in the definition of a random variable in any of the four texts I listed. Your "structure-preserving" is just gibberish you got from some source you should not touch and then burn outdoors and then flush.
The most advanced definition, as in the four texts I listed, is that a 'random variable' is a measurable function from a probability space into a measurable space. What I described for the set of all events is called a 'sigma algebra'. Then a 'measurable space' is a non-empty set and a sigma algebra of subsets of it. A function is 'measurable' if for each set in the sigma algebra in the range its inverse image under the function is a set in the sigma algebra of the domain. A 'probability space' is a measurable space and a probability measure on that space, and a 'probability measure' is a non-negative measure with total mass 1. For a 'measure', see any of, say,
Paul R. Halmos, 'Measure Theory', D. Van Nostrand Company, Inc., Princeton, NJ.
Walter Rudin, 'Real and Complex Analysis', ISBN 07-054232-5, McGraw-Hill, New York.
H. L. Royden, 'Real Analysis: Second Edition', Macmillan, New York.
I omitted the requirement for a random variable being measurable because for the more important measurable spaces, say, the real numbers with the Borel sets or the Lebesgue measurable sets, finding a function that is not measurable is super tough: The usual construction uses the axiom of choice.
"You can't do that with freshmen."
Well, no one should do "that" with anyone, but with your "agree" with that "part of the course's treatment" here shows that you have been doing even worse, apparently with freshman.
You are flatly refusing to take me at all seriously and refuse to 'get it': Your definition of a random variable is sewage, and you are even defending it.
For what to do with freshman, I gave more than one appropriate, accurate enough, and easy to take, definition of a random variable. There are also other sources. You very much need to take some such source seriously.
You just won't stop digging your hole longer, wider, and deeper: There you go again with your:
"Indeed, we deliberately avoided any mention of continuous probability spaces."
You are spouting more gibberish from some source that should not be touched and then burned and flushed. Your comments continue to fill much needed gaps in the teaching of probability.
There is no such thing as a "continuous probability" space.
Continuity is based on a topology, and there is not necessarily, and usually never is, any topology on the probability space in question. It is true that the Borel sets are the smallest sigma algebra that contain a given topology, usually the 'usual topology' of the real line or Euclidean n-space.
Where continuity enters is in a continuous density or an absolutely continuous cumulative distribution.
In practice what this means is that a 'density' is a continuous function f on, usually, the reals where f is non-negative and its integral is 1. Then the corresponding cumulative distribution F is the 'indefinite' integral of f, in TeX:
F(x) = \int_{-\infty}^x f(x) \; dx
It's crucial to discuss continuous densities in order to discuss random variables with Gaussian, uniform, exponential, chi-squared, etc. distributions. Gaussian is crucial for the central limit theorem. Uniform is crucial for discussing the usual random number generators, e.g., as in the recipes in Knuth's TACP that pass the Fourier test. The exponential distribution is crucial for discussing arrival processes, e.g., when the next Web page request will arrive at a Web server. Chi-square is one of the first distributions encountered in elementary statistical tests, e.g., for independence of two random variables. These distributions are commonly taught in first courses in statistics to students in the social sciences. Looks like the CMU computer science students are falling behind the sociology students!
More details on distributions involve the Lebesgue decomposition and absolute continuity, and you will find solid treatments in each of the four texts I listed along with both Rudin and Royden.
Loève also outlines the bizarre 'singular continuous' case, as I recall, based on the Cantor function.
"Your conclusion may be generally true, but it's definitely off the mark here."
I'm not "off the mark"; I'm dead on target. I gave some good, elementary definitions. Elementary should not mean sewage, but your elementary definition of a random variable is sewage.
"251 is far harder than any undergraduate math course at CMU."
Sounds like the CMU math department doesn't teach, say,
Walter Rudin, 'Principles of Mathematical Analysis, Third Edition', McGraw-Hill, New York.
or the equivalent. Tough to believe.
The material you are teaching is easy, plenty easy enough for a moderately easy course for freshman. Any difficulty is from the need for students to make sense out of sewage content such as your definition of random variable.
Again, for probability done seriously, see the texts I listed; for good elementary texts, there are many, but I have none at hand; often there are good, elementary treatments of random variables in the better texts on statistics for the social sciences; consider also texts on signals in EE; for probability at CMU, see Shreve.
For your course 251, do everyone a big favor and quit teaching it, burn it, and flush the ashes. Then start with some people who actually know the relevant math and develop a good course. And, really, from the list of topics, the course belongs in a math department. And, the course need not be difficult.
Bluntly, computer science very much needs to know math, especially probability, but generally gets a grade of D or less and on probability, an F.
I listed some world class experts in probability (I learned from one of them), but it is true that probability is not popular in US pure math departments, and a significant fraction of math professors will never have seen the more advanced definition of a random variable I have given here; they may not know the strong law of large numbers, conditional expectation, the definition of a Markov process or a martingale, etc.
You are badly wrong. Many people don't know anything about probability; that you don't is not so bad. But that you spout total nonsense about probability is bad; that you defend this nonsense is much worse. Keep fighting me on this and you will dig a seriously big hole for yourself and CS at CMU.
You want me to write Jerry and advise him that 251 needs to be cleaned up?
Right. But, it's tough to make good progress on tough problems without the most powerful tools!
E.g., for AI, it needs a lot of 'non-math' ideas before it gets to some math, if it ever does.
For more, let's start with source code. Suppose we have source code line
a = b*c
But, we conclude that
a = b*c
Of course if we saw
F = m*a
Okay, we are beginning to see:
A line of code such as
a = b*c
We could write
F = m*a
So, we have to ask,
F = m*a
F = m*a
Net, math, and science with math, are written in complete sentences in a natural language. The variables are all clearly defined, discussed, explained, etc. At no time is an algebraic expression of such variables regarded as a substitute for the natural language. Take a physics book, throwout the English and leave just the equations, and will have nothing.
Physics and math understand; so far computing does not.
So computing tries to write
force = mass*acceleration
The matter of using mnemonic variable names as parts of speech in English is a grand mistake but common in writing in computer science. Bummer.
Bluntly computing has not figured out that there is so far just one way to communicate meaning: Use complete sentences in a natural language. Period. That's all we've got. But computing has fooled itself into believing that algebraic expressions with mnemonic variable names form a 'new language' that, in computer source code, can provide the needed meaning without a natural language. Wrong.
For
F = m*a
So, writing these complete sentences to communicate meaning effectively is 'writing'.
Done!
Well, can do what Knuth did in TACP. There he did a lot with combinatorial formulas. To make much more progress, will have to get serious about math. E.g., the leading question in algorithms is just P versus NP, and that is now darned serious math. Don't attack that or even parts of it without a good background in math.
Other new and challenging questions in algorithms promise to need math for progress.
As I look at algorithms in 'advanced computer science', commonly they want to treat optimization. Tilt! Optimization is a huge field from applied math, operations research, and electrical engineering. There is deterministic and stochastic optimal control, Kalman filtering, integer linear programming, and much more. It's darned good applied math, and the math background I outlined is needed.
"numerical analysis"
That's a field of applied math. E.g., quickly get into advanced parts of matrix theory. E.g., consider R. Horn's books. E.g., quasi Newton quickly becomes an exercise in matrix norm theory. Long one of the more important tools in numerical analysis has been functional analysis. Likely the leading reason to pursue numerical analysis is just to get solutions to partial differential equations, and don't go there without a good background in math and likely the corresponding mathematical physics.
"compilers, computational reasoning, automated proofs"
For the last two, they are just fields of applied math.
"Data mining and AI"
The way computer science pursues these two, they are nonsense fields. The first should be just mathematical statistics, and for that the background I gave in probability is crucial. E.g., will want to know sufficient statistics, and that is based on the Radon-Nikodym theorem, and that is graduate pure math.
For AI, if someone can write a computer program that really has 'intelligence', fine. If all they use are intuitive ideas, good for them. But so far, the field of AI is very far from this goal in spite of decades of DARPA funding.
For now, if want a system that solves a problem well enough to look 'intelligent', then just engineer the system with the usual role for applied math. I gave a paper at an AAAI IAAI conference with the "25 best applications of AI in the world", and the best applications, really, were just good engineering.
"Systems, especially performance testing. Needs some statistics, but it's not hardcore math. No more than psychology or economics."
For some simple applications, yes, can just borrow statistics from the social sciences.
But for progress, it's back to "hardcore math": E.g., I published a paper on 'performance monitoring', that is, zero day anomaly detection in server farms and networks, and the math was based on some of the more advanced parts of the texts I listed. Basically I found a collection of multivariate, distribution free hypothesis tests. The social sciences have been using univariate distribution free hypothesis tests for over 60 years; my work was apparently the first good progress to multivariate distribution free tests. Multivariate tests are just crucial for analyzing performance data. I used a finite group (from abstract algebra) of measure preserving transformations something like in ergodic theory. My work was similar to some of what Diaconis at Stanford has done with exchangeability in distribution free statistics. This material needs all the background I outlined and more.
"Systems architecture"
For the future, before we build a large system, we will want the 'architecture' to have some known properties. We do this for bridges, buildings, dams, ships, airplanes, etc. So, we will want to do it for systems. We will want to know about reliability and security, at least. And we may want to 'optimize', that is, get what we need at minimum price.
E.g., consider part of the core of the Internet: Suppose we are given nodes and flows at the nodes. Then our mission, should we decide to accept it, is to connect the nodes at minimum cost to provide desired capacity, performance, and reliability.
Dean of Engineering at MIT T. Magnanti gave a Goldman lecture at Johns Hopkins on this problem; first-cut it's a super tough problem in integer linear programming. Actually, it was such problems in network design, and integer linear programming, that got Bell Labs going on the work that resulted in
Michael R. Garey and David S. Johnson, 'Computers and Intractability: A Guide to the Theory of NP-Completeness', ISBN 0-7167-1045-5, W. H. Freeman, San Francisco, 1979.
which is one of the key books early in the problem P versus NP.
It is common now to throw together a system, have various intuitive approaches to parallelism and redundancy, and then get a problem and see the whole server farm go south. Recall that this is just what happened at Amazon a few weeks ago.
Maybe some people, say, in national security, finance, or air traffic control would like such things not to happen?
A common situation is parallelism that is not more reliable but worse: E.g., there was a parallel transaction processing system. The load balancing sent the next transaction to the least busy computer. Then one day, one of the computers got a little sick, started throwing all its work into the bit bucket, was not very busy, was getting nearly all the transactions, and, thus, killed the whole 'cluster'. Bummer.
Something similar happened with Google's e-mail servers.
Instead, we need some theory with some theorems and proofs that provide some guarantees that we are getting the performance and reliability we need. That work will be mathematical; without a good background in math, don't try. And, yes, likely the work will need probability such as I discussed.
"Best practices. Software engineering. Not really mathy. Not really science either."
Not really computer science research either.
"OOP. Theology?"
There have been some connections with category theory, but I'm reluctant to take those seriously.
Mostly OOP in practice is simple and works well for some simple things. Otherwise OOP is not well thought out. And long OOP was a 'theology'.
My old view of programming languages is that they should be designed so that they 'admit' some source code transformations that have some useful properties. E.g., suppose we have such a language with such properties and have two pieces of code. Can we use the transformations to check if the two pieces of code are equivalent? So, right, we will have 'equivalence classes' of code. Some of these will do better on processor time, main memory usage, exploitation of multiple cores, etc. than others. So, with some code and these transformations, we have an optimization problem: Transform the code to equivalent code with the 'resource' properties we need. Sounds like math to me.
"But there are things to study that aren't just math, even in the field of computer science."
The problems are from computer science. But the good solutions are necessarily applied math because our civilization knows no other way to proceed. This situation is much that same as in all fields that have been 'mathematized', especially theoretical physics.
"15-251 Great Theoretical Ideas in Computer Science"
You mean there really are some? I always thought it was the empty set!
Okay, I followed the URL to see these wondrous ideas!
So, I saw some 'lecture notes' at:
http://server251.theory.cs.cmu.edu/twiki/bin/view/Main/Proba...
and there saw:
"Random Variables
We begin with the notion of a finite probability distribution D, which consists of a finite set S of elements, or samples, where each x in S has a weight, or probability, p(x) in [0,1]."
Sorry, guys. They blew it. That sentence is without a doubt the most mixed up, confused, uninformed, misinformed, just plain wrong mess I've ever seen in what purports to be some important mathematics. We're talking total upchuck here. Don't read that garbage.
(1)
"finite probability distribution"
Likely what he means is a discrete distribution.
(2)
"distribution D, which consists of a finite set S of elements, or samples"
Total nonsense. A "distribution" does NOT consist "of a finite set".
The rest is also nonsense.
He wants to discuss random variables but gets off on distributions far too soon.
Here is a much better way to proceed:
Suppose we perform an experiment and measure some number X. If we do the experiment again, then the number we get for X might be different. We call X a 'real random variable'.
For a real number x, we can consider the probability that X <= x. We write this probability as P(X <= x). We also write as the 'cumulative distribution' of X F_X(x) = P(X <= x). [Note: Here F_X borrows from Knuth's TeX notation for F with a subscript X.]
If X takes on only finitely many values, then we might say that X and its cumulative distribution F_X are 'discrete'.
Here is a still better way to proceed: We have a non-empty set S (usually denoted by capital omega) of 'trials'. Each experiment we perform is one 'trial' and corresponds to some point s in S (usually a trial is denoted by a lower case omega).
Given a subset A of S, we call A an 'event'. We have a probability P defined on events. The 'probability' of an event A is written P(A) and is a number in [0,1].
If in our experiment we observe a number, that number is a real random variable; call it X. Then X is a function from the set of trials S to the set of real numbers R. So, X: S --> R.
Then for a real number x, there is the event
{s | s is in S and X(s) <= x}
with shorthand notation {X <= x}. That is, we usually suppress mention of a trial s.
Then the probability that X <= x is written
F_X(x) = P(X <= x)
and is the 'cumulative distribution' of X.
For more details, we ask that the set of all events includes S and is closed under complements and countable unions. Usually the set of all events is denoted by script upper case F.
And we ask that for disjoints events A(i), i = 1, 2, ..., the probability of the union of the A(i) is the sum of P(A(i)). That is, we ask that P be 'countably additive'.
Suppose X is a real random variable with cumulative distribution F_X, and suppose for some positive integer n and i = 1, 2, ..., n Y(i) is a real random variable. Suppose the set
{Y(i) | i = 1, 2, ..., n}
is independent. And suppose for each i, the cumulative distribution of Y(i) is F_X. Then we can regard
{Y(i) | i = 1, 2, ..., n}
as a 'sample' of size n from cumulative distribution F_X.
Full details are in each of:
M. Loève, 'Probability Theory, I and II, 4th Edition', Springer-Verlag, New York.
Jacques Neveu, 'Mathematical Foundations of the Calculus of Probability', Holden-Day, San Francisco.
Leo Breiman, 'Probability', ISBN 0-89871-296-3, SIAM, Philadelphia.
Kai Lai Chung, 'A Course in Probability Theory, Second Edition', ISBN 0-12-174650-X, Academic Press, New York.
Loève was long at Berkeley, and Neveu and Breiman were among his students. Neveu has long been in Paris, and Breiman has long been at Berkeley. Chung has long been at Stanford. Other experts in such math include Avellaneda at Courant, Bertsekas at MIT, Çinlar at Princeton, Dynkin at Cornell, Karatzas at Columbia, Karr at UNC, Shiryaev at Moscow, Shreve at CMU, Wierman at Johns Hopkins, among others.
This disaster illustrates an important lesson: Computer science is out of gas, that is, doesn't know what to do next. It really has only one promising way out now, and that way is to 'mathematize' the field. So, the progress needs to be essentially applied math. For this progress, computer science needs to know some appropriate math. Basically each person trying to do such work needs a good undergraduate major in pure math together with some selected graduate work in pure and applied math. However, only a tiny fraction of professors of computer science have these prerequisites. Thus their work that needs math is often upchuck as in the example here although usually not quite this bad.
Net, anyone who wants to make progress in computer science should f'get about current academic computer science, study math, and then attack problems in computing as an applied mathematician. Bluntly, the alternative is just upchuck as here. Sorry 'bout that.
Any student trying to learn some topics in math in a computer science department is likely wasting time and money and filling in much needed gaps in his knowledge. To learn math, go to a math department. To learn probability, start with one of the books and/or professors above or equivalents.
"The Daily Deal I got offered today was for a restaurant 30 miles away: how does that make sense either for the customer or merchant?"
but still did not score the points:
The statement is a starkly clear illustration of a big, HUGE fact about GroupOn's business:
It's heavily just a LOCAL business.
So, even if they are in Chicago, New York City, San Francisco, and parts of Argentina and Australia doesn't cut much ice in Podunk. Instead, to make it in Podunk, they just have to be the best in Podunk, and the rest is irrelevant.
For the best 'coupons' in Podunk, customers and merchants in Podunk can go to a GroupOn competitor in Podunk who can be someone on the bank board and the school board, a former mayor, and known very well to all the merchants and most of the customers. This competitor in Podunk can be trusted in Podunk much more than GroupOn by both the merchants and the customers and, thus, get more deals and revenue, can have much lower overhead per dollar of revenue, and can undercut the GroupOn prices.
Such a local competitor has close to a 'geographic natural monopoly': In Podunk, the local guy can sign up several leading merchants just because they know him and trust him (and maybe invest with him); then he can get a lot of local customers signed up because he is well known and has the leading local merchants signed up. Then the rest of the merchants sign up not to get left behind. Then all the customers sign up because all the merchants did. Then all the merchants keep offering deals because all the customers are signed up. And there is no competition more than 60 miles away.
E.g., maybe the guy in Podunk also runs the popular local shopping center based on much the same mechanism of a 'geographic local monopoly'.
A Web site for the Podunk competitor? If that is a problem, then HN developers listen up: Develop a suitable general purpose Web site and lease it to the competitors in each of Podunk, Peoria, Poughkeepsie, Pleasantville, etc.
For the guy in Podunk, maybe there is another little town, Parsonville, 15 miles away: Okay, the guy in Podunk can use his success to expand to Parsonville.
More generally, if there are competitors in other surrounding towns, then he can, one town at a time, use his earnings from his natural monopolies in Podunk, Parsonville, etc. to undercut the prices in these other towns, buy out the competitors, raise prices, and repeat. Relevant terminology includes 'predatory marketing practices' and 'roll-up'.
I don't see how GroupOn can be successful for long with their current business model.
Is mostly about 1890 to about 1910 and then stops.
Really slow finally driving a stake through the heart of the quasi-mystical quaternions and killing them off.
No mention of the Gauss, Green, and Stokes theorems, exterior algebra, what is now called Grassman algebra, manifolds, differential geometry, orthogonal polynomials, Fourier theory, linear transformations, etc.
No mention of connections with systems of linear equations, linear transformations, or functional analysis such as Hilbert space.
There is serious question if the author really understands the subject. It looks like he got sucked down into quaternions and never really got out.