Remember the "and" part. Yes, we have abundant energy, but it's not cheap. My ability to get computations per dollar has increased many orders of magnitude in the last 30 years (or 60, but I'm not that old), to the level that my smartphone would have been the fastest computer in the world when I was born. Energy, on the other hand, is within an order of magnitude, the same cost: the real coal price is about the same as in 1800 (see: http://econbus.mines.edu/working-papers/wp201210.pdf and that's externalizing costs of climate change). That may have counted for cheap AND abundant then, but it certainly doesn't now.

Would you then say that England during WWI or WWII took the wrong action by taking those three steps? The situation is not very different -- the effected area is wealthy enough to pay for the resources under normal market conditions, there's a shortage of supply, and the infrastructure required to alleviate the situation isn't adequate to meet the demand.

Markets are not magic, they work extremely well in the appropriate domain -- but once you get into a supply or demand shock, core assumptions of market pricing go out the window, whether it be liquidity, or that the real rate of return on investing is greater than holding, respectively.

I think the idea that the GP had in mind is that a major problem in many disaster situations is that market liquidity (haha) breaks down, and so there's a huge incentive to hoard, and then use the relative paucity of suppliers to massively increase profit margins. Since illiquid markets are inefficient markets, the usual rules of supply and demand fail to hold -- the problem in the north east is that Sandy knocked out much of the oil importation infrastructure (as well as causing local refineries to shut down), which combined with fairly low inventory levels to pad the shock, means there isn't enough infrastructure for vendors to divert sufficient oil through at any price.

To mitigate this type of supply shock and restore liquidity, you need to ration (to reduce hoarding, and increase the cost of driving, thereby lowering demand), discourage gouging by introducing price controls (also mitigates hoarding.. but requires rationing) and temporarily subsidize supply both directly (ie. put it on national guard fuel trucks and sell it to the gas stations under cost), and indirectly (say by suspending regulations that might limit supply like the Jones Act, or low-sulfur fuel oil requirements.. to name two examples of what's been done). Letting the prices spike is only going to increase misery and give people incentives to do really stupid things, like the idiot who got caught with 120 gallons of fuel in home depot buckets (great, until his SUV explodes and closes a route for the real fuel trucks).

If we drop the hyperbole in terms of responses (and look at how diplomacy actually works), if China was to go all in supporting Iran (instead of nibbling around the edges and turning the blind-eye to smugglers) the US would respond by doing something along the lines declaring China a currency manipulator and putting a (small) tariff on their imports. Which would in turn, make the major export industries put a lot of pressure on the Chinese government to relent on the Iran policy (a small change in competitiveness in the US market is worth a lot more than the entire Iranian market).

But given that China's government is going through their once-a-decade power exchange as we speak, and they're already damaged by the Bo Xilai scandal, it's extremely unlikely that they'll escalate tensions with the US over Iran in the near future (tensions with Japan over the Senkaku/Diaoyu islands is a different story, however).

Remember, it's fractional reserve banking, not exponential reserve banking (which doesn't converge, and so doesn't exist). Banks are required to carry currency reserves. So if you deposit $1.00, they are free to lend out say, $0.88, if the reserve requirement is 12%, which can be deposited into another bank, etc, which leaves each dollar on deposit able to support ~$4 in circulation, in the above example.

I've found that there's a major exception to that: if you have to read anything that has typeset multi-column figures (like scientific papers), equations of any sort, or copious footnotes (ie. anything that's hard to dynamically rescale the text/layout) the smaller tablets just don't cut it. I'd love to be able to use one, since I find the ipad is just heavy enough to make it uncomfortable holding up when lying down to read for long periods, but until something comes along to deal with those problems (preferably a new format for publishing papers, that doesn't assume a constant page size), the ipad has the smallest useful form-factor.

Unless you ALREADY have a burning desire to do a thesis on a specific problem, you're not going to want to do a PhD in CS (especially if that's what you do now). When you're in mid-career, programs in the same field will want you to have a specific vision for what you want to do with your thesis. If you're pursuing a PhD because you're not super happy with the type of problem you're working on now, but don't have a clear plan for how to change direction, I'd recommend checking out CS-related fields, go to a few conferences (academics tend to be very friendly to this type of inquiry) and read a few papers, then pick an area that catches your interest. You need a compelling story in your application about why you're going back to school (I thought it would guarantee me a job at MSR doesn't cut it). If you get interested in a specific program, send them (either the program, or a specific professor whose work you're interested in) an email asking what to do about the letters of rec etc.. They'll let you know a) if you have a realistic shot at getting in and b) tell you exactly what they're going to want to see. This isn't undergrad admissions, there are few hard and fast rules about the application. If you get a professor who has funding interested in you, you're going to get a slot, period.

I'd check out Biomedical engineering, Computational neuroscience, Complex systems-type programs, Bioinformatics, Machine learning (may well be in the CS dept), and Robotics, to name a few. Programs like the above tend to have excellent private-sector placement rates, since a lot of people stay in academia and you come out with a slew of rare and useful skills.

Another thing to keep in mind, is that a PhD these days is a 5-8 year commitment (unless you already have you're thesis research mapped out, see above, then 4). While you won't be paying (in any reputable program), you'll only be making ~25k a year stipend. It's not something you should embark on without doing your homework about programs, and finding something exciting enough to sustain you through years of slow, slow progress. There were three mid-career people at my PhD program, and they were fun to have around.. but one thing that stuck out about all of them, is they came in having a research project they were already obsessed with, and they hit the ground running, where all the out-of-college types like myself spent a year or two testing the waters to figure out what we wanted to do.

Of course math changes the way you think, and often to the good. The real question, left unaddressed in the original article, is when and how do we start teaching math?

There is a body of experimental evidence, mostly from upstate NY in the 20s and 30s (see [PDF] here) that the main problem in early education is that math, with its many abstractions of notation and convention, is brought in far too early. Instead, rigorous verbal and written exercises could cover the necessary conceptual bases for math to be added onto later, while not losing huge amounts of time creating arti-factual stories to get 7-year-olds to learn division, which may then interfere with their later understanding of the actual basis.

Another method that's been suggested, also with a body of experimental evidence (see for an overview), takes the opposite tack, and says okay, we can teach everything the first time in a way consistent with later fundamentals, but to do so, we have to recognize that many apparently simple steps are actually 5-7 'micro-steps' and we need to break out and teach these explicitly.

Given that much more rigorous levels of math education don't seem to cause mass dropouts or lack of bachelors attainment in many other countries, I think the emphasis should be on fixing the way we teach math, rather than further devaluing (and yes, the ability to jump through hoops is important for successful employment.. and also, this guy thinks he can do rigorous statistical inference without a rock solid understanding of modern algebra?) high school and college degrees.

The only reason the bank protected peoples' money for many years in the first place, and why Switzerland draws so many international deposits, is because they have a long record of effective government, an independent legal system and bank controls. Moreover, given that most modern governments guarantee deposits up to a certain level (100k CHF in this case), much of the depository risk is borne by the government and ultimately the tax payer, not the bank. And the bank has already made its (legitimate) profit by having access to the principal to lend against for many years. But hey, when you can ignore those inconvenient facts to privatize profits while socializing the risk, you gotta do it, right?

Having read "The Smartest Guys in the Room" about the Enron debacle, I think a large part of the Enron collapse story was essentially a fallout of stack ranking -- a form of which they employed -- the remarkable thing, though, was that Skilling in particular and the other top managers were such libertarian wackos that they thought fostering internal competition within teams and between business divisions was a good thing. And then they combined this with a system where bonuses were paid based on deal sizes based on mark-to-market accounting, so the originator got a bonus and there was virtually no monetary incentive to actually service the back-end of the contract (and thus actually get paid and maintain a revenue stream.. but that's a different story).

Some of the behaviors that came out of that culture are hard to believe..
-The trading desks in different divisions taking opposite sides of the same position with leverage, guaranteeing the company loss (but not the division that won the trade)
-New MBAs were hired all the time, but not initially assigned to a team. They essentially had to shop themselves around, and were more or less allowed to transfer between teams at any time early in the process. Near the end this led, essentially, to something like 20% of the Enron workforce being shuffled around from team to team near evaluation time to take the 'bad slots' in the evaluation.
-They set up an entire subsidiary, Enron Energy Services, that would only be financially viable in the long term if deregulation in California succeeded, and became a national model... while having a division who's entire performance/bonus criteria was dependent on how badly they could exploit loopholes in the deregulation to make a short term profit from trading.

I think the bottom line is that if you let this type of Machiavellian culture take hold, it essentially means that you don't have a functional leader -- they've abrogated there responsibilities to the political abilities of their subordinates. If you look at Northern Italy and the northern part Holy Roman Empire, the former had no leader, de facto or de jure, while the latter had one de jure. Neither of those areas managed to centralize authority until 1860 and 1870 respectively, about 200 years after England, France and Austria managed to do so from more or less the same feudal starting point (they had leaders that were willing to intervene decisively in baronial disputes).

I think you're misunderstanding the GP's use of "production heavy" -- the GP meant that the south produced a lot more than it consumed (due to slavery artificially depressing southern wages), and so had to export to sustain it's economy. Not only that, but due to the vast preponderance of the production being labor intensive and inefficient agriculture (skilled slaves tended to 'smuggle' themselves north), the south had to import large quantities of capital goods. So they were in favor of a low tariff, and cheaper imports from France and England, both of which had lower wages than northern industries due to overpopulation.

The north, because of free waged labor, had local demand to sustain their production, and was in favor of a high tariff to support nascent industries from undercutting by European competition (the enduring solution to this problem was the mass migrations from Europe to the US after the Civil War).

Which is why, as Ha-Joon Chang points out, immigration and trade policy are two sides of the same coin: free trade with free immigration mean no tariff (regardless of the legal rate), while free trade with restricted immigration is a tariff on exports (the current US situation), free immigration with restricted trade is a tariff on imports, and restricted trade with restricted immigration is mercantilism (which is unsustainable in the long term, since even accompanied with capital controls, makes smuggling extremely lucrative).

To get back to what happens as machines become cheaper than people at ever more jobs, there are three possibilities: 1) It turns out that there are some things that machines will never do better than people at the prevailing wage (which must remain significantly above zero), and employment moves exclusively to those sectors. 2) We re-evaluate our societal compact that it's generally expected for people to engage in labor to earn money, or 3) The owners of capital resources (.001%) are unwilling to consider (2) while the prevailing wage crashes towards zero, and we get a humongous war/revolution.

I'd add that just because you're convinced you'll be doing computer science coming into college doesn't mean that's what you'll be doing coming out of college. When I was a senior in high school, I too was totally convinced I wanted to major in computer science. I wound up majoring in math and history (I know, strange double major) and now I'm in neuroscience (which involves much of my day sitting in front of a computer, programming).

Plans often change during college, so don't sweat about trying to optimize your resume for a career you may never pursue.

I think you're misunderstanding the primary complaint about the venture funding bias:
1) Stanford admissions selections, while probabilistic, are dominated by socioeconomic status (this also highly correlates with several often used measures of 'smarts', like the SAT).
2) Stanford students and graduates have privileged access to venture capital funding for their start-ups.
3) This gives incentive for a certain type of highly achieving student to apply to Stanford -- those interested in receiving VC money.
4) That incentive compromises Stanford's ability as a top-tier research institution to attract students who are interested in basic research in proportion to those interested in immediately applicable research topics.
5) Without the broad basic research base, the quality of Stanford alums starts declining because their applied ideas don't use the best current science.

I don't think, even if this cycle perpetuates that it spells death to Stanford or anything, but it sure is non-optimal in terms of technological development, and it will surely also cause a dip in the quality of Stanford's research output, which has generally been extremely high in the past 40 or so years... and given the amount of GDP the Stanford has access to and their research record in the past 40 years, that's bad news not only for the US tax payer but humanity as a whole.

It turns out that humans are really poor at estimating velocity unless they conform to Newtonian accelerations very closely.. While there has been a lot of research on these issues, I'd like to refer to one of my favorite papers, Sverker Runeson's 1975 paper "Constant velocity — Not perceived as such".

http://www.springerlink.com/content/nt61hh074k7123q5/

I'd first suggest to try reading some John Holland, like Hidden Order for example, which will elucidate the errors of argument better than my brief response.

Suppose we think about evolution and natural selection as a 'fitness landscape' (it's many dimensional, but let's call it two for the moment). Going vertical optimizes for a specific environment, going lateral means being viable in a range of environments. Evolution is in this setting inevitable, transcription errors are made etc and mutations happen. The theory of natural selection claims that those mutations that are beneficial to the organism prosper, whether that movement is an optimization within their own local fitness area or one that allows them to attempt to migrate into areas that were previously not viable.

One primary problem with Behe and Dembski's theories, is that they make contradictory assumptions about the fitness landscape. Behe argues, with irreducible complexity, that the comparatively low entropic (local minima) state of the mammalian eye is evidence of intelligent design since high entropy, intermediate, eyes wouldn't be sustainable (turns out all the intermediate versions exist). Dembski on the other hand seems to argue the position that increasing entropy is demanding, and the growth in complexity is important in the context of the fitness landscape. So those theories are contradictory.

But where they both run into a global (think variable, not earth) problem (human knowledge passing is Lamarckian, not Mendelian on the whole) is that neither consider the lateral aspects of the landscape, and think that everything is either climbing or descending, mostly in opposite directions!